Final_Version_Redda_Project_90__Accuracy_V2.ipynb

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        "<a href=\"https://colab.research.google.com/github/IsinghGitHub/CellStrat/blob/master/Final_Version_Redda_Project_90__Accuracy_V2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KYTcY3Gecqq"
      },
      "source": [
        "# Regression on HR Performance Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v1uiOgjeB3W"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6ndBCWGrLH4"
      },
      "source": [
        "# 1 | Library Import and Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 267,
      "metadata": {
        "id": "8c6xNSsNd5Xa"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from sklearn.model_selection import KFold, GridSearchCV, StratifiedKFold\n",
        "from datetime import datetime\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.linear_model import LinearRegression,LassoCV, Ridge, LassoLarsCV,ElasticNetCV\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve\n",
        "from sklearn.ensemble import AdaBoostRegressor, ExtraTreesRegressor\n",
        "from sklearn.preprocessing import StandardScaler, Normalizer, RobustScaler\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ArfVOe-gThF"
      },
      "source": [
        "## Data Cleaning & Preparation"
      ]
    },
    {
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        "outputId": "84aea5df-d23e-4b68-9d52-2025216f148e"
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              "       ID COMPANY ID                  JOB Department  MBTI Predictor 1  \\\n",
              "0  AIT160        AIT           Accountant      Sales  ENTJ         64%   \n",
              "1  ABS045        ABS           Consultant      Sales  ESTJ         63%   \n",
              "2  ORA262        ORA          IT Engineer         IT  ESTJ         72%   \n",
              "3  ORA335        ORA  Marketing Assistant         IT  ISTJ         76%   \n",
              "4  ORA290        ORA  Marketing Assistant  Marketing  ENTP         65%   \n",
              "\n",
              "  Predictor 2 Predictor 3 Predictor 4 CTAR  \\\n",
              "0         33%         33%         42%   0%   \n",
              "1         70%         25%         33%   8%   \n",
              "2         60%         25%         33%   8%   \n",
              "3        -17%        -17%        -33%  -8%   \n",
              "4         17%          0%          8%   8%   \n",
              "\n",
              "  Aggregate total using predictors weights of study 2  \\\n",
              "0                                              #REF!    \n",
              "1                                              #REF!    \n",
              "2                                              #REF!    \n",
              "3                                              #REF!    \n",
              "4                                              #REF!    \n",
              "\n",
              "  Decision based on Study 2 Individual Performance FY22 Gender  Age  Tenure  \n",
              "0                     Hired                         74%      F   34       3  \n",
              "1                     Hired                         70%      M   42      16  \n",
              "2                     Hired                         78%      M   33       0  \n",
              "3                 Not Hired                         40%      M   23       0  \n",
              "4                 Not Hired                         60%      F   19       0  "
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              "      <td>IT</td>\n",
              "      <td>ISTJ</td>\n",
              "      <td>76%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-33%</td>\n",
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              "      <td>#REF!</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>40%</td>\n",
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              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ORA290</td>\n",
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              "      <td>Marketing Assistant</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>ENTP</td>\n",
              "      <td>65%</td>\n",
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          },
          "metadata": {},
          "execution_count": 268
        }
      ],
      "source": [
        "\"\"\" Load in the data and examine the head. \"\"\"\n",
        "## THIS IS SYNTHETIC DATA\n",
        "\n",
        "data = '/content/drive/MyDrive/Redda/tabular-lstm-64ec7914791a2a99c328efa3-data_preview_LSTM.csv'\n",
        "df = pd.read_csv(data)\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Load in the data and examine the head. \"\"\"\n",
        "## THIS IS ORIGINAL DATA\n",
        "\n",
        "data = '/content/drive/MyDrive/Redda/AI Model Data v140823 CellStrat_V1.xlsx'\n",
        "df = pd.read_excel(data)\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "EXdzfzftm1oE",
        "outputId": "ca153486-34b7-44e3-a66b-e3c59ae78173"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID COMPANY ID                      JOB Department  MBTI  Predictor 1  \\\n",
              "0  ABS025        ABS                    Sales      Sales  ISTJ        0.510   \n",
              "1  ABS026        ABS               Consultant      Sales  ESTJ        0.630   \n",
              "2  ABS027        ABS  Chief Financial Officer    Finance  ESTP        0.785   \n",
              "3  ABS028        ABS          Project Manager      Sales  ISTJ        0.650   \n",
              "4  ABS029        ABS               Consultant      Sales  ISTJ        0.755   \n",
              "\n",
              "   Predictor 2  Predictor 3  Predictor 4  CTAR  \\\n",
              "0         1.00         0.08         0.33  0.00   \n",
              "1         0.70         0.25         0.33  0.08   \n",
              "2         0.57         0.17         0.08 -0.17   \n",
              "3         0.43         0.17         0.42 -0.17   \n",
              "4         0.30         0.08         0.42 -0.08   \n",
              "\n",
              "   Aggregate total using predictors weights of study 2  \\\n",
              "0                                           0.662237     \n",
              "1                                           0.550900     \n",
              "2                                           0.448472     \n",
              "3                                           0.413302     \n",
              "4                                           0.343929     \n",
              "\n",
              "  Decision based on Study 2  Individual Performance FY22 Gender   Age  Tenure  \\\n",
              "0                     Hired                          0.7      F  35.0    12.0   \n",
              "1                     Hired                          0.7      M  42.0    16.0   \n",
              "2                     Hired                          0.7      M  40.0    12.0   \n",
              "3                     Hired                          0.7      F  42.0    15.0   \n",
              "4                 Not Hired                          0.7      M  37.0    10.0   \n",
              "\n",
              "                         Degree  \n",
              "0             Engineer in Optic  \n",
              "1      Telecommucation Engineer  \n",
              "2        Bachelor in accounting  \n",
              "3  Engineer in computer science  \n",
              "4  Engineer in computer science  "
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              "      <td>ABS025</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.785</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.17</td>\n",
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              "      <td>0.448472</td>\n",
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              "      <td>40.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Bachelor in accounting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABS028</td>\n",
              "      <td>ABS</td>\n",
              "      <td>Project Manager</td>\n",
              "      <td>Sales</td>\n",
              "      <td>ISTJ</td>\n",
              "      <td>0.650</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.42</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.413302</td>\n",
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              "      <td>15.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ABS029</td>\n",
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              "      <td>37.0</td>\n",
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              "      <td>Engineer in computer science</td>\n",
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              "\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Load in the data and examine the head. \"\"\"\n",
        "## THIS IS COMBINED  DATA\n",
        "\n",
        "data = '/content/drive/MyDrive/Redda/Combine_REDDA_dataset_V1.csv'\n",
        "df = pd.read_csv(data)\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "nwT8zCmxo_-w",
        "outputId": "b744bf26-ee4b-4c9d-f3f6-a79e65c0d9f4"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID COMPANY ID                  JOB Department  MBTI Predictor 1  \\\n",
              "0  AIT160        AIT           Accountant      Sales  ENTJ         64%   \n",
              "1  ABS045        ABS           Consultant      Sales  ESTJ         63%   \n",
              "2  ORA262        ORA          IT Engineer         IT  ESTJ         72%   \n",
              "3  ORA335        ORA  Marketing Assistant         IT  ISTJ         76%   \n",
              "4  ORA290        ORA  Marketing Assistant  Marketing  ENTP         65%   \n",
              "\n",
              "  Predictor 2 Predictor 3 Predictor 4 CTAR  \\\n",
              "0         33%         33%         42%   0%   \n",
              "1         70%         25%         33%   8%   \n",
              "2         60%         25%         33%   8%   \n",
              "3        -17%        -17%        -33%  -8%   \n",
              "4         17%          0%          8%   8%   \n",
              "\n",
              "  Aggregate total using predictors weights of study 2  \\\n",
              "0                                              #REF!    \n",
              "1                                              #REF!    \n",
              "2                                              #REF!    \n",
              "3                                              #REF!    \n",
              "4                                              #REF!    \n",
              "\n",
              "  Decision based on Study 2 Individual Performance FY22 Gender   Age  Tenure  \n",
              "0                     Hired                         74%      F  34.0       3  \n",
              "1                     Hired                         70%      M  42.0      16  \n",
              "2                     Hired                         78%      M  33.0       0  \n",
              "3                 Not Hired                         40%      M  23.0       0  \n",
              "4                 Not Hired                         60%      F  19.0       0  "
            ],
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              "      <th>0</th>\n",
              "      <td>AIT160</td>\n",
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              "      <td>Hired</td>\n",
              "      <td>74%</td>\n",
              "      <td>F</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABS045</td>\n",
              "      <td>ABS</td>\n",
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              "      <td>Sales</td>\n",
              "      <td>ESTJ</td>\n",
              "      <td>63%</td>\n",
              "      <td>70%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Hired</td>\n",
              "      <td>70%</td>\n",
              "      <td>M</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ORA262</td>\n",
              "      <td>ORA</td>\n",
              "      <td>IT Engineer</td>\n",
              "      <td>IT</td>\n",
              "      <td>ESTJ</td>\n",
              "      <td>72%</td>\n",
              "      <td>60%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Hired</td>\n",
              "      <td>78%</td>\n",
              "      <td>M</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ORA335</td>\n",
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              "      <td>IT</td>\n",
              "      <td>ISTJ</td>\n",
              "      <td>76%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>40%</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ORA290</td>\n",
              "      <td>ORA</td>\n",
              "      <td>Marketing Assistant</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>ENTP</td>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8%</td>\n",
              "      <td>8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>60%</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5fcdb8a-b1fd-4d48-a2a5-f85d4121ea32')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f5fcdb8a-b1fd-4d48-a2a5-f85d4121ea32 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f5fcdb8a-b1fd-4d48-a2a5-f85d4121ea32');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b581f95b-e965-4337-87ca-ec6949254adf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b581f95b-e965-4337-87ca-ec6949254adf')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b581f95b-e965-4337-87ca-ec6949254adf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CqFCoz2rGUm"
      },
      "source": [
        "# 2 | Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGRvLHSogXo3",
        "outputId": "6f89ccaf-eb87-43f3-81ec-4ceeb3f1d8ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'COMPANY ID', 'JOB', 'Department', 'MBTI', 'Predictor 1',\n",
              "       'Predictor 2', 'Predictor 3', 'Predictor 4', 'CTAR',\n",
              "       'Aggregate total using predictors weights of study 2',\n",
              "       'Decision based on Study 2', 'Individual Performance FY22', 'Gender',\n",
              "       'Age', 'Tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ],
      "source": [
        "df.columns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDZmTvOgrjKr"
      },
      "source": [
        " ### 2.1 Rename columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "metadata": {
        "id": "loWHnEzcr3Fa"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Rename columns\n",
        "new_column_names = {\n",
        "    'Decision based on Study 2': 'Decisionstd2',\n",
        "    'Individual Performance FY22': 'IndividualPerf',\n",
        "    'Aggregate total using predictors weights of study 2': 'AggregateTotPred',\n",
        "    'Predictor 1':'Predictor1',\n",
        "    'Predictor 2':'Predictor2',\n",
        "    'Predictor 3':'Predictor3',\n",
        "    'Predictor 4':'Predictor4'\n",
        "    # Add more mappings as needed\n",
        "}\n",
        "\n",
        "df.rename(columns=new_column_names, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {
        "id": "lWz6AYE941i5"
      },
      "outputs": [],
      "source": [
        "\"\"\" Iterate through the dataframes columns and covert to lowercase for QoL. \"\"\"\n",
        "low_col = []\n",
        "for i in df.columns:\n",
        "    i = i.lower()\n",
        "    low_col.append(i)\n",
        "df.columns = low_col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPSf2ZbO_IYB",
        "outputId": "5d27402a-e362-4848-c747-67c4e1915866"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                  0\n",
            "company id          0\n",
            "job                 0\n",
            "department          1\n",
            "mbti                8\n",
            "predictor1          0\n",
            "predictor2          0\n",
            "predictor3          0\n",
            "predictor4          0\n",
            "ctar                0\n",
            "aggregatetotpred    0\n",
            "decisionstd2        0\n",
            "individualperf      7\n",
            "gender              0\n",
            "age                 1\n",
            "tenure              0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Count null values in each column\n",
        "null_counts = df.isnull().sum()\n",
        "\n",
        "print(null_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02RG8K7JBmI3",
        "outputId": "78f29155-3e77-4724-cac5-2f76947886c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null Percentages in Each Column:\n",
            "id                  0.000000\n",
            "company id          0.000000\n",
            "job                 0.000000\n",
            "department          0.029886\n",
            "mbti                0.239091\n",
            "predictor1          0.000000\n",
            "predictor2          0.000000\n",
            "predictor3          0.000000\n",
            "predictor4          0.000000\n",
            "ctar                0.000000\n",
            "aggregatetotpred    0.000000\n",
            "decisionstd2        0.000000\n",
            "individualperf      0.209205\n",
            "gender              0.000000\n",
            "age                 0.029886\n",
            "tenure              0.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Calculate null percentages for each column\n",
        "null_percentages = (df.isnull().sum() / len(df)) * 100\n",
        "\n",
        "# Print null percentages\n",
        "print(\"Null Percentages in Each Column:\")\n",
        "print(null_percentages)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKXNq2Y_hBuf",
        "outputId": "fe5b6dd8-0d08-4b14-e7c6-98b7d253e2d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3346, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ],
      "source": [
        "df.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpgDpWDKhEC6",
        "outputId": "fae6d075-f6a7-4090-fe86-84a46064ba14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3346 entries, 0 to 3345\n",
            "Data columns (total 16 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   id                3346 non-null   object \n",
            " 1   company id        3346 non-null   object \n",
            " 2   job               3346 non-null   object \n",
            " 3   department        3345 non-null   object \n",
            " 4   mbti              3338 non-null   object \n",
            " 5   predictor1        3346 non-null   object \n",
            " 6   predictor2        3346 non-null   object \n",
            " 7   predictor3        3346 non-null   object \n",
            " 8   predictor4        3346 non-null   object \n",
            " 9   ctar              3346 non-null   object \n",
            " 10  aggregatetotpred  3346 non-null   object \n",
            " 11  decisionstd2      3346 non-null   object \n",
            " 12  individualperf    3339 non-null   object \n",
            " 13  gender            3346 non-null   object \n",
            " 14  age               3345 non-null   float64\n",
            " 15  tenure            3346 non-null   int64  \n",
            "dtypes: float64(1), int64(1), object(14)\n",
            "memory usage: 418.4+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAwepsY4ipbU",
        "outputId": "bcfec27b-0e42-4da5-9140-040131e14732"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IT                                1072\n",
              "Sales                              928\n",
              "Finance                            477\n",
              "Supply Chain                       373\n",
              "Marketing                          106\n",
              "Technical                           82\n",
              "After Sales                         71\n",
              "Payroll                             59\n",
              "General Management                  57\n",
              "Sales/IT                            40\n",
              "Sales/Marketing                     33\n",
              "Sales SPECIALIST CYBERSECURITE      29\n",
              "Sales/Finance                        8\n",
              "Human Ressources                     6\n",
              "Factory                              3\n",
              "Sales Director                       1\n",
              "Name: department, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ],
      "source": [
        "\"\"\" Investigate categorical columns that seem irrelevent. \"\"\"\n",
        "df.department.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RgZyIgEmxxk",
        "outputId": "7dec1fc6-1d08-411f-f7ef-8ff384732cbc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ISTJ    1121\n",
              "ESTJ    1096\n",
              "ENTJ     307\n",
              "INTJ     247\n",
              "ESFJ     186\n",
              "ESTP      76\n",
              "ENFJ      58\n",
              "ISFJ      56\n",
              "ENTP      48\n",
              "ISTP      42\n",
              "ESFP      35\n",
              "INFJ      26\n",
              "ENFP      12\n",
              "INTP      12\n",
              "INFP       9\n",
              "ISFP       7\n",
              "Name: mbti, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 279
        }
      ],
      "source": [
        "\"\"\" Investigate categorical columns that seem irrelevent. \"\"\"\n",
        "\n",
        "df.mbti.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_RD2eE2sQY6"
      },
      "source": [
        "## 2.2 Dealing with duplicated values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTSiYEXgsR0G",
        "outputId": "3567b863-040b-4807-b3ae-504a68edf655"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicated values in training dataset:  1651\n"
          ]
        }
      ],
      "source": [
        "print('Number of duplicated values in training dataset: ', df.duplicated().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 281,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o32UvDPIsZL8",
        "outputId": "93c5785a-6714-4e16-f081-05a48a8a8740"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicated values dropped succesfully\n",
            "****************************************************************************************************\n"
          ]
        }
      ],
      "source": [
        "df.drop_duplicates(inplace=True)\n",
        "print(\"Duplicated values dropped succesfully\")\n",
        "print(\"*\" * 100)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uD-Lv2xAlEOY"
      },
      "source": [
        "## Creating numerical and categorical lists"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 282,
      "metadata": {
        "id": "d_tkSq_tlDKa"
      },
      "outputs": [],
      "source": [
        "# distinction is based on the number of different values in the column\n",
        "columns = list(df.columns)\n",
        "\n",
        "categoric_columns = []\n",
        "numeric_columns = []\n",
        "\n",
        "for i in columns:\n",
        "    if len(df[i].unique()) > 6:\n",
        "        numeric_columns.append(i)\n",
        "    else:\n",
        "        categoric_columns.append(i)\n",
        "\n",
        "categoric_columns = categoric_columns[:-1] # Excluding target:'Churn'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 283,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvQhKg2qq0wS",
        "outputId": "a9df4e45-b170-4dd7-e31e-ead8dddea4ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical fetures:  ['id', 'job', 'department', 'mbti', 'predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar', 'aggregatetotpred', 'individualperf', 'age', 'tenure']\n",
            "Categorical fetures:  ['company id', 'decisionstd2']\n"
          ]
        }
      ],
      "source": [
        "print('Numerical fetures: ',numeric_columns)\n",
        "print('Categorical fetures: ',categoric_columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6L_x3XqTmsMy"
      },
      "source": [
        "### We will be dropping the above columns along with some not required columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 284,
      "metadata": {
        "id": "qb7AR59LmlUd"
      },
      "outputs": [],
      "source": [
        "columns_to_drop = [\"job\",\"company id\",\"department\"]\n",
        "df = df.drop(columns=columns_to_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A92s79nvnSBq",
        "outputId": "6599722f-27e9-4c20-a642-3fc00744d394"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id  mbti predictor1 predictor2 predictor3 predictor4 ctar  \\\n",
              "0  AIT160  ENTJ        64%        33%        33%        42%   0%   \n",
              "1  ABS045  ESTJ        63%        70%        25%        33%   8%   \n",
              "2  ORA262  ESTJ        72%        60%        25%        33%   8%   \n",
              "3  ORA335  ISTJ        76%       -17%       -17%       -33%  -8%   \n",
              "4  ORA290  ENTP        65%        17%         0%         8%   8%   \n",
              "\n",
              "  aggregatetotpred decisionstd2 individualperf gender   age  tenure  \n",
              "0            #REF!        Hired            74%      F  34.0       3  \n",
              "1            #REF!        Hired            70%      M  42.0      16  \n",
              "2            #REF!        Hired            78%      M  33.0       0  \n",
              "3            #REF!    Not Hired            40%      M  23.0       0  \n",
              "4            #REF!    Not Hired            60%      F  19.0       0  "
            ],
            "text/html": [
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              "    <div>\n",
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              "      <th>id</th>\n",
              "      <th>mbti</th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>aggregatetotpred</th>\n",
              "      <th>decisionstd2</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>gender</th>\n",
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              "      <th>tenure</th>\n",
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              "      <th>0</th>\n",
              "      <td>AIT160</td>\n",
              "      <td>ENTJ</td>\n",
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              "      <td>0%</td>\n",
              "      <td>#REF!</td>\n",
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              "      <td>ABS045</td>\n",
              "      <td>ESTJ</td>\n",
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              "      <td>25%</td>\n",
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              "      <td>#REF!</td>\n",
              "      <td>Hired</td>\n",
              "      <td>70%</td>\n",
              "      <td>M</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ORA262</td>\n",
              "      <td>ESTJ</td>\n",
              "      <td>72%</td>\n",
              "      <td>60%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Hired</td>\n",
              "      <td>78%</td>\n",
              "      <td>M</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ORA335</td>\n",
              "      <td>ISTJ</td>\n",
              "      <td>76%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>40%</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ORA290</td>\n",
              "      <td>ENTP</td>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8%</td>\n",
              "      <td>8%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>60%</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
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              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-0d239d96-6ce2-4cd4-98af-18c4d051d734 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0d239d96-6ce2-4cd4-98af-18c4d051d734');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef9b644c-c0e6-4fa2-b792-0d7821571103 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 286,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "SiH_Z6TDn0r2",
        "outputId": "8ca41554-447e-4698-f995-32463612e54b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Apply Pandas get_dummies function to the dataframe to transform the categorical variables to numeric.\\n    This will create additional columns in your dataframe.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 286
        }
      ],
      "source": [
        "\"\"\" Apply Pandas get_dummies function to the dataframe to transform the categorical variables to numeric.\n",
        "    This will create additional columns in your dataframe.\"\"\"\n",
        "\n",
        "#df_dum = pd.get_dummies(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "metadata": {
        "id": "s4IExS-4n2sz"
      },
      "outputs": [],
      "source": [
        "#df_dum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {
        "id": "Pbi2tzzWobBR"
      },
      "outputs": [],
      "source": [
        "#df_dum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4Qr5U8GckrZ"
      },
      "source": [
        "## Correlation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_e_l6UMd2C9"
      },
      "source": [
        "Find out which attribute correlates with individualperf the most.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUWcW0HEeHIr"
      },
      "source": [
        "### What are the types of the variables?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LZZz3HgeI6R"
      },
      "source": [
        "Basically we have two types of variables if we are only looking into the variable itself. We have continuous variable and categorical variable. While continuous variables can be further categorize as either interval or ratio variables, categorical variables can be further categorize as either nominal, ordinal or dichotomous.\n",
        "\n",
        "'decisionstd2' is a dichotomous variable as it has two categories, 'Hired' and 'NotHired'.\n",
        "\n",
        "'MBTI' is a nominal variable as it has more than two categories. ISTJ, 'ESTP', 'ESTJ.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mHwXhWffUDg"
      },
      "source": [
        "The number in each box represents the Pearson Correlation Coefficient between the two variables.\n",
        "\n",
        "The math behind this: It's the covariance of the two variables divided by the standard deviation of the two variables. In this method, the correlation ranges from -1 to 1, which allows us to tell how strong the relationship it is between the two variables as the bigger the absolute it is the more correlated they are, while we can't tell it from covariance. That's why the first box is 1 as 'score' is 100% correlated with 'score' itself.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Q-tofyqfViZ"
      },
      "source": [
        "Back to our questions, regarding the correlation coefficient, we can see that 'predictor2','predictor3' and 'predictor4' have the biggest positive number, 0.53,0.37, and 0.36, while 'age' is the most negative one, -0.072.\n",
        "\n",
        "We can easily interpret it as the following: The bigger number of 'predictor' the bigger  the  'individual_performance' we find and vice versa, 'predictor' is having the most positive relationship with 'individual_performance'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSSLfJrikHcf"
      },
      "source": [
        "## Droping unnessesary columns based on the corr results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 289,
      "metadata": {
        "id": "ojA7nXqSkT7f"
      },
      "outputs": [],
      "source": [
        "columns = ['id', 'company id', 'job', 'department', 'mbti', 'predictor1',\n",
        "       'predictor2', 'predictor3', 'predictor4', 'ctar', 'aggregatetotpred',\n",
        "       'decisionstd2', 'individualperf', 'gender', 'age', 'tenure', 'degree']\n",
        "columns_to_drop = ['id','mbti','aggregatetotpred','decisionstd2']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQOzasBu5uMj",
        "outputId": "196ae681-f4ca-4d74-e92c-eea81e35da3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'mbti', 'predictor1', 'predictor2', 'predictor3', 'predictor4',\n",
              "       'ctar', 'aggregatetotpred', 'decisionstd2', 'individualperf', 'gender',\n",
              "       'age', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 291,
      "metadata": {
        "id": "Ugdblph1kS9D"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns=columns_to_drop)\n",
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 292,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvpEsNnIqbz8",
        "outputId": "cb2c7d97-beb9-4f0e-ca5a-ba1b6d812a0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'individualperf', 'gender', 'age', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 293,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DkmWdnt8ZkEV",
        "outputId": "21bc3497-1aea-482c-f237-f082cd03f930"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     predictor1 predictor2 predictor3 predictor4  ctar individualperf gender  \\\n",
              "0           64%        33%        33%        42%    0%            74%      F   \n",
              "1           63%        70%        25%        33%    8%            70%      M   \n",
              "2           72%        60%        25%        33%    8%            78%      M   \n",
              "3           76%       -17%       -17%       -33%   -8%            40%      M   \n",
              "4           65%        17%         0%         8%    8%            60%      F   \n",
              "...         ...        ...        ...        ...   ...            ...    ...   \n",
              "3341        50%       -13%        -8%       -17%   50%            50%      F   \n",
              "3342        63%       -30%       -17%        -8%   17%            40%      F   \n",
              "3343        35%        -3%       -17%       -17%    0%            40%      M   \n",
              "3344        52%       -20%       -42%       -25%  -58%            40%      F   \n",
              "3345        25%       -17%       -17%       -17%  -25%            40%      F   \n",
              "\n",
              "       age  tenure  \n",
              "0     34.0       3  \n",
              "1     42.0      16  \n",
              "2     33.0       0  \n",
              "3     23.0       0  \n",
              "4     19.0       0  \n",
              "...    ...     ...  \n",
              "3341  30.0       3  \n",
              "3342  37.0      10  \n",
              "3343  31.0      13  \n",
              "3344  27.0       2  \n",
              "3345  29.0       7  \n",
              "\n",
              "[1690 rows x 9 columns]"
            ],
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              "  <div id=\"df-cfb50e5d-aa86-4012-876c-ad3bde9b09fa\" class=\"colab-df-container\">\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64%</td>\n",
              "      <td>33%</td>\n",
              "      <td>33%</td>\n",
              "      <td>42%</td>\n",
              "      <td>0%</td>\n",
              "      <td>74%</td>\n",
              "      <td>F</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63%</td>\n",
              "      <td>70%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>70%</td>\n",
              "      <td>M</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>72%</td>\n",
              "      <td>60%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>78%</td>\n",
              "      <td>M</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>76%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>40%</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8%</td>\n",
              "      <td>8%</td>\n",
              "      <td>60%</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>50%</td>\n",
              "      <td>-13%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>50%</td>\n",
              "      <td>50%</td>\n",
              "      <td>F</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3342</th>\n",
              "      <td>63%</td>\n",
              "      <td>-30%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>17%</td>\n",
              "      <td>40%</td>\n",
              "      <td>F</td>\n",
              "      <td>37.0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3343</th>\n",
              "      <td>35%</td>\n",
              "      <td>-3%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>40%</td>\n",
              "      <td>M</td>\n",
              "      <td>31.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>52%</td>\n",
              "      <td>-20%</td>\n",
              "      <td>-42%</td>\n",
              "      <td>-25%</td>\n",
              "      <td>-58%</td>\n",
              "      <td>40%</td>\n",
              "      <td>F</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3345</th>\n",
              "      <td>25%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-25%</td>\n",
              "      <td>40%</td>\n",
              "      <td>F</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1690 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-cfb50e5d-aa86-4012-876c-ad3bde9b09fa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cfb50e5d-aa86-4012-876c-ad3bde9b09fa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f50d07ca-b8f5-4599-8518-18e3ec75febf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 293
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uit_OjX5jZga"
      },
      "source": [
        "## Checking unique values in all the columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 294,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K-X7vqNQ2cH",
        "outputId": "065b3550-a896-4ab3-ee07-cced77780ba7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values:\n",
            "predictor1 55\n",
            "\n",
            "predictor2 46\n",
            "\n",
            "predictor3 18\n",
            "\n",
            "predictor4 18\n",
            "\n",
            "ctar 17\n",
            "\n",
            "individualperf 51\n",
            "\n",
            "gender 2\n",
            "\n",
            "age 38\n",
            "\n",
            "tenure 24\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Let's check the no. of unique values in all the columns\n",
        "print('Unique values:')\n",
        "for i in df.columns:\n",
        "    print(i,df[i].nunique())\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyMInQ2Aqi-V"
      },
      "source": [
        "## Let's convert the columns to number"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh99M7nutXvB"
      },
      "source": [
        "## What is encoding?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q20odR4FtZ5T"
      },
      "source": [
        "Encoding is the technique we use to convert categorical entry in a dataset to a numerical data. Let say we have a dataset of employees in which there is a column that contains the information about the city location of an employee. Now we want to use this data to form a model which could predict the salary of an employee based upon his/her other details. Obviously, this model doesn’t understand anything about the city name. So how will you make the model know about it? For example, an employee who lives in a metropolitan city earns more than employees of a small city. Someway we need to make the model know about this . Yes, the way you are thinking in your mind is what we will do through code. As obvious we are thinking to rank the city based upon some spec . These ways of converting a categorical data to a numerical data are our target."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRAjlHH0tb6v"
      },
      "source": [
        "### One-Hot Encoding\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKUX2K1TtfE0"
      },
      "source": [
        "One-Hot Encoding consists on generating a Boolean column for each category for a given qualitative variable.\n",
        "\n",
        "Limitation of One-Hot Encoding\n",
        "\n",
        "One-hot encoding is a very popular transformation to the categorical variables. However, it increases the data dimensionality (The Curse of Dimensionality). When the qualitative variables in the dataset have many modalities, the transformation via one-hot encoding will lead to a significant increase in size. This is not desirable especially when the original dataset is already large.\n",
        "\n",
        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arPZbDKZhMdBFTlhsRqNRpIktFo4AYUT0unqJAlg8VtUVIS2AG3wm6s3SqFfQcAZEIAQE+NLp9M5QoKpICKdoZ3uSgPgValU7tq6Gm+XAm+Nd4FCgIJAJRBw1IwHEx2tVrtt27Z33nlnwIABH374obe3dyXoc60sMBMyMzOHDRvWsmVL+KdNmyYbDq7VEJegVhhi0D2zZs0KCAjAjOenn37y8/NTbCnRfcABY9DHxwdA5efnYzqo2EAyY9uDk5eXB4gUcAQ4EFkZGRmTJk26ePFiq1atMLiwooB5jwxdlXgcpXjA5WD6ffv2rVq1avbs2R07dhQ97cZCQTCu0WgMDQ1Fw0NCQtCFVdJJSiFXQQCwr1ixIjo6+oknnkhISIAGQmI3ZrOrQCFHARMgABceHv7SSy/5+/tjDJ47dw66WU7jsR4ZnLCwsJdffhmKee7cuWfPnlXAAUsIcHJycnr06HHXXXcdPXoUigezCJdRPIKtYX6C9Xty51GMvmDBgoYNG6LJGPwe1fDqbCwGiZhYR0VFdenSZTh31UmAS9R14MCB1q1bP//88y5BbTUTeezYsQYNGjz33HPVXK9LVNe5c2chvjDQqpxgR814BKGY90B5wo+ZrCdMZtFPmOclJSVlZ2fn5uai4YriqXKWLVsglkqysrIQXlhYCPzLJvDMEGhlg8GQnJwcGBgIBAoKCqrcbnVdYCGa9Ho9wBFSFeCAcxwhYV0RIgEOxpTj9kccPkpFX4pprHtPZmUdg2YqHFyd400GHB5leVNGXjZXBTfiVwFHBkd4wDBw8AMZOGXYAgpZjjkUDWXNtxQrVv4RHSb3WeVLUXIqCCgIKAi4OwKK4nH3HlbapyCgIKAg4GQIOHyprZrba7GgQnp3FVtKctXiUcyp5UDFoyBwRQQ492ANxo6J8IYqOIty1AQjCYKsBNjIthJYPfSIASWGko0AgsIKVSUsWFFiMa7WUuUyrzSQa6gL5EZbPSDfRmoJyhFYOqnyXAYBd1M8fCyShLDvfsEiZdquBCgIXAEBzj3lsE2NiRQScRDR9H8xDVbRd4U2VHEwp4AUb3kji+i65vqsBUG527WJSuGtpPJIq9lXR4YAp+Oa63JABiKkJHkyddeOhQPoc+4i3Q0iGxeXaFeJB+fuD4U6Z0HAxknF9PCT8QiuCUfUYN+3pIS2ir7qoccCY46pS70doFKBKhK/laGBkKRSS2WW7CcSpQ7yqlXQvRbSv07hSihF0r3EHVBHTkGccxNRqtOdmFiwN6jDL3fCa/+LYEG98JzYuWHeA3e+t+Y3CuS2iQjnuctwhn2crR5RmvLrZgjYd7XctFKBxCQkR3BA+/yD06dPnPvayawivFzHg+3Z0OqXy4GH8laAheQ0sudKeUUClCsxvE5RtOn75QMH3bZudwJVyusnz/U5mQZrXby00oGQ+PR6Pyu8fOKZ/7tj7gdfpxbQo9kCshBjdcgKnz054lFEl4ylJpkZvei2fdWSqVNu/2zbEfihy1AA1BGy4Hgok7K+ePe5Gbf/38HETESCCt5q+xqK/aXqKo6osM++BPvetQ9HYXi08IsB4//57Yl7pr331TqqQRDN66IEZdhAEM+BLCeW57P+yHllz1VjrZhToWUrtYVcPapsrH2NVe53GcUDJgCtdPEZdwIICwwwekmbXriFQ6B4ZRue8wc3vbNoyfI/dsuQwTzBqTM5pQBaxIL35cLl9PYJ5EDF4x4I2LMBOprzhj0XoZX8pTlT5lfLlq385ofL+SSFBKPJXIRj8wINIWgFw6jEYXowJRdD5cIlWSQkozq4k98xtj7yG2ZRGjmh/xgd0KfKTHn//PLFpt8/P3zqIi9ZzlpuPRUKRCVlh0apQDQZFIhDmwWZSa8t/OztleuzjHQlKwWqOHmceoQI8cvFHcMILS6cy2s88mYRbSpGgxdu75aNX3y5dN1erk35MCeQCXAkKvx17YpPl354MslIT7ZaKBt3ojT8wqFwhMlpECKUs+3XmuU//4gSiDhbY4pbYZUzKolTlxK36a3Fyxf+sk0uEz1LxNvTaeUPtJa6U8Uj5fSkZks60elIKxcCj30S8WiLLc5uCxFpbbVyTi4ZZV+YtRZRZokIRz44+x4PZx30PvETXmsSrA+/wFEtgbMtCLVChDcVeEo8eus1viqm9fa1RpWXHVEoH2VSH1sLJ7khZ1E8boOAkErETpKENytFu6xcxEWhRPa3fTAxBdP7NmjgV1Qv1MfLdleV4BhbSuIWzp2wfzknIRfxE9agIKgRJeq1WqRIySlAVZA+tpSggq5WKsneyC64nsQSKAanq7V6ldHspSEyg4N8rCQIBWl7qPhfIoSUxBVGVmlMGF4C4pKW6fT+XozpfXz0Opv0QOPtxiBI5+VSKMDADXHUUjFkuRwvS6SffxACvQ0EMgY0QQlQMNuhPvAOqB3KtLleerrsERMNlEVkczBJIhDejAY+mmQxF3cj9CGNZaHnkJ6My4q4kh1BPEL1cqKs2e3kjK83yRmLb7Gc4WBQH8EJBkPFoEwQiikip7dYzqAbbITJfUJsarHjUhSFXDaGsF0rZwUVTaeMMlejWrnhvHtRkpUewZdUo8CxFNdVo+yzsQ6Hyfl+rGMDvSZAL0uhGixXMlQYUWB0BKvFWMGvSl06nV1fliyAnq5UXdmUSoiTI2A3mi3WEVmKYmEp8zEqx4DlaHxCSxktxiKTxL+3AAu97EuYJFCgJTRl+QvCw2qDW/kJwoczVsmqRJ2luBiBpAohuYRusxLmE2zg0plrDZnYa/fYtI6NojIllIeJmY8piFkongJbDtIoGnUpOUKylSCxJbL7S6iWab+KwGY6fo8w8ulLgEHahFkgO0l6ajS2qrj+Ka6BeqEEVKJOBKM64gFesR0h5XivImeQugRRkO6k70iy4JcbA/QEGsryASa4ZZuM1HD24aBRkCq4pAxIIkeJLCLI1lclCJT4XhkHCVmsecUf6h28al0izPbAe8724MC/tl50YBWVL9raE9QP1LtnTsXt2XP4Ykp602aNo9rERoTVAfU5l8/uOXzsRMIZb9+g5q1bd45tqS5xaQplJBNAxS6cOnFw/6HEyyl1wiOiYjo3axiKN5UthYXH9v2bxvSx0a3/3bxx3+mU8Tfd2KBubVla8ezKjwsjIMY2OlR4clPO7Tly/PiJU35+YS1jmzdt3sRPqzWbig7s3n70eLxZ49WsZdumzZuF+ht4m4VgwRovPWEAW3Iu7T8Yt+9IvNEixcS079KtAy5aQVTqufgj8ecbxHZgl05s3LanQ7/hHZpG8u0KXq8dP+VeOL0r/ny9Fq380k/9vu2fsOieA7u0RQknD+/+d9+BC6k5dSOb9+zbq15tPy4vVMyYe2jf7n1Hjqn9Inp1bGUuLeSJsGt1QvrLmKRdOLVz76GkpMuBtSNbxkQ1bNzIF8K6MGf3jm1HE85atPqGTVq17dwp2EACDHlt9jP+qnQatSn78h9//RN/+py3n190TMfYdq10HJMzB3eeT8tu3rrd2YRj/xw82rvPgOioxkK2ir6QyS7iQtygMmdeTFizdrORGWLad+jeoY0YvZQMspurnLPH/t1/Kr9T185hQVB/7PyxXQeSclrEdG5a248S56du37ln76HjWr0hun2Xrp1iMcaF+ueqlkq6ihNy5lRC3LEDh8+nZdaJiGgVHdukfgRkdE7K+T2HIGdO+fgFQ850iIlSa22TYKESqVyapSUnHDscdzgx+XKd8IZRMR1bNKgD6WMuzD/6765MrXdM61b7t/y252j6+Mk31osIwjwKUAk0BDJ8rsxyMtMO/vvv8ZOnvUPCunTpoTKmxMef7DRgRC1t/qaNW2qFNmvdNOjPTRtyNCHDhg/2Q7eYM//a/NeBowkWta5D1149OsWgHEw4QQ/Lv7T1z+17jiZ4+wd16dE3tnUTmpIyy7E9u3LUPhERocf37jx29nLD+pFd+vQL8jZYVz+vAlOVREHDOcLh7iMUu2TJEtwWLMoXE9hrqguTUjnXX99+1KFZXbnJQ6Y9lFgkZZ09cNuobnIg82/y5Kd/ZfFJ7d9LHw9Usd7z3hU1nvtr1eg2DeSU4a17f/XLDkRlnjw8o01djY/f+HFjBR99+ut2hGPeKlddQZqRXmTBBVAvvvjiF198gYxyYAULUZJVHAHITVzYjvSxsbF33nknPLgavGx20QVipSP51L47RveU2YB5R62Pz5Ckoo9emAlusTm/8fc/dy6Hr+IUnowN8/HtPGpvKhWQnRw/7/Zhdsaa3+znPr2YR3V+9fTd/ox1GXVTdChprElvfpvLqwQbUTQnwkS+wq0fP8M0+naDRrVtFIKU4x56GaF7Ni7r2AgFWF3fifcnZRZScinjq3ceqWMLbxvTtW/nVnhasnEPRZpRvGgZPZVyAhxc9YjPcyBKDEk5DSgSOeN2bhzSuYmtBhbQbOBualHeoqfutDOUtVOe/uhMPuXOPb0L47DWyHvP5lHTLp46fO/YvnJ2pq71/McreZul5wfFgPK+ffrV9yHz+v5XP6f88qAGAehBE1Gx7In7kKDnTVN7tbFRYmjw5uotPHnuPRO7M1Z70xE85T17W0ekfG/NXh5lfOWW9sxgeHT1P3gszLn84uyb9XobLf5hL635g/jDNgwFVTyABjg8vXr1mjhxIjxGLq/gObj2s47NQm1FsO433Z1skTIT908b0VUOVAW1ePTNH1JziPK9Xz+JXoy6+0X44c5u/WpYmwg5ZYPYfsvWkTzJOx03IzZc5e03ccKEWjx66TfbEI4+AgiUk7OI1Z935oUHxsuFdOkxOKZlU5g96+NNUs7+AA3zDmkzZvBQJPBrM3j7JeRNfe1/0+X0zDtswc8k3OAyk+Pm3kEphTOERb/93R7Oh+l39u8UUKt2zyFD5VXCaXPfE1xfhCm+JI0YMWLcuHHw4JsaVFaVOlo6dISrGsVDrEnUXdr7qxiU4+575MtFLzQN1g+4be6Z86fm3dQJgIbWi1244N1Hp40Vk8e3F/+GLNs/+x9uRuzBFU9a/O7mfAzFDpq44IMltw7qg1z+9TpvOytJOWfui/UTvTLklpl3PvPegdPJyF4JhSFnURQP9ZnjXUUVD9JxKVOUdnw2Z5iQiOgFb70xoTcERKs9Zwp+emM6MYBX2IOPvfzOC0+05twwYMZbOdSE8zF1vXw6jjhIQy/vrVkTEKn2D53zwusfvfBgCJdxD775LeLWPHYPz8cCQ1rPmfP0D7viZZFHxYCT8TVe8km7vnxTpGTtB94w+e4jZzNO7F/fphbThUWv3XU65dzJR8Z2QIIZb/2CxJs+nEOfeWCGcXfM+/it54e0Ced5NUs37aOyLJVUPDKvWlL2DurWCGW26Nr/5ddfntCliX9It/NG6YPHxlBFdWPvnr9w8VMPNOfD76YHF6FO47m9EMy1Rtx3nlqYN35oLySs17L9oiWfvf3k3ZSLsdfWkZZ4p6fVKGwb3f6xZ976/JedCIRD7eIP9SAH5asXHhUZg+v2Xrhw0UM3CU0W+MXvB5Hy7vFdoHg2H4PX+MSElkj52c9C8UjPDuqMoTz/r1OI+/z1uxA16P4XEhLP7V//eZMgFavbYcUuiioymawyHQ/clVI8BYVkslzc/XNDTkffWx/45I2nWoRohtzx6Jmkk3MmUI+EN2i2aME786ZYVcI7S9Yjy4GvH8f2VLO7XoA/+9y+KN5b7Qff8u4nn982kJBh3k0++D0RKuCZYYKz2JAp995+18MHjx5FFpAhE2ZjEGnRfb0pY1DLqU/OX/Ds7E7NaANM5R+2FZLJdLxBHbG9F96ty5A3ln53JiPlw+cmI0H0PS/vvmQ+snllwwAVMzRd/e8ZlH/fhAGI6jLnnYQsafOKt0nHNO61MxHGljRjVH886Ws3eeSJF567l3c3a7QxgaKKCsno8WDFw+WFVJCxfN5oYNTp/lfSuB66fIF0w/Ffl8Ge8q03IO6C1c5d8fI0JBt462OIPfzN0xgsfZ76BP517/0fwtmAh/dfonkYOP/xW4Yh4L6310Kdz4qmOfttC1baW8vyyOTpK/QjZ1EUT4Xwuu5EJLYqMOORkx1a+ylMde+wzkcu0rjCUtLZrCLJcnF8V5iTXi99s1lQlLxzNV3mzAIOXYJkzYypozO0H34aJsrRP1pie9s7dNPxiyLljm9fQ4Bv9LgLkrTjgxeQp2HHiZDacLJxXSxkbXJlz4rXkdK/5dC4JBrkcIumj0LIwPtJrIMhD21ZjEd981vA7K+MIyPpnqesc4XUM/+ObIMAtuQ3rngqO+ORefWn+SRGYwZOSuIUFxXQPd9YB2hBmiZw6aY4TpK05bu38ewd2uIyni8ehOIJHDErTZLS/10R5sV8G3baeoSGJNyq959ESjbgEajtL0cOgbfPtPsuce0CGSvSFGNiUzzfvDwXKeu1vPFCttDO0sKHSffMfPpDZHngZlnxmJ66ORrhS3+xKp75Q6Db/N6Py5WSd5Ec1YR/tf10AdqSn/TEVLJK73xsCUowFRnBBnKnUEipGQ/iLNk/vDwFWTo/OD+TE3ou8RQ44MS6rzCn8Y3seOKSVUJ88cItSNZn0jOg9fSPz9bCjOee+cix66d3EN649z1nU6xThOem34CQwY98hNjnhkbBP/B/7/G4YiisaEAdmziBxpONg8lMfn3dv5wKKXHPhr5hYIjQbcDYfKJRHQPU0LIf/xKxl49uGgi21tf+estJUGs25r1yF01xnlr+V2LCzlZ6Ftio/W+H0FdSXlrSyzO6U8mf/47HB8aQ/7a5xFoFKXGjY/Hkt2Z7Eh6LiqilDlU8fCKAGp3YmfMLTx6KB4FPPnQvjBgcEKldF/3ALhw5eZmxtreMiqprXWy95da7G/iyA0mJqegInVZC//Ctv6zcbKR/fMqkWOozC2zWyXcOQsiBLSeREFvHjGlvn3QDLwWx1j3hUsvQCFecqyLAtxDyzmVkwDCcPKFVKE1VVHrfev5alnbpXNIlXds+g4f0E60L6zzurr5Yzso6n2vEhjc4DhyByXSRlJedz8Lqj+7fHIKXXNeb7oiJCs5NOGvMQXFgN9Z+9MgIzkZiXEHYleUimJNIOXT6bVHhEFlwptR0Cvn9vYfoXn6Vd3Sfu/FozExMzjFmpuNUg9dQLiMQGNyg480330J55HNKeKikIxoLLoJ01qlb23B6smgN3vhqXFF6alo20/Ue06d7lCi79/ApM3s2zU+/kJSGZhKaOGUHTFIuJKUWsK5d+vVqhSFJCIyfOhNikG36A9jpORQdbxhXB0npXABhWQYTypXPvyEyYPZ9df14UpQzYgYWIhIvXUCslk4pUDKqmnelznZuQatFFMjWsAsmGuTm5Ml9W3hhY8c74qXl/1JA4Sn8/qeYoxdhzdq0FKpl6oghfN7CIus3wrJqRgLJk7rDhzSrY5Uzt97/bEQt3daj/56GMjZ4kUDh+8onjxxA9okTb64XAluWJMlt00ehqLSjSfDnF+SBSx67bzrZuVwuoS1gj9Icoi7MKlQx3243tLXOkOq37zVoRG9mxNIe8qkK8gpZWL8u7dpSMeCEbKMOPGJMmTKgJbbWNHqf/330K8I1qgJTVlHdAJZxeu/I9nXBWD7BEY8v2Y6oImMBfs1Z6PqwdnwJ0RAQ2rET5o4mlQX9Vh3Obr26OqqrTB044xISDpuDHftnH2vSl3Z4ufPS0JQzedcp0hs8ZO+O35NzWWytYFisKfycDO9fnHsjbr549iysGcGEu7aeQEiDVigWK93wSgYVOsOAI0xqHQ7HiCOY1k0/Xrby4/II6ILpTPT5v+JIJsqtgZWpVhXFX0g/dZm1xxQao+/yzuNH8TdYT2YOGS8qnNtnklmjVbGMnAOnzawRMRTLOrDlwrk0FtJJ7YVPARUhpK6QTTA8sRnOVRbkLCVFGRCdJDhg1hDD+foZOBnwa7VUPLvpvsfnTOqbnJrj7eOvVZn9gkMi/PT5RZAOBSfO5LGOVArE1+n9JE/VXIjzkOv6kUhssrPn03gp1pHFN59Z0da9mRezWUOa+xSmnzgWn8A0dQNxdjiT9qYhyEC0T4A/5nzpmem5aBEfbNknj1BZUa1JkhsxNJmkI7BMRqNKpxMb1zZMxOgkmLz4Ub0Tfx9idw/GI9yxY4dJm3Pxjd13ePmAVteqHYzYfIlKhoujUx96rcnCajGqRh/+6pKP29XVG400v0K3xHbvQenKHjWj0JIO0z0z3lFl+/49xYa0k+P0wX5QFXkHz8lyJuHAlozMohahjWGAZJlMIA4djvR1Ihvg98SxBMb6CTmze9uRLLQukjDEGQf8BhiIT4CGBmhYX0lGAAGKJOQxoyEWlnsiLj0rKgLoMktW8t69+5nKQEkQb2Zag1dhIeQVVDPDAm4e6vAOeW3R0uhIPdjZrFJ7q0zdhwxM/eunnCzmF9l64dsL6wfkFzI9kK/lrYvpMQAZM9Eeg1pn4JUyVWBAOExxHOOmOhzvAJoTO87czKd2jxE3g8pHbxvz4oefnzj49x0Thr7y6aqGg4cMj2Sn/lg47IZHDxw4+Mu3H038v+fQq/27dAUL5uQZ8VtYQLq9fbs++F3yzOTZr763N+7k12/Pe/jZxdgb6ju+P04pcebWFpmpV2H48M4llQOHEMW5AQJ0qJGx5h3bjozxStr52cART27f+ufqhY8PmDn/olfj6f27s9wD998/87uN/+zbvvH/JvX/I5k16X1vk3AM+1wcFdZoNYVmFtS4ede+zXOTdo4beMvff287+ve6UdPmJOaykUO6R2pZ1sWLqEILsQEHCYEKhZygZziMZ26rI5gLQVNhkcxeXYZ2RU07/j3o36LPjWNu8E4/+OxTj18sJPs6uhPNOebNuu/Fz389sXfby7OmPb0aNpNGLxSPYFYqvxKO5ODAW++Nraf/benr0x+Yd/rQ9o9ff3HYXYsLI2MnDGwCMXT/XXP/3Lb/r42rp06esuUiixo4toEBks5CjWPqQsYiOg1s0abB3g1LJk25Z+uufzd/+0HX4RMxBqfcOwqzuewcblkbyYgma45ylXVEhkpLRuT2z58ZNvut7du3b/hs/r3PzEdI95798IsVLabRcnWlbti4GULmPfvO+r8PrXz9nlW//MVCIgoyMlnjqB4jGzFjclJK/pDBg0cNbrvuq8Vv/bIzhZHQx5ULNvjxVI6zFJmZd2DfQaMQ9+mT019YvDzhwF/Txg5+ZfG3jQcMHdXO/8I/y4ePefSfHf/8/PUHN0yZjcnLuCEdUXRmfiHMEYsRYLDYbv2gCr5bdO/Dr36wJ+7kT+/Pnf3UAmjnAQP6Y5qBJoBBCqi5hAZ+ZQVMQZxD6K++2dT+AD912vipH375859rv5w66ebv9mZpA3yIiyXS41ihxFId0qKwyLZtu4zuiON8my5kDB46ZFiXxis+fueTH7al5rN6ndo0alsv5/zR7UXeA4eO7FLf8OE7r63acoT6DwTh7TFJbYHOhlOptLx06EJ6rAZHc1cHuCo5XADljU1Zoq4g5905dwo0xOQmssO4M5nS3t8/i+TLFTJag6a9cuJiNnL88uadCOw95w3Kbspb9vTs+iK/ShTAZr36IQ7vWJKOj6lNEWvjsVCPteAi61kfynZtTl43V/Z4rg24yqbGuK3IHo/oF85G5v1/fhnbWKyjCG4I+upwhnT+wGw6aFDsQmIG/3yYtjOknEM01w7vtO0C7aTv2vJTh/q00wsnll0adxu94ygti38yaxwCb3xmIeUCX0E8cyceKQRHHPjDb28+iJR9H18M9kMw/V9w8cMnJ1GhTN8oQqzjqZdto83n3HOH504bwKOYt05V28sryMsXj6+v3spzXu3s5VVPtREqVD9GysrFwXwCaLCOoja7kqW0uE23kfgrdq0G3LE+jsZIxrHNBgR3HHssnRq09fvl7ayQWBMPmDDrQjo17sXOjRB027Kf4cfIAgLW4Yxn7hBCElSSPniYN9+XzzitxbDB0186lYvInJv6EiUr/kzFA44NRNhGe90GYdSXAfUfXkmHuBL2bx3QvhECgoKD6vLD1c2G3XPgEu3n0RkMW3vxSCEl93isR/6K8hY/fAfpQDFhgXHa9abjOdKJHd+1jhDBPA4zmhtnnzhHHLL1/ZkICp/6FC/VuOr1WfVo5oX81kn1rBc/pK281FPTOJw/JdBmGNDgHMEbz3Pih/MLhaQf3TBrON/KQzk6ryaNG2EjTe0fvgN7izl7qHDfHjuPUO1Gvi10+vCWXh0bITg0NDRQTfHNh0w/lEzY/bX+i9aNCKSQBs2E4Bt49/NJtMtUcFMzYu3//bgLD1JO0rOTu+Dx03XEddVwuMAqhVGlEzr+Ho8K40Nj8H3gjQ+jevb57ufNlzNymrWO7tJrcKA3azBg+vdrgtb8vD7+fLpW69Wzx9Bp995C80/GmvYc++Jj6pbD+tCDxnvac2/H9Bm4fM3P5y6l+HjV7jpg5C2TRsDMNNWKuP35txrEJUbXxfocFL/1bWnKpTi3QEDMXDGgsWIW22fy6pXhS7/6Jv58pt7g071n73ahXqx2zKvf/t582ddb98fhPFBkw+gRE8f3b83tEe+IJ194Mi2wSRMuyDr1HvX9Tz/+vP7PP/cfMkuatp26TZg+IyoEfMQ633THI+qwvsN7C8zwHmEp8MBaIih60Lh77ivqN6wLiW9cPoZ9IEPo3c8srt2k5+oNf2Xn5vUdHjFmyh0je7REtE9k66ff+LR+9Jc79uxVGYL69RvQJtx/9a9/927dkMSveL2Iyrk2J/ae+PuSqmETpm+p32TF9xviTpwKrl2nb/9B4QZTUFT/t5esbvn5tweO40SWpXmLtlPvuKt5A9IwPmHNXn5qVmGLQXV9Scj1GjPl97+afrRq7e6DJ3z9A3r0GTJ87MS63Bwc/sSzGTtODGxPDdHwXZBS9jQwEQKo56ibnq/VNKbXCFPC9u//3G1iuq49+02cNDmSpL1m8l1zIrumdGxCgEXGDl224sMf/th5/kLqwIm3R+iNJ5Ivdouth6gmsb2+/Orbz5Z/ufPwGbzr2rXnwJsnTWlWBwoAMPHFUps6QeJSDkxC0kbrfdebi1sMHfXN6p8vpGS1ad0sutdI7AOGdR27es2Pn3+z8mRShlbn1bnXiCm3Twzhpke9bjc++5i5bp/hvEDd+LmLGsYO/eKHtUmXU7286vYYPOLWySMxMbJ4h0x8bH7AoQsxoQTNldAQVAW2HLxo7fZhq77e+PuWOrHD+reOWPLImM8O5adh8TG02VPzHvSK7NasPskrMe9t2Lr3jytXf7bix60790sabcfuvcdOmtwmjLDrOXTybz/W/2rlT3/sPuzVo0vXvoPG3jQpnHaZdLc//ESjxNxRsQ2pUu/AQbf8X0rQ2e7R4XgSEzIKd5jDoqtDFvUKCwvxvfdPP/30oYceyszMBP00wK518Qp5+D4LpIZYd64IDpgm0fUedo4LnVI8T9FoeolQjDAIJ7uM1+QlYsHZavWFCxfwAlOjRo1uvfVWOfCailISVwQBsC7Mdq1W2xarDV26fPzxx7DxdToxFSkuQO4CIWeLI/7LV5Y9SrKLnB/9XpprUs7EfbnmF6PJ4uvjLQgAS6an5Uc0bTV9Eq3nCCc4zixZNOXxHRoIGsRbjbYcJf7yisupXSSCUQ9wgEyrVq2WLVsmhqSIKsbkmkYWv8zBfsgQgeUPGUgV28TESrLl9OF/V/ywyc/fF3e8UMNosLCUlPxO/QeP6F28p2JNXmV/iBK0l8ue4m4C5+Bb1717946IiPjmm2/AOcAKdV7DWhP2day2hJVWDkapVlMUr724aiF3fv/5hz0HDnvXCkQ00kBuFObnm5j6hlvv9knb/Ud8wfARg+kNecbit6/p3mNciqbR/gsJsbWLyxHVXYWrr0QPFXpVJ8AZOXKkl5fX6tWrMR2E56o5rjnSqWc8xJjoNOtVtYCRbhBBE+HBr+znYdZwCuSnZyib1aFPMY2lRQ2Zq0RRiKBkvDiKsj7jjy2r8tf1EaDu5BcCy93NRaLMMDyeHzGROUowl5xe8I14LMV7PCUvARzGNRVnI8mYl5GcfK6gSPL39YUCAIo6rfoirho11KEZPPaE6TocK8eJy7ZkNkRi+EEMyhULMNaCeV8UE2Clj2q/VifygPlFGcVlWqumImnE2I0yGyY0ImWbDdkxtNAiECCjJzxII48saE/cb5CTlpJ4NrFWYC2t7fAOjlWcT8qvl5pNJQIlMZL5r0BASAAUzhGwwmWN4oH4EdWJhsg18iyY6OA/3v0k/UWjrwYVzHDqRZuggEduF6eAnqwlE5EabIsINHi4AKM4u5yyDG1kS+SkpyWfO+eVly9OKELt5eVmq3Q+uXkFa16b9/iyHeENY2bNvCkj+czypUtSGBs56VGhdfBqO5osM0+ZwosxKDeKw0XdW5o8G8g8ASId65xb8XBLkisQQorQlllTMAWxO82FBF8iFsF2wxBsB+5HATw7Nx1LMJPAFuGcw6y9YZffsdgrpVcbArxrbXwAkUHig7MS/pJwIakJRz4K5+xAfs43toQUQLKP/6Ufiii+LoVukEbBnMFUEa26vTq/m5yylAdpaIbDa5ajOHsKEogcpCGCyfTitPGSrT6SETjPLLKKZsnFVMYjF2vLDE2BAccp4kFIQFUWt70Yk5IJbQUQTKTXOEQSP06hj+494sPeI2wpSv+VSq5SoEZqo+iC4s6iXHIXCCLkzhJxQNDWjzT4CWRBIn7/09nDTZVSSdRukj783FGJNRIZeVG0eKRFk2J04JPp4/zBcaSUY6bcjn/lUnSuw6Duey7vjY9/8smnkcDLJ3j0pDu/+OIeQYw4lS6XirLIyaBYyRbg8Si73i2DqoCKJysJsjXIYX+cXfHIgApguX3BURZMAYagcUmGIXoCaTjevC+IWSR+TpXAI9awHV4s7iOMDB7ORQhPVjy06FFxboMAdTrxhbXzwTaCYUQDRbcL7kKKYsFRzCvgNMqMNOLXWhYECRjNVi7F0rnZkiqlJIhgOS5QabIgChF/IAMEASKQyztRJ+UXUeB6QQA/78mrpchKOtE4uYmiCvkRdWJk2ZPEz3/aEOR1CmJoqNE0iJzIjkdOHLUJraVkuBDZpipFSvtfxNJY5ZMSURTKERLfrj4RQ/rA6uPVic6y5uIPxSXQ7ZwyUfYVXtEv0y+yoSgrIPAADZucKc5PFXAxwoN4dSXII8VjKwSgkKzCWTIbXMXlWH2YoKnG/N8L+JeUnJwQH+/lZWgVHePHr0+lk/j05g/pUszmOMLUPECEApFTbjg4UJBtJd7WQZTZaj8IlqdCisvhNCCBlRZH/nF6xVOy8TKOcnApfOUEnN/kVNQ3eJBjrRFW8O2SKV43RoAPUtG+0pzAQ8sNlPEQzCLS2Ke0K5XSWheG5WxlPEKSkYywsZ9cQoliBcfasstRNo+cyZbiuv/aSrYWJB5LBpauVI6VPSKz3aPQN8gI+XZFR5Kb4jkoMiw2jy2bKJVosCvfGmkfIvtlj62Eiv6VM5byyI9yQRwR/mMjBH9LJLNrBbxyt5erevh8yGpTR4SH459cEbetqWBRgn2Vco1yvbJHzi6HyB4RVaocOb2jPS6meBwNh1K+gsD1IGAb1XbCxr44ITO4yKGU4tE+gTv6/wMTuclXwEyOdwMP73DMS9CUcvpf2CI0Xy5/yoEsfErjFkBVq+IRM0E3YCClCU6LABYKqmetoJIIcKlDeYVHfqxkcVfMZluKLpHA2cEpQawDH8oFB/VVg4CSO7z8/qc3XcvdbEHX2aiz/XUQQFcCp2qrq1bFU7WkO21pTi34nBa16yBMBlz2XEdh7pZVwaRsj8qYwCPEuBxSNnF1hlinOleY8lQPJdUDhWMVD5Sn/F6FbcZdPejVZC21a9fGO0x6/mEQIFA9HVmTDa7pusFa4j0DGfOapsgp6hcvpuCDWLj3EwSBJ/FbPfasU7S/AkQAnODgYCRUOKcsWhhTjpNdjlU8YP309PSdO3d+++23sCyghBzXkrLAVX8IRjVe2mjYsGFGRob8Ppp7N7n6QZZrBLB4BxCPePvv+PHjv//+O152g5BVAAcmAAGuUaNG+/btS0pKev/99+Pj491+AMq88Z8eiCOM0927d4NzBDjgJUUrC87Jy8ubPHnymTNnmjdv/p9IVi6BYxUPaII1iu7MyclBT0MWYzBUjlBXyQXFg+9gglqFiau5ywB7djZd01fN9TpzdeBG8CGQwQCEE3ramQmuNtogjgCO2K8X+CjgyODn5uYCH4cC4ljFgx4NDAzEx3fh5Fa5vQf2AmwoXMWBlipy0HHdDZEqrsyBIQ/TbDh3jqvORUteu3Zts2bN5s2b56L0O5TsjRs31qtXb/bs2Q6txUULr1+/Pq5ZchDxjlU8EA0y6VChDmqDUxULGwp2N1oNpQvCFMVTDb0D1hJsBsyV1SQBuKyV8UXR/Px8BAIi7GQoDAkoAI6w6AEO9gIQIu5qU8ARzAMcMN3BvMfHp8Sd3CK2Sn4d/v4Q+lhuTJVQ7LSFoKWCcRX2rak+kpmtpghwqnoVPrxKdwhwwDCCZzzELL4KIHKUAASPsKHlwCr3OHbGY08u2uPQltjXVSN+8K7cZzVCgMdWag87/PaPHosJGl4WB46N1RD0ZGTswREoCdHk3gKqgj1ePfaKA3VaBdupJFMQUBBQEFAQ8CgEPE/xiEk1frmzvqzsGftPHsXZSmOdCgGMNkGPddxVyzUBToXAVYgR4AhkZP9V0rtBlGcpHuparF2C6anr6Nxt8VXDbtCZShNqFAFZZBCbcUcMZhO4NUpaTVYuoOBX05FXkGL1iD+2wJqksqbqJkgghIhPsAYq1v3gJ3LEHzcFp/r2eGqqZ+3rFWu44m5xW7hku/fcFqD8VRC4VgSKpQMuvocAwbcAlK0UDiJHBuPOKkxLAcslLnaxSwV7yKPARAYH2gbHk4rbLtSRm+47eZbikXvanH0+5/hpVVCzgCZ1hWXhpv1bzMaKz3EICDOeJIgZn0WB2aoRHyMraeI4rn7nLZkjw/UKfWimNJ3Yx5bFrqcOQA5OeY2XwQGG5cWXBtO1nl3S1rDNQfG3hAP01me6+o+cCJHDyRrFZwmxynbpWPqqlenb4uFXnILAdSMAyQCj3owvPIPFstZ/krR8g6WQPsApmPC6y3fhAmgZCQoGsJzdN/uOKYMHD7rxpknL1m1Dk6CiMVJJqnrkpAfCl4NDi//GMwfmzrwN4IweN+HTn7ZASAEcMwfH/bQOut71FE+ZkSy+b803bMTwxHQVn4C0W+uAAuKdR19IZ2YzkqotRSw9W8owu/CAVkh3GgT4ShIEqIYV5V6cPfHY8JknX/iyKL+ICLRfPHEagquPEECD4aZSZcTvunvypAUrf88vKDh74O+ZY8Z+8ut2kIEvSmNEX2ElrvrIrJGawDEQVFDJOSf33n7LxDe/+jUvv+DC0X9njBn33o9/gCQNSSxyNUKeQyt1vaU2of/lRQxYTLSyQXdF4suwJpXarkX8znOkp64zW9QaRCEdJcVX4DHJV+lcT+86lBuUwiuDABcMYDOp4NK5xx45u2Clf/0GqkYRGoW7gCY3+vBnyQcLv/4rfVNyUv8wDMDC2R2jn3pkfssWS3s1DsTgVGk8csoDcDQkgpYuXbxi+4VfTicPa+iNxwd6tH7m0VdbR7UZEFVHAjjuiI3rSV7SIjCRsIhhNJnpG+ZqS156fkK8sQDL66R1ipLP559KKMrOpzj6xjh9jRxaR2KmogvnC06fhCGq8vHX1fLzdGsUYCnu+hGwyYXcQ7vzc6RGq/5pvHK+Sqsy59GdSZ7tcMiCvmrNTv61K/FC84de5VoHz4Z3fvvm0sHNP6/dRPhIZNG7o1lPjbuawwokY+aTW/fEn2z90FtC6yD9wvVf58ft+f67DZTXTWfMdvODqyHkbHG0sFZwJr7g0EFdWETqys9yjyfWfeur4Ba+qUs+uPzjLyqNxhLQJOjGiRE39sWFCUhsykq89NGn2QePS2YTa9IruGEhKzCrtXyi5GyNU+hxLQSsRj3z6zS8xSfDIUnSlv9kysnDKQPXaocDqCWzD8UeO5GsOp97y5hYqkIyMyxRBNWrrc9Ky05FgBUn17OBqTXX48QdAfuPJBSeO/fw7d1QlCSZaf0moF5t/9yMnAyEABUSdm7nXLG3VaIrDIaCrAVzD/bolXlSG37/Q7VbhCQ/MCrx+QXeg26p9+ZC75TjibdMObvuOLrMnHH25A1tz7/3re+QcaGPPWkwnTp757zkD3/WBNEHshSnIHCdCGAUYQPdJiJyWUERnTFQ9A4NVBKb2flpan9VszZRHGeBS2gHgzboslA8PIQ0lGc6k9rbXxMYicbbWCa4rUEVlHoB2NHhfHd0LjjjQU8QM8OZLH6hvmNGt/h+IVZG8/evvbjoSPjqryPG9UGc/9qN+nvuT3v4ufojvsxeuTh9i9Ti72+CuscgKvjlt/2CMhM/2CCZqBTFKQhcFwJ8ExGrudirYDjWBg1Ek2xXNOmuC4b/yKyWVDh8Yef81AadhMVytzXq7dp6NS8d7VMxU0mGCWCSF775wZgLCuirNVaOc+V25eWoG7by63U77cfB5Dyx18ySMz96uXDz+0VnM/Stm2Su+KDI2CArPSf30lHd1HsCuNaR8GksrTZk0pScBJM5O1cGQvFUEAHaYOOiFumFB78VzOueyTggfI+Cr+qikW5qpV5791mBMElMY7R4G8Wkxhq4w1xQJyQIrIMVp2Jj8trrcPUcJrOkNZr9CvgZSFtjdkoWfUgQpLObbvG4tEI1m3AYxmZGWViRUd+hl37CrX4Na0kXUlUBQX7RXbxbdtYZjFkWs3dUBxUZVxYJZoRWqzapNQZ/iSnHqW2cXqm/UELIJ1RRpQpwi0xC7+JX3h/Hxgb9c4vWXVcjMPkjNdO4QSTTSz/8vmF857EWM/ZWIVFTLub4+wQEIRbvN9BUyPOsF7GM1jAi3C8sZGtiwiQWzDADpI3nrAvpPga/QKAiZoTwuJlz5d6mmTuOqgnlobbUbVC4/6C+VY86g0eHTr29zpgbDYP75jG93idYH9ok5/nHctPQd2q1wYChkLbuj4ufb9QFBbhZd1ZDczC/Ee9Uw4NVAo6pZ76GcQWwoYG0embQipOyV0jkIcFgEOKQuu17efv7LH/vRfi51mHb3pnpHxDWPbo9ASG4SEyH6NljHG94aEx7nZfpg5fmULO1OvzsfH9mUHj9ft36wA/VTeFu51xwqc1mSFoKcs3p6ea8AtEpXt3H1B39Yeqk/gU3PRA0MMq4ccn5dQk+d71Qp0PzoNEzsz7+/OSgXiHTbvZp5pu/5Z/UhR8VWQJMOSWmt27XuQ5pkJjf4IuNly9fFp/3lhffPH3NTeBtMpovJBfEF1ho8cTLw+c9aqaiCY06eMa0O3dsuqPbqGmTxt6QsOXHRZ9/P+Px90cPbAnM6D0VnH7D4oVDGNaZC1XRC6SGiMnjb/tt9YyuQ2+559YRh7f+8eYn38548oPxg3AWQ8KmIS1FchXlzC25VtpcUPHYjABN3YZ+AwflNw6hNktM5x1a/4tVmoceSd++KSV9n+Xsab/uQyLHD0KkLrxV41U/JM6cnb1uZX7r8KIT6UF3j68b3UVT31Efdr3WbnC59BgJR48e9fLSBwfXBvGy7oHfk9UPLYyoDD7tu9SdYFR788HldiLjmngV+4G4NQjnCrqMmvzxuxmzX1vwzTfZmXH7ho743/8evBvmPd7s1uBABnC7pnLdIjFGCgYOwOk8ctrSJcaHXpq/+tvMMwePDhzyCK7PEeCoNbj3zw0nPa6neIhBwcywFCJigqa39BVX4xB3q9U+jestXhWRn5Yfd4a1amPw0qN5uDwHRpWmUc/GG3cZk84U5Ek+zRqJZkumQrdg4BpoBD7JDlvs8NGTrVuqQkJI99vrnhogyAmqpF0e8JrGz3fojBZDsQJMyyZuulJSYbjFyQtskatUXSbf9/fkqSeOnQ8JDwsOoA0MuqgNWkfsjQE+D3NiyBDTqFUdJ9y5ZcKkY8eSgkNr1wkKJnAwCcT7iBwc9zPmXE/xwIaCkKPZuUXS4oAaf6uHDg4glBZE1SrvYN/21HM8AJNVcQaLTnPqIxrqSRUxCUdJIDq1Bjo1whNQesVVHAHcB6Hx91ElH45Tt2mlCQnmG6FcyliHU8WLcpeUEBGcN0nI4jomNMtjobDvUgECfrFgpFH5N4+i5TU84tc6NCm1x2kdarSNQ2zg+EVFtRCB+HVvcFyvv2EggWiwLTQOtAbUD90AjF7iXUX3g+JROCgY0Xv4VdM9uMLBA5VF5hYcZAR+iQcUd00IFOWzsGaWF+pZPt9zNCctNY32kfllzAJyDwRVsCUsIjJWuRNQXBOsbpaYRp4w/HBrlQ0WwRsinMfjx83aXaHmiLYTGp4Hjgt2OJjUNsT57KX4K6LowhIsjAcKEvZVCd7mRRBz8AQloirEMh6fCNfmSsygksxNihZGSKv2xqWnpaba6x5PRAhcBROG8xaYTgwt8euJaNi1mQYYR8YuTPFaEfBMcFxvqY13l9AvdlqmfC+fBCGDVQaUUDByDoosEWNlCOXPfyGAmx/8MfFsY3pIrS3aGze+fUsWXHK/Rwb5v4pyj3hhz8hWDW+UwloEg2CE0uxQ+tk9uOCaW+GJ4CgG2TWziZJBRkAl3sCVWCvTvAhp9d64zLJrbnJixaMgoCCgICAQqL4ZD9ZhaE/GfR0a6L6Nu1rLzGq9xmKE7sE9GHzeI9XgvMe9eexq3VCBOAWcsiDZY2LvL5tSCalCBKpJ8WCrBc7tRTMYV6vVoqVV2EPOXxTmPdhIw/YGdI+k1eyLG9OuNQsOsp6xBv0ApHoWVXCtYvVU5PydAgqBBrhRFqbweBpnXqWbAA5eCUACgQ8exQ7lVbJ4TpQMjuOa7HDFI69feo5ECAoK0ul0GPOO6zZnKxlah948t0itTXMw79l3dGy7Vqpg6+sIxUcLHUQ2RIZA28vLy0FVuGKxQrACE29vukdXAce+EwU4BoPB19dXAcceGfgFOA6V2I4VjiDdaDTiepXt27dDOqA9sv1Vqqlu84g25ufnZ2dnZ2ZmolFAwBPMTD7j4beeyLrnyJh2LR2+5ibm0DDQgDbYbPPmzX5+fm7PYxUcLIClTZs2qampJ0+exP1GR44c8Shj6OooAZxWrVoBHAioixcvxsXFKeAIxDCmIMH69euHXyjmq8NY6ViHK57CwsLjx4+vW7cOIhhdW22rLpVG5HoyCh1Tp06drKysnJycShclUHJmjVV2s64c3UNrbtZ5j2iLaFelYSmVEQpGKB54oHh27tz5448/4hoFRfEAKEANd/78+XTcZ2g2A5n4+HgMQIFYKSQ97dEeHHDO2rVrExISYDJ6ODiABVJajKbAwEBYz8HB/E18B/CHYxUPWgKdCbPr9ttvB/eLVjmgFc5SJBgX7AuVA0uqVq1aIKtyQlAAhV9naVgZOnANBA6L0CUQ3IFQ7GuVo3tozQ37PeIKkCpecwPaYDDUD8xDQ0N79uyJZROxoFQ52K2Ncf0/QAYIwLVu3fq1116rX7/+jBkzDh06BKBcv3HX2wIBDjgHcmnRokXgGQUcGVMBDmaBbdu2DQgIEONLjq1Cj8MVD3Y7QG5UlPjqbRVS7rxFQev4cAcSMfgrQSj6G1oH3Z+RkaHTeTnbWQW1SiOZc9UaL2YqVo1X1D2OXHMT8OJXr8ddSAyjBSOnEoC7cRZoYnGZHuSsAk6pjoZ0UsAphYn8CM6pnPiSS7iKx7GKBxXLpAthehVSXD0KLYWDwjCZTPBcv7GA9bp9B476+eEbuJDqQrA7CUi4dMuo0UZbzCUsaKGFypn3lFxzA0QCnKqa0kGeigKBvDB0nASmmiUDaIjFbQEO7BjHLdnXbEsrUTsWYDD/w6/MOco6pAyjAEcgIwdWrcfhiseeXIe2xL6imvVXidZBEyCXJW2ttuZpKnOGhXnz21BrtmXW2jGJw5U5ZpO/ryWOB5X+kGs5uufo2PatWBBfcwMbyLqnyttDyr9Ss8wqp6RmC5QnN/ZocGwqMwWv2bY4tHZ7fDxEQF0dT5lzrp7sOmOrVfFUlYV7nW12UHYHcC2+n+zjazpI+yfO6iT+Bo/1UiJO5JXW3PYeHdMuqjrOuVXPyHHWDrkaXUBGAUcABKNeeOwBcW8BdTXOsIuz18R2wVXsrVbFU8W0e0BxsE4ltV5lMeLTHPA6X4st/A0ee71DNF5xze1Y8fs99vMeZcA7X88qFCkIOBABRfE4ENwqKdp6HxodGXNCxUNKRqiZchtbas1NpTXvOzqh7Lyn3LwuFmhTpDjibaMcXrGbpShWKzo4AF8GHOsForZwT/xLr1/T1R9Y2RD4iHUECnRXONy2Ye7aYS7UrlLzHuhN3KkTblm17xjuEi3xDQUHrFJWK06gHzKDPlDIJKzhYFffZMKuNR0w4TrHiZdKqwEnjg4/UGIPDoHlxO8LVAMudlVwnQPNo8KXwIGL4Bf8JbZyT6fMeNyzX52kVdA9GDr28x5xl+i+uPHtWqqCQ+j9HiGdxa+TkH2NZKCJpF+wOI4/JYUpdA/FuXLrrhGMkslJdHJIrgCOwIbEayngShbjrk8Ej2g7fBYVPlepFhIZ37d0b0DcQ/HQyOcijjqRe4S17a786krtupLuqfF7rKsERCE5oGzMFkmjxrJozrbfNu46dFzlFzZ05I0t69VCFMneKqnMNQtB22HGa0gzA5zf/j54SmcIGjfp1gYhOtLI0DhAyCMBstM6eLme3kwwFhw+bPKJ9mmsU1vfEHBX9ePkiofGtRi68qCzLYYWqxoeZTOahCSwnQMTBhUSyB5bORQAv13fc9uDoq1p8ceWWPl7XQgAR4BbZt6j3hs31v7bcagDvedasAtqLXihCYIjI/Gj+U+/u/ZvP632Qmr2u0vXfLH0g+5R4WoVaSVA4IEMxccSByfz3DKA8+u+IqbKvnz5y7Vb3n37le4t66ols8QIHA9UzoJ5MCkWWud45pfPJD61Wz11U/Mn6un12NGVN8Sua/g5ZWYnla0QQOT4UOWfU0AfCacyM7MKM1KhN9A32MC1OXShGafAbE6FJVMuyEjwYcW9OCX2fykGspBkAZmrdJ0Pd9Yo+Kl+xVURAoLPZN3D93vm2r4dZ93vQVUuBzuYhPiEm6vffPzBva8uG/Lg/B17j2xb817enh/ueu7IFS1JAAAus0lEQVSTxFzwqAbbPTYTtooAdZFiAA5uVwKx333x6X2vfD7uwVcP7N69Y+1HSZs/m/bYW6eyCRyzhA+meOJg47xDC2zMXLAm6dVJ8Xeszj91wpySa7ObFcVTE2zOhRDXFnTUPt+Un2vigxi3hEGjSCSk4ChOYrlFedAS6EIyPC2WbFMWAq36iQQDKSdcLmY2F2aZ8khbUU7IC9w2Bg3Dc0lSlimfYQhQqWJpviZa7b51lqd75O+WuuxZA/AftnZw1P3S3q/XbQnvM/etO8eiDyM7jt6z/q1D61b+/Oe/eAQPSsRY7tu75bcMhy34ObbLe7/9bZv/DU88MW0IEtaNHbZ3w7vxW3757vcdeKTBhhFafgluH0pHVU/lb3/v8pKQgEd+qf8y0wQVSka3b7aTLrURF/KxCrXCzLmHstauy0kqNKvb+nfr6lv7UpEp2KtJpNZ8Ji8xoch4Ku/vFJPx1vD76unUuYWnv0vberwwuY42rG9gv7Z+DTA7ElPWxMwf12YnJBb5NvVpNDq4Z10dfYcjrSh9X87Z7rXq/ZH2z8G8I7X1rcbWHhSk06tViu6peuZHt0LGyPMefL+HnzUQa27FZw1457vMmptFwjoaO3PwiL9Kum3GJBCPw1u4LDa8/00+mQ8fO3GUsU5afCNcwrwHyqfqUXXiEnHVsQnknT0c75uVNW3yCCIV90qoVKF9JgTlzD1+9Di7sZsGt83ScPfEpUhoXLS9oXfnlS23BhnCtiS/yCy5MKwJKLd2TjoOIHVgLJHWMaW9njA65vikffmJSaa08Sf7h+1uNvXMS5fNOrPxyKMJwwceibkz8e5jxiR/tTo/a0P7wy3mpSzNZYELLz/f8Vj/ZSnHeCda1p6d1eTomFVZO8wsb3bihCHHpx4toI7dk7N54JG2PrtDlmRs1hTtmpU4ctKZ5y/B4KC6FVf1CAiGk3WPbc1tzd64zLS0NMwf4GjO6SISWuaSi6kpFi99dNuWgAxN4MDVi9Yzw+UMHuKkA63qO7hEidAopFQupqcZ9ZpoXFROjkIYC2/lZfZOSYLPBhcP9qQfwee06qLxg9YBMlnmXJojegCzOOmMh093aGl45aV35qVvWtb01wmBAzALeTRscoe4lvsL4nCASKPSpcOe8u2/u9nitt7N1UWF0SenNPJ+5JMmLzUwsLca3vX4yXsePjf+tqBDp3LW3pz8/sdNfr09lGb6L4cPGnxi/MPnZv7S7ONCSwYGwt1hS54Ln1pXU9DC4DU69egnuXGP61tC83kAA9TAQAeqZeY9czXanP1HxrRpXhhaN5wWR/l+Tw0QV9kqsYuDq2GZlphWdnW1el9sO3LZKsStHOVRHoneT4E8LSFtwjVe/iYODucHjwJENNbK5/zVUSz5aywF3NzCQr/7g1GCFZyqudjJYcaz+3JPhPrdOq02KQy4Rn5R30Y8uSAzKQMcqzW19erawv/+Dr7NEZVnOnSk4HJPf++j2bu+Sd0Za+h0sPB0Wv7hzVmnA4wn8tRNsX+z6tI6IzM20UUeLrygMm9GLoulgOnZK5F3BJHE0N0QNMk/c3WSKR0PiqtOBHQsy2KWikxF1VlpldQlNIoZOz1FJkMhvwHMNg86aTY2x24kGfmerHdwoSwOVOP8Tgm8T0mmujwA0AAwDwQIgJCJxQ0xYhmaKJfEqARgbvXgtIqH+NAkZUkqQ0uvzgS5ZDFKFr1a292r0YZcCz/4UejDdIEq+p48XJElHQcLfFTnLhgPn887l2PMb2Ro8UT9UY0NIQfzUpi2xfmiw14WY7ZkOqe5MDbo1lY+VGyhyp/pYgJ4d9Pis6XQwHQGj+l+jlx1/wh7jm4Iwf4b5LKKxeseOyPNaNMiICw8VNxR6CpLbcBOaJnIyLBa/n6nj59hHVrj7T8V2TH5yUamr82/B0jnW6obZyeoD9gQPGGhofh3Mfkya9vQpmJSEjILu9cJQSx6XGDoBARXMwll2RxyD+emqpmMGqjOaRUPbThqdZE+auOWjA+Ti2aF63BZJnXIguyNG/ILJ8BakFRFzGJifK8G3aWujYcA7YDbIiZOMedoNH6sKO+yKb+Ot/+RTAMr/HNQrUXdApoYzbl6ps8uyMxBAjhVINNHC8YXK/N8GHhAz9cAsxVXWUrrnJbujG7qL7SOeMUdqxBlB2VxfqfxgUtxsBLkNG7f2WRevGTpBw9PWoiTBQhJ++fLNJ/6kS1p18fEJB32rjxOvuKQKLW5YfsOTPr08/feenjYApyfRsilP79mgc3bxbaDX6jpUvMhhHugU0HnYIvTA5banFfC4lAbUweODLqhvuVQr7ix71z+7XjW+lfPPvTEuW+OF6UHgJ+lwjxzXr7Z2ksBXi1mBvdanvbeB5fWQ+vsz9025vTURy4sAft2DRgRpcn7X/LDu3LOMeb7aeq7dyc9+09eAqKyzedY/nosBFi53FKYas7J4McZbUEeyP+OajIgxT97rZOgm3damtG6qY8rah2CiewUFd6BZH7Nu/Vsf+TXRbNeWpl8IXnHxu/6DptZf/iEAb16IJVaraVjkh7HUhCjApxmnbq027/23TkvfXk+OXnXH2sH3vRAuyGjh/ftBnD4UT9P2Ncgfrmqs+Sas5k5uwBnIN3dOeuMh16zoVWY9kG3fN8obdK5+e9e+DTDy7yx6GxL35BQfdNUdI3Wr4lXA8lAp6LJTNb4fNRo6ZTTt75yfl5izqEDBevXZf99RySd4Azx7bK04aK7z//vsTNPtTG0Wpnxpr9Xy+kGWqMLZ4YYQ12Jy0M8mrQhA32at+GTIefVySDUBZ2QuqW0zilpZuum/hHhddGD1In8vV2XmOtYe4DTyllVdceseefizix6eY4lcWhS/L6L/i1XPzqrdSC9p6LR0OUwnrCEUpoxaSDT+ekp9zx85kj8Gy88ln16WEriwRzfJq8/eFskliEtZhW+pA4j0+Omg6WhwqJNqL55C+9cfz4pRDTsGXdFxUkVj73Q71B71vHas45l782wqJ6t1S49Y8VzF/emmrOYT9S40OlmXRT6By/xEXcbmn4RtWNj6qYN2btvq/3Ady1+Mmi9xa5ltzqz9geO+yL1tyPGtM8afTkseAC9NSCxvn7Dv/LpbuDnbVCExrvTJxHNg/S10evW13/KsIcSUGkEKqJ1Kl14zWTk0xhoH2gXlV/EM5+tGnv7qg07E7v07PfWqMlNg2liTu86QzW5kjqtOiz5kimB4xX6xEdfj53+w7rtp7r06L184nhfX5iMmOjgFjerTVJ1tbpmSWqvbkGTfvYtaKIlY9pdVY7oGydVPNbhzNjp/PiNOfE3hwyL8m8vKH7m4ispqj4xXgF4jPTryANx5wY3lekFPdXgkAH4JxLjF+dc8YvFU5UufErYVDmcZklM5e0b3gbTHhoB/O4KTa1Gfnw3mFvfxYkV33UgcOW5jp8Lz3U4INAmfAGNmAcMBeM9ts/NsX1ksMBW0DiYvXv0FgaNTgwolbZ1j/GtaenR6mRw8OzJAJFNAjZSqfX62s30BI7b305tP7WwcoOT/OH3kJj/TF1814nh0089tz7jwOnc/SvO3Lsw7VBUQLvGOigOE/QTd1gqE+89Y5yTQxPwS5+24OFopGSNsUZRBIkJSiac0Fy2J9oUhd9JoHBpMgSIV5jrhEFYw3Hx7apoC6GBPkJDBP8I9uN+efHQpfuw8sRzcHjPlgMOnR+hUYZ7f51XDlW+7RXPSaxCupmzDP/BQRT6S1sA7umcdMYDsPl1bJqp4U+0NzT9Ju3r4UefxcpYj8AJ69scGxrQAn5+UwkSYiEUjEscLMsv6jI+VxUheCgVJbKQulKcIxEQ+F5B65TY17H2iCOJcVDZJDmtAlT8IWtG1GXjOs8Vq3xQckBsWJQCB8EO6heXKxZAlJJHbvwFUidVPMSOmJNg8qkJjK1zT1TgjTfVv2BipnBt00h9EFiKm8lYBUVPWRlXcHBZPpZDZE8xR5YMKvlE0qQ4peK7dgTK0zpz+WkCl19hswdDsImVW8owTZkA+6zu71fAqUgfl8s87s05Tqp4yCwC8PSHXn8w6MLa6XCXkdUh2BrpEUvDeO8BZ/gwbaC5nZM5C5/N2JR/GeLs5zp4S5SfnC59hq1MJiVAQUBBwM0RqFbFI8+yKwQqT42TZ7QbgxkO/cMZAezL4D1SrpJ4KddWZoUqdqJEdE+the5IV/Mrv5yIMhsp/LpPedppDS071+Fa5068r+Po0wS0ZUSc4umu3Js3FXAEW5QLDqLcW5hUcEhcCZwKZq9gsupTPGD6SjTJtuhZvOwFDeTke25VJ/hwnr/AqK6nZulmZr0ZqIL96uhkWAktYj5eUhLfWysx4ylP6zzK7ybwC3Pd93UcDahSfg0hIJSNonJk+KtOfMlFluNxuOKRlY2G3yNSDgnXGOT8H6vQ6/VYCYS7xpYVJxd9j3N5ZmPaHr/P8MnV0nOK4rQ149Ooiwq0rWILJgebtzJ8urjku9ZlVthwI05prVO1dAs202q18Ah/1ZbviqWJESezok6HW3sUcKw9KYMDnkGQwjllOdyh48ixigekC1MiNzfXZDI5tCVlgauREGidnJwcNBau0gQIoPz8/GNaN9Xgg3W4S85pXifDkQ+88ieZcw+cDqCFTztX43Od1NRUg8FQPSabXbud1AscatWqhQFYWFgIEjMyMoSQdVJyq5css9kcFETHlAoK6LLHrKysqrKMq7cdDqkNsis4OBj84ziJ7XDFA6ZPTk5es2YNEALfu7dQQD+hgX5+fhjkcGgy7M1KzOKRC+V4eXnVq1fPIZxVBYUaDiSkq3D7pc1dQevMLDvXuZ65oK02618xMIBwSkpKQkLC999/D/BFL5RK6VGPAgHI1nbt2gEZ8NLvv/8eFxcn7Hr3HoP/2dH24Fy6dAnGCsA5fvw42FLhHIEA5gk333xzeno6v13iPxGtTALHKh5QhJbkcwcPZgOVkMKVaVbN5UEzYUNVTt/IVAOl6yxBLsoRHtAmYRmQXvEtsZxY/StsoAQNBFwQsuCyvLw88JiHC1bR4wIESBCj0VhUVASexC9QcgQ/uGKZgEKsTIB4cA7sY8FLrtiWqqVZSGwMpetZs/lPkhyreMD9MNubNGkyZ86c/yTFbRLAUvjoo49CQ0PRosoJQTEGnHMkCNOhxBKbeJ2qxPd1xGkC3Dld4i3RKm8RxAeseLjw8PBo7tyGi6qqIY0aNWrZsuXIkSOrqkB3KqdBgwbgnFGjRrlTo6qqLWFhYY6bJ5SwWKuKYrkciN3KSV65BNfyiH6CNJQ7zEOaX3KuA60jVtjCef/L18Y4kNkE4A610VyLFUGtmN9goiNggcflmuA4gsXAVMApF2EBDsaU48SXY2c89q2SZbF9oFv6wc1orOe0F53I3+bBRRP0LdFyT047uqPFCMGvcI6uzvnLx4IJoACdMiBgSNnv/PQ7mkJAgdME+BXq2R4oR1ft/OXL4DiO1OpTPGhDla+0OA6XSpQsaxqM+Upkd90suPZfg/dbSetU0wrbVbAC+J6G/1XQKBWlgCMDIusbe6Fk75dTepqneqyTalU8ntaFHtJejVRk0zrlnGHzEBCUZioIKAhUHAHXUDyYTMAYkacUtlPKtGeAY038XjdKwje5bZFuPr+qeBc7MCUtrnF3lRU2D7Eiy7Ao4SKY1kMQEJxwlV8cuKchKs7dF4/cq+TwiCjP5BzXUDxlBrDoLCtfct1DSYixsaxt7UmP4NqabSRu9AHgJ/RPnZFuc+j7OjXbzArUbpOm0MTQxdZrBfERQiFqyWyqQCHunIRbhzQ0CR6OCldDwmp054ZXuG0y51AOGS535RzXUDxClfB7QvOZSafW6vj8p8SMB3xcgC+RqpiBPgbJp0gV7nMl4bUjAKRNUDzndbeeku6MbaovdXL62gt04RxcTGBGbiaZqtZYL3TAvjWOmGs0NFUX3yl04SZWknTecBq+dLs67leHXYh7L/jXTHDlooerY2ILmvmV4RxcR0lvXOEmKp7EHT/L4xqKB90jhm7a6cLvD2t6tmctI2gk2DbxxVzHsnOjlF1b1aMdCxKrbp5uZRJEDnIS0xnYhXjN45nmRnyuQxdswPFR5EEGfrHskNBqSAqWm5yw//Qlb7+Q9jEt6HpCLl8hWoSB76DucNpi5dUIAGAFSLKkZ6kDa4mv1RPhHqt+xGwGHx4VnGNMPb3nRLLOUKtD+9ZqsI68v0C8426yzFUUD7eYmDrzWOBra9ijdUjxoMM0WFqDDUWnRvHagmrft9LJ+qoWTVhQIMJonUPMfIR0ECwupkJyCA8kk5SsCkVdEY4VcyqN2pyVpWrcolnD8HAP1ToyUsQ/JBoK9/y+4u3Fa3ILmKnA2GrQpDmzpoT64pooCxSy+Gahu8kPGYIreLi9iHPcVhlrvsg+WsD+CWML7mG19DRwPQ0Qe5yEFOLQSHs3rVj08arUPLMlt6BJn3GPzZ4RFqCzmC1qyDh3dK7SKpqTwukNlgAfyUCWJRxm79AZaqF3mJZ5+TEfX6YjZYq7zrg6oZ0fYWyipZSYr9HhhQbkopDiZPyQgkePA45pBX8wZvC6UvOm9cPDw+znOhXM7jbJwE9w/IUZtufHhcMG3fFrStiDDz88tH3d1x69/eFXP86jW6OQwKK2Tc/dpu0VawhJV/5+gSXpuPTgg+y+V9TL1rDMIow++tCWhzuBwJHNywYMnLLqlPes/3tofL8WC56594n5i3ENolpDlywTgm7nXGfGQ/qDrmnWqlXe9G0atVbD+4OUkFV94usBxOI0wiUtD6cBjyvFSDchMcVB99CH5HhpSMcdlwtkfUEzud+k1tbKKv2L1yBatGgRHh5hr3Uggqu0EhcojAsFehURFxx/uuLPgsh+qZs+0jHWt1+/9s2Cer28ZvTQwRN7NTObLGqtSo2ddQ9DiNQLH3jpx9mcWepVl1T3P2NZdEJtMVqYL8fCQ/WxlbexA4jV2W9X/ZET0DVzxwofBA/s37aBf5env+vWq//M4W3AYDyNC4yFayLRxcaBCupBz/SkLi17jkq7D0kpabb2im+U4olY2ZRWYErD6yXw00k3S2ZmhtFE95/DPFUxKS9Pte+4tPuwdDmdsiMZnwCRX3FXR4Ab+BLu4mzYsKGHax0CChcEYE2XsfNb1mdeTJ/51AvQOnz9l/WY+aD+9MZ/9u1GgJa+V6SiA/8e5mDIiRZbAtmEp6Tt66WJsRIzQpwinBuOHgZIieZi/4+xSzs3X4w/ccczL5DWodVH1m7aA/6XDxza9w/8JJnc0bnKjIdzKWYqBpZ12fLyPNWHWuln8G4q889h85+x3Dsakx0rJ2sMsD7V973Ksuuzn++lTstNU3/+or/UVTNtPAvUmuJ/Uc18WvMHJj5+TJ3E5j3IXrmXvqetHLOpCIeTzODrlSKxvb8i2d0sDZgOBg8adfZCMk4hdW0bAz9tF+PjsaxxpI4VXbiEEKy2UULPm/HI3R1Yh40NJS20GYuPRU70fSmZwur3iHW25EsX8iRT716diQDIIOKciFBDfvq5MwjACi3YC2zmZs51FA8HvnYtdeZalttUNXc+eyAEPWJZ8YX6vnnqBo3ZyBgsidLhN/49TPXpsyxdLzrLolGp81L02VnMS8tyjqhGf6IKHKj6cwYzeJt3/WL65hv9V63Ut/TDMRtxkMTNutiBzRFaB78OrMMFiiaxIKmkIq1G8qYBxY0goru5Th+MM9akeGiu7oGOswad8YE0lTDAzFKhWHrzTDjK4wCzSmXCEmzJQdTSYKhdhOVIUtVuCZWLKZ68AktuALt7umpcX9Ejqm5z2OWDlrU/Q/GovHS2TtJKPv5SgTe4HQ7Wp8UvUGI+Ki+mXv2TxuDFZs2w9GlOce2Gqw/8YLljmWZgFxbqw0/CUbDiroiAGCHyOJE9V8zgIRE4uoX1eh2ttMnOLOFNHo921HzbFBkCVONxm1z/3fvQyljqx8tf9kmN/C65EkH20a7vdx3Fw63qnDzWYIy6U1/0iKXQqFbrWa060gPjpKl/qd9jzKDjMx70ioU191IZfa39o5FYoIoV8m787KCUdYklbmL/W4XX7jXBKo05ksWEMT2V76knj1yfj2uqBbI16qM16IuMGamXGYsUi28qln2i0NzGl5busRlWUxTWbL0YVfI2jxCjHgpEud3AEfH38vFTaVNTsSTrb0tlOmmyBPsGI95d4eLi3NZcp/7LTUeNnuXsZl5ZRKkW9iWn+FIqS9UwfGwEExzM5mlR1KL2VqtDfNFr1ECdv3plhiqft9XHzApMrHGUenR/1rcz696n6IH7cn+cawn0ojFChxEUpyBwLQgIa6V1x3b46Pl3y39HVhWOweJPzsEzkq9vZCS8ZpIfHmrtl1xDwqojoCAwFCd2uiIb1svJl77//DcOC1+kyfwnLtfft15ThODkiltC5TqKh/Mpjq0mb2GPPcmOZqo1Wugiy4ZvpVfeUD0wQoUjBZgPoZ9MWDENKjx2pHDF1+gySrP0Z/bDcnUi7fKyp29jEfns2BHWvRu7cZA0KNKy9XtTBp13c1/rgjdO+XEIApjL4KMQuMihUc/2MVEbPp67+vBZYqXUUw9PuTuoy5iRPXvgEfuHKhxq48aTQ8hwoUKx52USmtiFiHYMqXznz7dF17Ztmm5d8b/v/z1J1RRcfnDSDO9mMWOGEOdIbmoLu85SG+/6bIm17cJ27JYefYLd0IklGNmaNWpNmOWZcdRHuUXMWyOpsKamNQ6KYes/MUx+hbWux374yRJuYKoClmVm0cNYqy/Za69ZzhawDpGW9d/of95gWNZHatGfv+3Da1F+FAQqiAA351V0s5ZGc/u00f/8tmL69OnHbux76djeBevPfLTyvR7Ng6B38PIZHSCm/zzXcctdyoPI0eHtbpsdj7mgzetp0IB5cDeBSuM168G74nb9edd9s/aO6l507uiiP5Ne+eiN4dgAoOOR9EE/9/vElMsoHjFhL6qjeuRDFquzHN/MHlmoigtQP/iA5fUbschGp3xb9LK0qMOCqU3+c+ZLlvbSvPksvKn6lbssA+ZK+zUqk5Exb/Xnyy3j/mTPfqBecpF1GsjOLmYRdWhuRBc8erJg8LRxXxXtJXWChV0NTXqC2w36dtu+5+Y+8Pob78R0GfbTjiOj2tVDJfytZJrteDZz0ZIk3qGr3Ub1TIG6jjfh4abWfEUZixgDdxPg2FOL3ku27nrjfw+98s47Tdv0Xbl577iutM6GnUF3FUkuo3j4lQKWqAbqlg3RI6pm0WzYfTCbYEvScOY9ZOl/k5q4G10pgadVj0yy/N8EtVaNK91IOtTDainxPk7Kq2/sy27sYymScFWuVRzYjt64a0dXdDAo6a4JgeI3mvAeKdbT/Os9s3jNE++ZNBqtkKrybYEez1i0zoj3mXq1Zz3aiqvt6LUmD57wCEbDljSZvCpD3blvrXjwNZMGEotbKHTazcZe7sc8rqF4uOKgmwaxUI7u4noFxgJfvMBMhd9ZAJ5GN/HOpGRcv6gNtA9EWoe6FloH1id29KhHMQjUOiqNLtriegtx7te/grmVX8chQDwjXqrFH1oTUam0WjGs6HZQITsUxuIoWUcoR8OKmeM6xvlLJsEkhA44h971UulsnCPuVXVjjFxD8dBrocS54nAhVySkLcjD3wnnkaR0EEIOnI1I6B67NOhaEUNzJxQIZUPp+ckSPiqoEMUpCFwrAtaVNC5BYKXSP14E5AhYCo/XWqBbprcJWHmMkfYR2HjsuLPO+2TOgZ3CWUdwjhUdt+QGukTKFZyVNYlpBeMWs6+VfPGyAIJJRVEY/VhTiT/WB2uM/GTNr2gdGxDK32tEQDAnt+LBRQpjlQ+fDaUSsWXRKhHt7g9WwcRRKJdzyjCT+yDiGornv/GWu0j2/HceJYWCgIKAgoCCQA0goMjpGgBdqVJBQEFAQcCTEVAUT5X1Pq3MKk5BQEFAQUBB4L8QcPhSm3xLFTz84rv/osj14+Umu35TnL0FQtnLgOMUkIfwWEU6BmggmQyO5wzAioAjYMGvQAm/wlORvG6fRuYZx7XU4YrHdrQUb3bza4gc1xSnKblWrVo67pyGIrclRIwQbMwKNjMYcHGS4qwIiBHn4+Pj5eWFIPGroGOPAMDx8/NDiMI59rAIP2RY2cCqCnGs4oFEMJnw7XD222+/waBAS9zerIANjgFfVFSUk5ODhgMBt29yVfHitZYDrSPPeLKysgoLCzds2ODr61sN9tq1kloj6YFDTExMenr6yZMnExMT4+LiPMf4+0/AAU50dHRaGn3AOCUlZffu3bKJ/J953T5BQUHByJEj8RsQEOCgxjpW8UDTZGdnr1ix4t1338UaCL6X7PZS2Gg0du/e3dsbt2OTysGvg3pOKRYICEkKlQPZ+v7773/yySeYbiqKR/AG2A+fJz948OCZM2fmzJmDX0XxyKMG4DRq1Gjfvn0QSvfeey8UszJUBTgw5mDGnT9//vTp0x07dpQRq1qPYxUPpHBQUNDkyZPHjh0LceD2XYs2wm6CAbV8+fLIyEi0VxGCVcuvpUqDNQNhikUkmPYPPfQQLuiEyi+VxmMfsdiAdaSePXs2b9586dKlmZmZyoKSzAwCnP79+0dERHz55ZcKODIy8EBuY67z448/wqSzD69Cv2MVD8SukLweJQ7QWCEQ0U+i+VXYYUpRMgIwzcQEWgYZVo4cq3gEAoBILCJhLqhgUgoBmXMUcOyRkbcDZXzsY6vE71jFAxLlU0YwMcSKfJXQ7ZyFoJ8wyPPy8jDa5YY7J6nuRBVgh0OLYKApK/Vyz2LEYYoDPhR2K5bsHbpdLNfrEh7AgkU2/MK6B8EKOPa9htGEcSTGlH14FfodrnhkZYN1J7gqJN3ZipL7yb2b6Wywgx57HlO2MeQOKsWQQEkBRwZH9ojRCmTgkRlJjvVAD9hGLCQ4tO3urAnKBU5c2Yhf4Sk3TeUC0WFwlcur5FIQUBBQEPAcBDxO8XCFY6FfUutCtSsXCHsOwystVRBQEKh5BDxL8UDP8LvYseTHv4tgpkupSe0oqqfmWdEdKLAZMvhLDk0Sv+7Qtsq1QYysYjBo85OebODwhQf+U7nyXTlXMQg2NLDnxLGxco4bg+PwPR6nYgws46Iv6Yyz+FQpTp0xOuTtoYzvVH3j4sRwIUL7BfCo8GYrv6cDfgryZCdeoBa40MezrBcaYggKcPALjICQB8JEkodzCIFgNuMD6oJT8F2e4nA3BcezuhvdSZMblcqcn5517GhOUgb/HikP9GTpoLT9+hAQYoIrHbqrAlqniNsyQrJcX9munduKzP+3d/bBUZR3HN/de79cEpIg5BXBQYjyIlSEMhQorUZQtIKonQCFqS+AI5WKxbYU7bSSVoraZmAUSivagZmmVWf4Q/ClWoq0ooBapyYqMYiKSSAkR5LLvV9/z/PcHRcQyGazkN39PjCX5/ae394+n+e3v+/zPPvsHv8VZ3bxXpI6OgNdEbbAlcFJcGGm4Mu7/MauqvqjT6kL6/sK1Wk53tIVDJM8czj8xkcRstTvvJ9bGHvEw4YvTElIPtmJrkg0dhG/h30W7OT53MXlz/aeeOyv0thFvh9fe5ai2AwCPSXAwgTFVgoX7AHl4dZX3zwZzC6dOcHmwAPL2RCQ+BCIk411r+/cebTDHgx1XDbx5hunjXLI7BdaGT1LjnjIVwQcyrQ01j+/4x+fHzmc43MPn3LrDVNHuah/HI/TYrueeqGhyhmvVtRUlESbsXajLOsxMelJ/mMbU4k3LS/CtlBJdk8nLcC1OWWPS8YzJQ3lrP32YMm1xCxSPHyiuWp5XcV3v/rVn2Mh9pRC8RPr/fbI9T4wFjb57FrrkYOLvzdlzg9X1X/26d4dz86ZPrpq+176dvqQZt2SdwLrfTT9bP8MDl8Ge7x+X+XU4UuWLOkMBA6+VjN32uhf/ul1Olgb6yez1M8OvA8Ox3gjHuofUb2pMURGMKDmY40Yo7n1zD7mqalScvDU9DK3cDokm13iHdQ+oIhdgAAF0JOHmx5a+sXTL2d985pQVi4FU8tTYTGTTjxJilSvf/TF/cE9//v0W1eWRQKrq++a9mB19fhrrrr5cl8kGnNQx55osTPbQonBsVGd41v/WP3KIfmN/3767TFDE9H2yXdOuf+pDeMmjr9jTB5d/DHloMdwTc1c+TTVIY/1Swm6/zitOrGk97LJD1E4rTriIznLZ3PYLd4btdAprn9VKbjGAp326TeWvLireNlsmZwrknJD/b+9v36DItFohp4o0bC/ran1thVVpDp0qA7vwJXrNyr73jjwxm56S9Nt9EpT5JZLtAhFkqINb3/R3Dp51VOkOkRAtmf/6PENnvf2vfPK6+ytSW8NNFxrsw6CEAz/np1fVq86UbP1fUX+2On11x2ndjp8c8V+WX7PoRy8dNzR53dTu4qBUWegsW786IP0kSK/P21+w++fC/hbFZeOPzhBB4NkKQJK/rC82+8rvuV6uy2WCEYsVfezVDZ5R/VHdbXtJ6OzZ99OxRKxKJteKp6R7Wo7Ul9L2eS8A1MfayVaUksV/ujQxx2N/gUVsymfhDNwms/jb277kraQdPPZOLORMd5Um5gVpnZwDfA2b9vUWJtwzb1z2K3X+YblNsyd0dbQNajqifzyIcdfqjm67idSYn3xvGmxY5/UT6846XcNfexx35DCYPNnJzat7/g8P3tWltnaE/W5GATY5XFaou/0sn5c3B8PhFi4oF4PEo+tXV1dndGInf+4C01CCCpXeR15QfbwY5qNMGVsPX/j82p3dYWDwVBOFotFfFqS2Y3zKbltLWyLSeEYUHhYu7DkGpjnLBztHDDuir9vpAXw0Y9fbXqxdti/3ho8dSh96p5za2TBzKbVPxs0b2/wny901bYN//ebhZNHMUtKjR+c3LxPwlyIoIFXbQTEdC5Nx0sKm7OnZbAUMbTt0lTW7PoqDQOd3X6DuDAm+ejCGCdlUeHhjUwDvoTTFnZ1C8WFMSWrK0irU+xs9YUJvalbbY3l7PHWY9KQkdlTl3N3jgbe3eOw2zr/9ofG9/L89Y3O0cNCHx6OhBzt4bbYJ7WeZSsGcdWJBbtsbk/+4sUdXynRjoCxqoyj7Z8EUutcZNGZt2oH/szGSaqv1+l1R6X25mOSRNd4xMbQh9FAgddLb2g+jr2eaW36LZyEx+31JpT2JrpS4KMhD690tDYStvvyWHSmobPYZi4aRq4TuasciUfYL0xTz4C9RNvllqOJ9jZXoM3x0eGcG24r3bzFQ91RGtG7naxAKikut83jpEVvqQ34CwJ9R4ACCo8pfbdHg+6J5hsZiLKigkg8+vLeg5SnyxisMrFDH0Zs7uJilhWaY+RQxGqkPlH8IqOSwfn0d9ee/WwHAo5U/0HYYS9kcOImFWRjt3YiRivcklWwl5VHpCLv/DVFP39i6Obtpb/bWLxyaU755W53vnvkqOCzW1vfbaCGpOEOvbZs29684z+ObPxaJcFA6msCdEc+uSYSu0LBYmvu+IkFJYN3bHzST4psZyt6nn74N/GCcVOuHkv5BC/DllNbLfFZtJyx37hkaMmujU+2EAEbg7NlTVUof+SMyexnp9lzVsyIxYBTbakuQCIelWJhKcE7UJLknTy3YNzq+kfuDR5dcsmIonDd7i+3vqRMuOOK6gezrl3gSFTV//S+yA8WeHy+WPjzjh0vyM1uvrzGjK2KOl1EAnSpJx6To9S1v4gH0V++mh4Fw2+/H7zwtu/v/Mv8e5Y8dNft1504tH9Z1babHnpy5uQr6ECpDE0oxek5Mf3lqC/QcaTgDFowb/7zm+bedefypZW3dBx59+5Hn5v/8DPzptMF6YRNoQXVJM1mUx8DCg/NBotWyBngHTsyVspHLWwRkfvyt9/xrFvXeP/dzYEuOacke+XakpWLyInkwUMv++Atf9V9Xy2sDNOU6awFQ1at9bUEQrnuC+Ri+BrrEFDszmGjBkyVZKcIpGYLGapakua5qf7UV7z6psqalyIPPLCqYvM62kPlirUbHrmfFnKxxxbQA3Xi7DKP1VIazpXXzqnZ9cy9y++ZuXUDQVj28Ibf/mIxjX1IcvhCFROCMaLwUDMwL7VdOvGSpeMlhQ1OaTErDUkVx8DS1esGr/x1LBKxO112cZsO7y/klozN3bi7cH0oHg07snLY2iM+f0qnBGUtOMpn9UfqawLkSIqSnVuxMPs7kmLnTmbBgJpBla+dZmvNqc9+1axFr1VUdtIdTomEz0eiwyIrnbXJ54tkWFkk2w3O9Yv31lZ2hmh8GMv2+TibJBxGI7UG3TRkjCc8YgVRPE5dATmtOuxGCkoJWbHJDreL/osWSgoS61qwJZ1ODw1xTo1y2J1r4iGFpmlPVOTiERBOyGOEovATi/wuteDt4h3WRf1mvnyHPwZUnII2hy+L9RQpse48nbAy7/VROdPFVlHNc7yeAceZ5U0W52xofk10icXQ+Rx7Mt5HxhMeOpkJc/Ik58DZkIUtOkyKz2mNQHOkbP0Bu96bujqUWSK1I4sHiEwkyPeOAPNMip7CQfkuhHNZ2bUEASHA9NrtFKSHycvsgXaW5XMuOBTiKHCZF47xhCftpukMnePJLoEYu6amzpjciPOfl6B85hbxln2euSNeHi8g0AsCST/q7k7d3/Vir4Y3SRNIZ1ICzU5P2ijeGr6evapAmkk6k7kbEw8CjSc8mQ1zZl4oUFKH0oLEy53jozP3gy0gAAI6ERDxNB1V0xmdvg677YcE0iG6Hx4bDgkEQAAE9CVAV1P0/QLs/esIXLgRj/mWop/GM7OCXztwPq083oLABSBArghvPBtnwDkbGb236y48okMR48n0JwBdPnU6nRGe9G457D+TgLiiT16WKf+ZBSyYj0ajdrvd7/e3t7dT9QkRbbEgh6+tMrmKzWYjOFn8sdBEBp6TBkWuQnDoVT8m+goPqQ4FYqoPVYNSumLmzpSVleXk5OjXZuam14vakdC73WyVvHjtxR5MaUKqQ/UaM2bMiBEjKOPx4AFRp9o5DaeoqAhwTnHJyJHD6NdT0Vd4RCBoa2trbW2lLkZGpUybJaFtaWkJBAJCaEl+MImsX2OLMXR+fj75WDgcbmhosE7/pidUCwoKJk2alJeXR3yOHTuGzlAmNIIzYcIEr9dL456mpibTz8dk1v28+dLSUhooF/OnuJ63cG8KUFjUI1EnlHZ74MABa/azysvLa2pqOjs7CQIprh6EsU/qjhGEjo6ONWvWWNPNenPCwwYEekxgy5YtdIqFQqE+jzZ69cdpfhA9CNG+QNFjP1dXEGDV8UJpEOgVAT1ONL2WU5Pq0OH2qpqmMtKjzUwFSENl4GMa4MEUBHpEQKcIptc1HnG40B4RHDH465GPqywEH1MJDMVBQDUBnSKYXlNtqusHAxAAARAAAWsQ0GuqzRr0UEsQAAEQAAHVBCA8qpHBAARAAARAQAsBCI8WerAFARAAARBQTQDCoxoZDEAABEAABLQQgPBooQdbEAABEAAB1QQgPKqRwQAEQAAEQEALAQiPFnqwBQEQAAEQUE0AwqMaGQxAAARAAAS0EIDwaKEHWxAAARAAAdUEIDyqkcEABEAABEBACwEIjxZ6sAUBEAABEFBNAMKjGhkMQAAEQAAEtBCA8GihB1sQAAEQAAHVBCA8qpHBAARAAARAQAsBCI8WerAFARAAARBQTQDCoxoZDEAABEAABLQQgPBooQdbEAABEAAB1QQgPKqRwQAEQAAEQEALAQiPFnqwBQEQAAEQUE0AwqMaGQxAAARAAAS0EIDwaKEHWxAAARAAAdUEIDyqkcEABEAABEBACwEIjxZ6sAUBEAABEFBNAMKjGhkMQAAEQAAEtBCA8GihB1sQAAEQAAHVBCA8qpHBAARAAARAQAsBCI8WerAFARAAARBQTQDCoxoZDEAABEAABLQQgPBooQdbEAABEAAB1QT+D/d726U5CDkmAAAAAElFTkSuQmCC)Heart Disease"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BSUydwwtnL4"
      },
      "source": [
        "## One hot encoding with Pandas (get_dummies)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 295,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze4Fjo05uu4g",
        "outputId": "0a4a5857-65a2-45ee-8764-6a0a2d5848ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'individualperf', 'gender', 'age', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 295
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ki0mQ2asaR0a",
        "outputId": "6910c881-6ba2-4d7a-b268-0b3d49425236"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  predictor1 predictor2 predictor3 predictor4 ctar individualperf   age  \\\n",
            "0        64%        33%        33%        42%   0%            74%  34.0   \n",
            "1        63%        70%        25%        33%   8%            70%  42.0   \n",
            "2        72%        60%        25%        33%   8%            78%  33.0   \n",
            "3        76%       -17%       -17%       -33%  -8%            40%  23.0   \n",
            "4        65%        17%         0%         8%   8%            60%  19.0   \n",
            "\n",
            "   tenure  gender_M  \n",
            "0       3         0  \n",
            "1      16         1  \n",
            "2       0         1  \n",
            "3       0         1  \n",
            "4       0         0  \n"
          ]
        }
      ],
      "source": [
        "# Create dummy variables for categorical columns\n",
        "categorical_columns = [ 'gender']  # List of categorical columns\n",
        "data_dummies = pd.get_dummies(df, columns=categorical_columns, drop_first=True)\n",
        "\n",
        "# Print the first few rows of the dataset with dummy variables\n",
        "print(data_dummies.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 297,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SKnNaWwsb41u",
        "outputId": "e5df36ad-fce3-43f0-abda-deee8b570917"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     predictor1 predictor2 predictor3 predictor4  ctar individualperf   age  \\\n",
              "0           64%        33%        33%        42%    0%            74%  34.0   \n",
              "1           63%        70%        25%        33%    8%            70%  42.0   \n",
              "2           72%        60%        25%        33%    8%            78%  33.0   \n",
              "3           76%       -17%       -17%       -33%   -8%            40%  23.0   \n",
              "4           65%        17%         0%         8%    8%            60%  19.0   \n",
              "...         ...        ...        ...        ...   ...            ...   ...   \n",
              "3341        50%       -13%        -8%       -17%   50%            50%  30.0   \n",
              "3342        63%       -30%       -17%        -8%   17%            40%  37.0   \n",
              "3343        35%        -3%       -17%       -17%    0%            40%  31.0   \n",
              "3344        52%       -20%       -42%       -25%  -58%            40%  27.0   \n",
              "3345        25%       -17%       -17%       -17%  -25%            40%  29.0   \n",
              "\n",
              "      tenure  gender_M  \n",
              "0          3         0  \n",
              "1         16         1  \n",
              "2          0         1  \n",
              "3          0         1  \n",
              "4          0         0  \n",
              "...      ...       ...  \n",
              "3341       3         0  \n",
              "3342      10         0  \n",
              "3343      13         1  \n",
              "3344       2         0  \n",
              "3345       7         0  \n",
              "\n",
              "[1690 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d8c4fd9-7ea7-402f-9535-8cf253b535bc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>gender_M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64%</td>\n",
              "      <td>33%</td>\n",
              "      <td>33%</td>\n",
              "      <td>42%</td>\n",
              "      <td>0%</td>\n",
              "      <td>74%</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63%</td>\n",
              "      <td>70%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>70%</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>72%</td>\n",
              "      <td>60%</td>\n",
              "      <td>25%</td>\n",
              "      <td>33%</td>\n",
              "      <td>8%</td>\n",
              "      <td>78%</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>76%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>40%</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8%</td>\n",
              "      <td>8%</td>\n",
              "      <td>60%</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>50%</td>\n",
              "      <td>-13%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>50%</td>\n",
              "      <td>50%</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3342</th>\n",
              "      <td>63%</td>\n",
              "      <td>-30%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>17%</td>\n",
              "      <td>40%</td>\n",
              "      <td>37.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3343</th>\n",
              "      <td>35%</td>\n",
              "      <td>-3%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>40%</td>\n",
              "      <td>31.0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>52%</td>\n",
              "      <td>-20%</td>\n",
              "      <td>-42%</td>\n",
              "      <td>-25%</td>\n",
              "      <td>-58%</td>\n",
              "      <td>40%</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3345</th>\n",
              "      <td>25%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>-25%</td>\n",
              "      <td>40%</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1690 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert:hover {\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        const element = document.querySelector('#df-3d8c4fd9-7ea7-402f-9535-8cf253b535bc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 297
        }
      ],
      "source": [
        "data_dummies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MBmtfPEcv31"
      },
      "source": [
        "## Deal with Percentage Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 298,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-vDdhuWcvie",
        "outputId": "79da0fdd-c698-4010-a958-38b3461e9b13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   predictor1  predictor2  predictor3  predictor4  ctar  individualperf   age  \\\n",
            "0        0.64        0.33        0.33        0.42  0.00            0.74  34.0   \n",
            "1        0.63        0.70        0.25        0.33  0.08            0.70  42.0   \n",
            "2        0.72        0.60        0.25        0.33  0.08            0.78  33.0   \n",
            "3        0.76       -0.17       -0.17       -0.33 -0.08            0.40  23.0   \n",
            "4        0.65        0.17        0.00        0.08  0.08            0.60  19.0   \n",
            "\n",
            "   tenure  gender_M  \n",
            "0       3         0  \n",
            "1      16         1  \n",
            "2       0         1  \n",
            "3       0         1  \n",
            "4       0         0  \n"
          ]
        }
      ],
      "source": [
        "# List of columns containing percentage values\n",
        "percentage_columns = ['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar','individualperf']\n",
        "\n",
        "# Loop through the percentage columns and convert to numerical values\n",
        "for col in percentage_columns:\n",
        "    # Convert the column to string type (if it's not already)\n",
        "    data_dummies[col] = data_dummies[col].astype(str)\n",
        "\n",
        "    # Remove the '%' symbol and convert to float\n",
        "    data_dummies[col] = data_dummies[col].str.rstrip('%').astype(float) / 100.0\n",
        "\n",
        "# Print the first few rows of the updated dataset\n",
        "print(data_dummies.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 299,
      "metadata": {
        "id": "Nc_PZqkou8ub"
      },
      "outputs": [],
      "source": [
        "df_dummy = data_dummies.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 300,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "O6MYXufhvAli",
        "outputId": "508b53f2-728f-47df-b66f-9921bd70b329"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
              "0           0.64        0.33        0.33        0.42  0.00            0.74   \n",
              "1           0.63        0.70        0.25        0.33  0.08            0.70   \n",
              "2           0.72        0.60        0.25        0.33  0.08            0.78   \n",
              "3           0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
              "4           0.65        0.17        0.00        0.08  0.08            0.60   \n",
              "...          ...         ...         ...         ...   ...             ...   \n",
              "3341        0.50       -0.13       -0.08       -0.17  0.50            0.50   \n",
              "3342        0.63       -0.30       -0.17       -0.08  0.17            0.40   \n",
              "3343        0.35       -0.03       -0.17       -0.17  0.00            0.40   \n",
              "3344        0.52       -0.20       -0.42       -0.25 -0.58            0.40   \n",
              "3345        0.25       -0.17       -0.17       -0.17 -0.25            0.40   \n",
              "\n",
              "       age  tenure  gender_M  \n",
              "0     34.0       3         0  \n",
              "1     42.0      16         1  \n",
              "2     33.0       0         1  \n",
              "3     23.0       0         1  \n",
              "4     19.0       0         0  \n",
              "...    ...     ...       ...  \n",
              "3341  30.0       3         0  \n",
              "3342  37.0      10         0  \n",
              "3343  31.0      13         1  \n",
              "3344  27.0       2         0  \n",
              "3345  29.0       7         0  \n",
              "\n",
              "[1690 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>gender_M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.74</td>\n",
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              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.70</td>\n",
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              "      <td>0.33</td>\n",
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              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.76</td>\n",
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              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>31.0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>0.52</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>0.40</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3345</th>\n",
              "      <td>0.25</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.40</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1690 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8bd219bc-46b5-4e47-b30c-4c2ff69b2390')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8bd219bc-46b5-4e47-b30c-4c2ff69b2390 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8bd219bc-46b5-4e47-b30c-4c2ff69b2390');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1eb79eee-ae55-42e3-a14f-91b86f5b0e82\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1eb79eee-ae55-42e3-a14f-91b86f5b0e82')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1eb79eee-ae55-42e3-a14f-91b86f5b0e82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ],
      "source": [
        "df_dummy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "metadata": {
        "id": "faOfOcvO1BD3"
      },
      "outputs": [],
      "source": [
        "# making a normalisation function\n",
        "\n",
        "def normalize(col):\n",
        "    return ((col-np.min(col)) / (np.max(col) - np.min(col)))\n",
        "\n",
        "\n",
        "# applying normalize() fucntion to all columns using apply function\n",
        "\n",
        "df_dummy = df_dummy.apply(normalize)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZUHqaGa8ud6"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 301,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "id": "lmVdiPci8Uwg",
        "outputId": "9355145e-b60f-47a5-c4a8-fb6a46afb54d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "#df_cor = df_l.iloc[:,5:]\n",
        "df_cor = df_dummy\n",
        "corrmat = df_cor.corr()\n",
        "\n",
        "plt.figure(figsize=(20, 12))\n",
        "p =sns.heatmap(corrmat, annot=True, cmap=sns.diverging_palette(120, 20, as_cmap=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wNsCMkYJs5ZJ"
      },
      "source": [
        "# 4 | Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "id": "12t9gYL_Bz2e"
      },
      "outputs": [],
      "source": [
        "def Reg_Models_Evaluation_Metrics (model,X_train,y_train,X_test,y_test,y_pred):\n",
        "    cv_score = cross_val_score(estimator = model, X = X_train, y = y_train, cv = 10)\n",
        "\n",
        "    # Calculating Adjusted R-squared\n",
        "    r2 = model.score(X_test, y_test)\n",
        "    # Number of observations is the shape along axis 0\n",
        "    n = X_test.shape[0]\n",
        "    # Number of features (predictors, p) is the shape along axis 1\n",
        "    p = X_test.shape[1]\n",
        "    # Adjusted R-squared formula\n",
        "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
        "    RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "    R2 = model.score(X_test, y_test)\n",
        "    CV_R2 = cv_score.mean()\n",
        "\n",
        "    return R2, adjusted_r2, CV_R2, RMSE\n",
        "\n",
        "    print('RMSE:', round(RMSE,4))\n",
        "    print('R2:', round(R2,4))\n",
        "    print('Adjusted R2:', round(adjusted_r2, 4) )\n",
        "    print(\"Cross Validated R2: \", round(cv_score.mean(),4) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NBYvs4vzD82a",
        "outputId": "29b85d80-db16-468e-d17a-46b0271f5907"
      },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
              "0           0.64        0.33        0.33        0.42  0.00            0.74   \n",
              "1           0.63        0.70        0.25        0.33  0.08            0.70   \n",
              "2           0.72        0.60        0.25        0.33  0.08            0.78   \n",
              "3           0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
              "4           0.65        0.17        0.00        0.08  0.08            0.60   \n",
              "...          ...         ...         ...         ...   ...             ...   \n",
              "3341        0.50       -0.13       -0.08       -0.17  0.50            0.50   \n",
              "3342        0.63       -0.30       -0.17       -0.08  0.17            0.40   \n",
              "3343        0.35       -0.03       -0.17       -0.17  0.00            0.40   \n",
              "3344        0.52       -0.20       -0.42       -0.25 -0.58            0.40   \n",
              "3345        0.25       -0.17       -0.17       -0.17 -0.25            0.40   \n",
              "\n",
              "       age  tenure  gender_M  \n",
              "0     34.0       3         0  \n",
              "1     42.0      16         1  \n",
              "2     33.0       0         1  \n",
              "3     23.0       0         1  \n",
              "4     19.0       0         0  \n",
              "...    ...     ...       ...  \n",
              "3341  30.0       3         0  \n",
              "3342  37.0      10         0  \n",
              "3343  31.0      13         1  \n",
              "3344  27.0       2         0  \n",
              "3345  29.0       7         0  \n",
              "\n",
              "[1690 rows x 9 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.74</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.70</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
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              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.60</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3342</th>\n",
              "      <td>0.63</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.40</td>\n",
              "      <td>37.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3343</th>\n",
              "      <td>0.35</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.40</td>\n",
              "      <td>31.0</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>0.52</td>\n",
              "      <td>-0.20</td>\n",
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              "      <td>-0.25</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>0.40</td>\n",
              "      <td>27.0</td>\n",
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              "    <tr>\n",
              "      <th>3345</th>\n",
              "      <td>0.25</td>\n",
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              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.40</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1690 rows × 9 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ],
      "source": [
        "df_dummy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {
        "id": "PH32Aei66E2g"
      },
      "outputs": [],
      "source": [
        "# Remove rows with null and NaN values\n",
        "df_dummy = df_dummy.dropna()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 305,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZdxiQsD6RVL",
        "outputId": "a52c6ebe-d7b4-4f81-c98d-e5d7b72f749a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1690, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ],
      "source": [
        "df_dummy.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTqerdeFTWx6"
      },
      "source": [
        "## Outlier Detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIXIV4S0UNJA"
      },
      "source": [
        "## Remove the records which are above the upper bound value and records below the lower bound value!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 306,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gKi-9lAVl0Q",
        "outputId": "bdbff4e9-d744-4db5-f93c-7b6e535ec441"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "      predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
            "0           0.64        0.33        0.33        0.42  0.00            0.74   \n",
            "1           0.63        0.70        0.25        0.33  0.08            0.70   \n",
            "2           0.72        0.60        0.25        0.33  0.08            0.78   \n",
            "3           0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
            "4           0.65        0.17        0.00        0.08  0.08            0.60   \n",
            "...          ...         ...         ...         ...   ...             ...   \n",
            "3341        0.50       -0.13       -0.08       -0.17  0.50            0.50   \n",
            "3342        0.63       -0.30       -0.17       -0.08  0.17            0.40   \n",
            "3343        0.35       -0.03       -0.17       -0.17  0.00            0.40   \n",
            "3344        0.52       -0.20       -0.42       -0.25 -0.58            0.40   \n",
            "3345        0.25       -0.17       -0.17       -0.17 -0.25            0.40   \n",
            "\n",
            "       age  tenure  gender_M  \n",
            "0     34.0       3         0  \n",
            "1     42.0      16         1  \n",
            "2     33.0       0         1  \n",
            "3     23.0       0         1  \n",
            "4     19.0       0         0  \n",
            "...    ...     ...       ...  \n",
            "3341  30.0       3         0  \n",
            "3342  37.0      10         0  \n",
            "3343  31.0      13         1  \n",
            "3344  27.0       2         0  \n",
            "3345  29.0       7         0  \n",
            "\n",
            "[1690 rows x 9 columns]\n",
            "\n",
            "Cleaned DataFrame (outliers removed):\n",
            "      predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
            "0           0.64        0.33        0.33        0.42  0.00            0.74   \n",
            "1           0.63        0.70        0.25        0.33  0.08            0.70   \n",
            "2           0.72        0.60        0.25        0.33  0.08            0.78   \n",
            "3           0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
            "4           0.65        0.17        0.00        0.08  0.08            0.60   \n",
            "...          ...         ...         ...         ...   ...             ...   \n",
            "3340        0.60       -0.13        0.08        0.08 -0.17            0.50   \n",
            "3341        0.50       -0.13       -0.08       -0.17  0.50            0.50   \n",
            "3342        0.63       -0.30       -0.17       -0.08  0.17            0.40   \n",
            "3343        0.35       -0.03       -0.17       -0.17  0.00            0.40   \n",
            "3344        0.52       -0.20       -0.42       -0.25 -0.58            0.40   \n",
            "\n",
            "       age  tenure  gender_M  \n",
            "0     34.0       3         0  \n",
            "1     42.0      16         1  \n",
            "2     33.0       0         1  \n",
            "3     23.0       0         1  \n",
            "4     19.0       0         0  \n",
            "...    ...     ...       ...  \n",
            "3340  40.0      14         0  \n",
            "3341  30.0       3         0  \n",
            "3342  37.0      10         0  \n",
            "3343  31.0      13         1  \n",
            "3344  27.0       2         0  \n",
            "\n",
            "[1569 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Calculate Z-scores for each column\n",
        "z_scores = np.abs(stats.zscore(df_dummy))\n",
        "\n",
        "# Define a threshold for Z-score above which a data point is considered an outlier\n",
        "z_threshold = 3\n",
        "\n",
        "# Identify rows with outlier values\n",
        "outliers = np.where(z_scores > z_threshold)\n",
        "\n",
        "\n",
        "\n",
        "# Remove rows containing outliers\n",
        "df_cleaned = df_dummy[(z_scores < z_threshold).all(axis=1)]\n",
        "\n",
        "print(\"Original DataFrame:\")\n",
        "print(df_dummy)\n",
        "\n",
        "print(\"\\nCleaned DataFrame (outliers removed):\")\n",
        "print(df_cleaned)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y9pXQ4Sb5vj4"
      },
      "source": [
        "## Train and Test Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BBvGSprgZCNy",
        "outputId": "633a2fdd-55b2-4824-ce07-6aa249bcae68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
              "0           0.64        0.33        0.33        0.42  0.00            0.74   \n",
              "1           0.63        0.70        0.25        0.33  0.08            0.70   \n",
              "2           0.72        0.60        0.25        0.33  0.08            0.78   \n",
              "3           0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
              "4           0.65        0.17        0.00        0.08  0.08            0.60   \n",
              "...          ...         ...         ...         ...   ...             ...   \n",
              "3340        0.60       -0.13        0.08        0.08 -0.17            0.50   \n",
              "3341        0.50       -0.13       -0.08       -0.17  0.50            0.50   \n",
              "3342        0.63       -0.30       -0.17       -0.08  0.17            0.40   \n",
              "3343        0.35       -0.03       -0.17       -0.17  0.00            0.40   \n",
              "3344        0.52       -0.20       -0.42       -0.25 -0.58            0.40   \n",
              "\n",
              "       age  tenure  gender_M  \n",
              "0     34.0       3         0  \n",
              "1     42.0      16         1  \n",
              "2     33.0       0         1  \n",
              "3     23.0       0         1  \n",
              "4     19.0       0         0  \n",
              "...    ...     ...       ...  \n",
              "3340  40.0      14         0  \n",
              "3341  30.0       3         0  \n",
              "3342  37.0      10         0  \n",
              "3343  31.0      13         1  \n",
              "3344  27.0       2         0  \n",
              "\n",
              "[1569 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62fcaf61-eb1d-4c8c-a261-bc8c6bfbfcf2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>gender_M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.74</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.70</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.60</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3340</th>\n",
              "      <td>0.60</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>40.0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3342</th>\n",
              "      <td>0.63</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.40</td>\n",
              "      <td>37.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3343</th>\n",
              "      <td>0.35</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.40</td>\n",
              "      <td>31.0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>0.52</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>0.40</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1569 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-62fcaf61-eb1d-4c8c-a261-bc8c6bfbfcf2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-62fcaf61-eb1d-4c8c-a261-bc8c6bfbfcf2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "  </button>\n",
              "\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26e04782-4b25-459e-b04f-f378313ad80f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ],
      "source": [
        "df_cleaned"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 222,
      "metadata": {
        "id": "GnnsrE_vsu4H"
      },
      "outputs": [],
      "source": [
        "#df_cleaned = df_cleaned.sample(frac=0.7, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "id": "W5Fnn8dY4rUr"
      },
      "outputs": [],
      "source": [
        "# Split the dataset into features and target\n",
        "#X = df_cleaned[['predictor1','predictor2', 'predictor3', 'predictor4']]  # Features\n",
        "#y = df_cleaned['individualperf']  # Target\n",
        "\n",
        "X = df_cleaned.drop('individualperf', axis=1)\n",
        "y = df_cleaned['individualperf']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PURBuPtPGFCw",
        "outputId": "70fd2fca-dec5-4251-f54c-42cee70983a3"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4  ctar   age  tenure  \\\n",
              "0           0.64        0.33        0.33        0.42  0.00  34.0       3   \n",
              "1           0.63        0.70        0.25        0.33  0.08  42.0      16   \n",
              "2           0.72        0.60        0.25        0.33  0.08  33.0       0   \n",
              "3           0.76       -0.17       -0.17       -0.33 -0.08  23.0       0   \n",
              "4           0.65        0.17        0.00        0.08  0.08  19.0       0   \n",
              "...          ...         ...         ...         ...   ...   ...     ...   \n",
              "3340        0.60       -0.13        0.08        0.08 -0.17  40.0      14   \n",
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              "3342        0.63       -0.30       -0.17       -0.08  0.17  37.0      10   \n",
              "3343        0.35       -0.03       -0.17       -0.17  0.00  31.0      13   \n",
              "3344        0.52       -0.20       -0.42       -0.25 -0.58  27.0       2   \n",
              "\n",
              "      gender_M  \n",
              "0            0  \n",
              "1            1  \n",
              "2            1  \n",
              "3            1  \n",
              "4            0  \n",
              "...        ...  \n",
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              "\n",
              "[1569 rows x 8 columns]"
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              "      <th>gender_M</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0.63</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.25</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>3341</th>\n",
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              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3342</th>\n",
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              "      <td>-0.30</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>37.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.52</td>\n",
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              "      <td>-0.58</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1569 rows × 8 columns</p>\n",
              "</div>\n",
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              "\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6765f67-e5a4-4e60-ab0c-0ed3b707b681 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_ypRWSfGH40",
        "outputId": "a6a54bd3-51d6-4bd6-903c-158d5df9d233"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0.74\n",
              "1       0.70\n",
              "2       0.78\n",
              "3       0.40\n",
              "4       0.60\n",
              "        ... \n",
              "3340    0.50\n",
              "3341    0.50\n",
              "3342    0.40\n",
              "3343    0.40\n",
              "3344    0.40\n",
              "Name: individualperf, Length: 1569, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 311,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bw3JSPD5BQEU",
        "outputId": "33bf406a-3b10-459d-a031-2e37463b4873"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1098, 8) (1098,)\n",
            "(471, 8) (471,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "print(X_train.shape, y_train.shape)\n",
        "print( X_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 312,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMkhG5VCR3nG",
        "outputId": "7ea5a44a-1fb3-457b-c113-6d277b97bd41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept:  0.3407422281767361\n",
            "coef:  [0.17417752 0.29442147 0.0933845  0.06610129 0.02841458 0.00133521\n",
            " 0.00507893 0.03088686]\n",
            "RScore.  0.5174251513408232\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)\n",
        "lin_reg = LinearRegression()\n",
        "model = lin_reg.fit(X_train,y_train)\n",
        "predictions = lin_reg.predict(X_test)\n",
        "\n",
        "print(\"intercept: \", model.intercept_)\n",
        "print(\"coef: \", model.coef_)\n",
        "print(\"RScore. \", model.score(X_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 313,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "9xr8I-yOR_Hy",
        "outputId": "4a129017-1667-4f66-ca99-6efbd8fbd8a5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.scatter(y_test,predictions)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 314,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPgT_zFkSGRe",
        "outputId": "995608bd-5417-4c26-a30f-ae244d0cd2e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.07816539828492995\n",
            "MSE: 0.011426482971989137\n",
            "RMSE: 0.10689472845743675\n"
          ]
        }
      ],
      "source": [
        "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 315,
      "metadata": {
        "id": "h1OvmETjBZY3"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Creating and training model\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train, y_train)\n",
        "\n",
        "# Model making a prediction on test data\n",
        "y_pred = lm.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 316,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ft0BzzKOBmhO",
        "outputId": "0fe69972-d66b-45ef-9912-239e33e9e7c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Linear Regression  0.517425           0.509069                  0.492659   \n",
              "\n",
              "       RMSE  \n",
              "0  0.106895  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45e7a9ac-d468-4ba1-98d0-33cc2a7e0106\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.517425</td>\n",
              "      <td>0.509069</td>\n",
              "      <td>0.492659</td>\n",
              "      <td>0.106895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45e7a9ac-d468-4ba1-98d0-33cc2a7e0106')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-45e7a9ac-d468-4ba1-98d0-33cc2a7e0106 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-45e7a9ac-d468-4ba1-98d0-33cc2a7e0106');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 316
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(lm,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "lm_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "lm_score.insert(0, 'Model', 'Linear Regression')\n",
        "lm_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 317,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "ysFFiVXEBoit",
        "outputId": "cf5219f0-b644-4636-c984-ffa3f3503adb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear regression for RGB dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 317
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (10,5))\n",
        "sns.regplot(x=y_test,y=y_pred)\n",
        "plt.title('Linear regression for RGB dataset', fontsize = 20)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVoCoTe5CXdD"
      },
      "source": [
        "##  Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 318,
      "metadata": {
        "id": "Qbecs1BMB86G"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Creating and training model\n",
        "RandomForest_reg = RandomForestRegressor(n_estimators = 10, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 319,
      "metadata": {
        "id": "IankzW1LCZyD"
      },
      "outputs": [],
      "source": [
        "RandomForest_reg.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = RandomForest_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 320,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "A2Q6wyo4Cb4t",
        "outputId": "16aca397-d0d1-4f30-ca96-14e921e8623e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Random Forest  0.859504           0.857071                   0.81416   \n",
              "\n",
              "       RMSE  \n",
              "0  0.057677  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30d92fda-77ae-4641-a5df-89133ad48e4a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.859504</td>\n",
              "      <td>0.857071</td>\n",
              "      <td>0.81416</td>\n",
              "      <td>0.057677</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30d92fda-77ae-4641-a5df-89133ad48e4a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30d92fda-77ae-4641-a5df-89133ad48e4a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-30d92fda-77ae-4641-a5df-89133ad48e4a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 320
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(RandomForest_reg,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "rf_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "rf_score.insert(0, 'Model', 'Random Forest')\n",
        "rf_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvKoko1EChiN"
      },
      "source": [
        "## Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 321,
      "metadata": {
        "id": "5uFjLE2pCd59"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# Creating and training model\n",
        "ridge_reg = Ridge(alpha=3, solver=\"cholesky\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 322,
      "metadata": {
        "id": "1YC4CeRyCkWJ"
      },
      "outputs": [],
      "source": [
        "ridge_reg.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = ridge_reg.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 323,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "l5a0MRIaCmE3",
        "outputId": "4e7c2a1d-6014-4726-9f16-50911e7780c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Ridge Regression  0.518053           0.509707                  0.492712   \n",
              "\n",
              "       RMSE  \n",
              "0  0.106825  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82a5d001-4bba-45b8-8437-c39cd728172a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.518053</td>\n",
              "      <td>0.509707</td>\n",
              "      <td>0.492712</td>\n",
              "      <td>0.106825</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-82a5d001-4bba-45b8-8437-c39cd728172a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-82a5d001-4bba-45b8-8437-c39cd728172a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 323
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(ridge_reg,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "rr_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "rr_score.insert(0, 'Model', 'Ridge Regression')\n",
        "rr_score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dv7RuSlCqo8"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 324,
      "metadata": {
        "id": "l_73QvdqCnzd"
      },
      "outputs": [],
      "source": [
        "from xgboost import XGBRegressor\n",
        "# create an xgboost regression model\n",
        "XGBR = XGBRegressor(n_estimators=1000, max_depth=7, eta=0.1, subsample=0.8, colsample_bytree=0.8)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 325,
      "metadata": {
        "id": "EdLCREAgCt-U"
      },
      "outputs": [],
      "source": [
        "XGBR.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = XGBR.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 326,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "SIfmY1dfCug3",
        "outputId": "1bcf622d-865c-4bd9-b156-f6ec58851b84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score      RMSE\n",
              "0  XGBoost  0.925724           0.924438                  0.880065  0.041937"
            ],
            "text/html": [
              "\n",
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              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.925724</td>\n",
              "      <td>0.924438</td>\n",
              "      <td>0.880065</td>\n",
              "      <td>0.041937</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f953d17-275f-448e-bbc9-b135d2493ad5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8f953d17-275f-448e-bbc9-b135d2493ad5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8f953d17-275f-448e-bbc9-b135d2493ad5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(XGBR,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "XGBR_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "XGBR_score.insert(0, 'Model', 'XGBoost')\n",
        "XGBR_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 327,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "kSUq_6aAC-xE",
        "outputId": "e96c88c0-c7ff-411d-c0a3-40122330a5dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0   Ridge Regression  0.518053           0.509707                  0.492712   \n",
              "1            XGBoost  0.925724           0.924438                  0.880065   \n",
              "2  Linear Regression  0.517425           0.509069                  0.492659   \n",
              "3      Random Forest  0.859504           0.857071                  0.814160   \n",
              "\n",
              "       RMSE  \n",
              "0  0.106825  \n",
              "1  0.041937  \n",
              "2  0.106895  \n",
              "3  0.057677  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.518053</td>\n",
              "      <td>0.509707</td>\n",
              "      <td>0.492712</td>\n",
              "      <td>0.106825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.925724</td>\n",
              "      <td>0.924438</td>\n",
              "      <td>0.880065</td>\n",
              "      <td>0.041937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.517425</td>\n",
              "      <td>0.509069</td>\n",
              "      <td>0.492659</td>\n",
              "      <td>0.106895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.859504</td>\n",
              "      <td>0.857071</td>\n",
              "      <td>0.814160</td>\n",
              "      <td>0.057677</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adb51cd1-4a1b-4aef-b099-2986a4f30adf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-adb51cd1-4a1b-4aef-b099-2986a4f30adf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-adb51cd1-4a1b-4aef-b099-2986a4f30adf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ab39baba-7f52-490f-88ab-d033550bb9e2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab39baba-7f52-490f-88ab-d033550bb9e2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab39baba-7f52-490f-88ab-d033550bb9e2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 327
        }
      ],
      "source": [
        "predictions = pd.concat([rr_score, XGBR_score, lm_score,rf_score], ignore_index=True, sort=False)\n",
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 329,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "Lulb2to9CwIX",
        "outputId": "8e92f17f-c5a1-4eef-998e-231656be2a47"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "f, axe = plt.subplots(1,1, figsize=(18,6))\n",
        "\n",
        "predictions.sort_values(by=['Cross Validated R2 Score'], ascending=False, inplace=True)\n",
        "\n",
        "sns.barplot(x='Cross Validated R2 Score', y='Model', data = predictions, ax = axe)\n",
        "axe.set_xlabel('Cross Validated R2 Score', size=16)\n",
        "axe.set_ylabel('Model')\n",
        "axe.set_xlim(0,1.0)\n",
        "\n",
        "axe.set(title='Model Performance for RGB dataset')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGw5Ezc1NPc0"
      },
      "source": [
        "## Feature scaling and Improving Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 330,
      "metadata": {
        "id": "0oBoam9XC2VD"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Creating function for scaling\n",
        "def Standard_Scaler (df, col_names):\n",
        "    features = df[col_names]\n",
        "    scaler = StandardScaler().fit(features.values)\n",
        "    features = scaler.transform(features.values)\n",
        "    df[col_names] = features\n",
        "\n",
        "    return df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 331,
      "metadata": {
        "id": "g4koz_EWNbuW"
      },
      "outputs": [],
      "source": [
        "columns = ['id', 'company id', 'job', 'department', 'mbti', 'predictor1',\n",
        "       'predictor2', 'predictor3', 'predictor4', 'ctar', 'aggregatetotpred',\n",
        "       'decisionstd2', 'individualperf', 'gender', 'age', 'tenure', 'degree']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 332,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_1bD6XPy3H9",
        "outputId": "86dc3c5c-d0f0-4a9e-c181-dbae1cdec271"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'individualperf', 'age', 'tenure', 'gender_M'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ],
      "source": [
        "df_cleaned.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 333,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BrwwUr9a1uup",
        "outputId": "95fa9bda-1b85-4610-d3e7-b06534fa5b8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    predictor1  predictor2  predictor3  predictor4  ctar  individualperf  \\\n",
              "0         0.64        0.33        0.33        0.42  0.00            0.74   \n",
              "1         0.63        0.70        0.25        0.33  0.08            0.70   \n",
              "2         0.72        0.60        0.25        0.33  0.08            0.78   \n",
              "3         0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
              "4         0.65        0.17        0.00        0.08  0.08            0.60   \n",
              "5         0.77       -0.07       -0.08        0.08 -0.17            0.50   \n",
              "6         0.68       -0.13       -0.08        0.08 -0.50            0.45   \n",
              "7         0.68        0.20       -0.33        0.25  0.17            0.55   \n",
              "8         0.66       -0.40       -0.50       -0.08  0.17            0.30   \n",
              "9         0.70        0.37       -0.50       -0.42 -0.42            0.63   \n",
              "10        0.68        0.40       -0.25       -0.33  0.00            0.60   \n",
              "11        0.79        0.57        0.17        0.08 -0.17            0.70   \n",
              "12        0.61       -0.23       -0.17        0.00  0.00            0.45   \n",
              "13        0.61        0.47        0.25        0.50  0.08            0.78   \n",
              "14        0.67        0.23        0.08        0.08  0.00            0.60   \n",
              "15        0.46        0.00       -0.25       -0.17  0.00            0.73   \n",
              "16        0.68       -0.13       -0.08        0.08 -0.50            0.45   \n",
              "17        0.68       -0.23       -0.17       -0.17 -0.25            0.50   \n",
              "18        0.44        0.53       -0.08        0.00  0.08            0.63   \n",
              "19        0.71        0.10        0.33        0.33  0.00            0.55   \n",
              "20        0.67        0.23       -0.50       -0.42 -0.42            0.70   \n",
              "21        0.55        0.83       -0.08        0.33 -0.08            0.67   \n",
              "23        0.57        0.10        0.08        0.25  0.33            0.70   \n",
              "24        0.48        0.47       -0.08        0.33 -0.08            0.85   \n",
              "25        0.55        0.50        0.17        0.08  0.25            0.70   \n",
              "26        0.58        0.17        0.00        0.00  0.25            0.55   \n",
              "27        0.57       -0.13        0.00       -0.33 -0.33            0.40   \n",
              "30        0.58        0.13       -0.08       -0.08  0.00            0.50   \n",
              "31        0.76       -0.17       -0.17       -0.33 -0.08            0.40   \n",
              "34        0.45       -0.37       -0.33       -0.17 -0.33            0.05   \n",
              "35        0.57        0.20       -0.17       -0.25 -0.25            0.50   \n",
              "36        0.68        0.43        0.00       -0.08  0.00            0.65   \n",
              "38        0.36        0.27       -0.17       -0.08 -0.25            0.50   \n",
              "39        0.60        0.43        0.33        0.17  0.08            0.78   \n",
              "40        0.63        0.37        0.08        0.08  0.00            0.60   \n",
              "41        0.62        0.37       -0.17        0.08 -0.33            0.60   \n",
              "42        0.67        0.17        0.00        0.08  0.08            0.60   \n",
              "43        0.79        0.33       -0.25        0.33 -0.08            0.60   \n",
              "44        0.56        0.00       -0.33       -0.17 -0.50            0.50   \n",
              "46        0.57        0.27       -0.50        0.08  0.17            0.50   \n",
              "47        0.62        0.67        0.17        0.08 -0.17            0.70   \n",
              "48        0.75        0.23        0.08       -0.33 -0.75            0.42   \n",
              "49        0.59        0.07       -0.17        0.08 -0.33            0.55   \n",
              "50        0.55        0.43       -0.08        0.17 -0.08            0.62   \n",
              "51        0.75        0.23        0.00        0.17  0.25            0.60   \n",
              "52        0.64        0.00        0.08       -0.25 -0.33            0.46   \n",
              "54        0.77       -0.07       -0.08        0.08 -0.17            0.50   \n",
              "55        0.62        0.27       -0.08       -0.08  0.00            0.75   \n",
              "56        0.61        0.47        0.25        0.50  0.00            0.78   \n",
              "57        0.61        0.50        0.08        0.08  0.00            0.70   \n",
              "\n",
              "     age  tenure  gender_M  \n",
              "0   34.0       3         0  \n",
              "1   42.0      16         1  \n",
              "2   33.0       0         1  \n",
              "3   23.0       0         1  \n",
              "4   19.0       0         0  \n",
              "5   31.0       0         0  \n",
              "6   23.0       0         0  \n",
              "7   31.0       0         0  \n",
              "8   41.0       0         0  \n",
              "9   33.0       3         1  \n",
              "10  30.0       6         0  \n",
              "11  40.0      12         1  \n",
              "12  47.0       0         0  \n",
              "13  33.0       0         0  \n",
              "14  37.0       0         1  \n",
              "15  34.0      14         1  \n",
              "16  23.0       0         0  \n",
              "17  28.0       0         0  \n",
              "18  31.0       0         1  \n",
              "19  37.0       0         0  \n",
              "20  36.0       0         1  \n",
              "21  36.0       1         1  \n",
              "23  40.0       9         1  \n",
              "24  31.0       4         0  \n",
              "25  36.0       1         0  \n",
              "26  38.0       0         0  \n",
              "27  43.0       0         1  \n",
              "30  36.0       7         0  \n",
              "31  35.0       0         1  \n",
              "34  34.0       0         0  \n",
              "35  32.0       0         0  \n",
              "36  26.0       0         0  \n",
              "38  29.0       0         0  \n",
              "39  31.0       0         0  \n",
              "40  36.0       0         1  \n",
              "41  30.0       0         0  \n",
              "42  35.0      13         0  \n",
              "43  26.0       1         1  \n",
              "44  32.0       3         0  \n",
              "46  29.0       0         0  \n",
              "47  31.0       0         0  \n",
              "48  28.0       0         0  \n",
              "49  30.0       0         1  \n",
              "50  31.0       0         0  \n",
              "51  30.0       0         1  \n",
              "52  46.0       5         1  \n",
              "54  31.0       0         0  \n",
              "55  35.0       1         0  \n",
              "56  33.0       0         0  \n",
              "57  34.0       0         0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f7f41e1-f079-40ad-8aef-8ecb58b9808a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictor1</th>\n",
              "      <th>predictor2</th>\n",
              "      <th>predictor3</th>\n",
              "      <th>predictor4</th>\n",
              "      <th>ctar</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>gender_M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.74</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.70</td>\n",
              "      <td>42.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.60</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.77</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.68</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.45</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.55</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.66</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.30</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.37</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>0.63</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.40</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>30.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.79</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.70</td>\n",
              "      <td>40.0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.61</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.46</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.73</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.68</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.45</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.68</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.44</td>\n",
              "      <td>0.53</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.63</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.55</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>0.70</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.83</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.67</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.70</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.48</td>\n",
              "      <td>0.47</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.85</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.70</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.55</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.57</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.40</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.40</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.45</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.05</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.65</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.37</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.60</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.60</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.79</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.60</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.70</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>0.42</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.59</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.55</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.43</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.62</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.60</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.46</td>\n",
              "      <td>46.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.77</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.78</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-6f7f41e1-f079-40ad-8aef-8ecb58b9808a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f7f41e1-f079-40ad-8aef-8ecb58b9808a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe0f60cf-e2ac-4a5b-bd21-3f2da142d243')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fe0f60cf-e2ac-4a5b-bd21-3f2da142d243 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 333
        }
      ],
      "source": [
        "df_cleaned.head(50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91nzd1NZO9ns"
      },
      "source": [
        "## Comparing different models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tByUQ5ZnO_tf"
      },
      "source": [
        "## Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 334,
      "metadata": {
        "id": "5qdqDSIfNv_i"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Creating and training model\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train, y_train)\n",
        "\n",
        "# Model making a prediction on test data\n",
        "y_pred = lm.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 335,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "XhV3XZm-PCDi",
        "outputId": "cb4f9a64-5f90-40f0-89b3-6d940994755e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Linear Regression  0.517425           0.509069                  0.492659   \n",
              "\n",
              "       RMSE  \n",
              "0  0.106895  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
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              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.517425</td>\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6f4f50a3-2990-407a-8418-5ea91a2b6d9f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f4f50a3-2990-407a-8418-5ea91a2b6d9f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(lm,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "lm_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "lm_score.insert(0, 'Model', 'Linear Regression')\n",
        "lm_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 336,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "7Ka-wRbaPGD9",
        "outputId": "29acd84e-897d-4236-ff03-eae7c67a5173"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear regression for RGB dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 336
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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vngKdv/3tb2Hnsnnz5sC4nTt3Rpz3j370IwaAzZo1K+Tn/pOthQsXRrx/V//+978Dj/3tt9/GdF/GIgc6ixcvDhz7888/D3uMn//854Fx27ZtC7ktEX83ohVNoPPnP/85MObVV18Ne6wHHnggMO71118PuS040ElPT2dOpzOu+QYLF+goisKOHj3KXn75ZTZixIgeg9rgIP/xxx8P+3iKonQLVoK/ugZ3jEW3tckf8L755pu9ej1+9atfBY4X7qIPY4xdeumlvT4x37FjR8THiiXQiUakz98HH3yQAWAZGRkxH/cPf/hDIDgP97nHmLYlsKioiAFgy5cv73Y7BTpkMKBiBIT0k2uuuSYkoTPYmWeeGfjzoUOHQm7btGkTHA4HAOAHP/hBxMeYPXs2AG2L1tdffx1xbHt7O44cOYLKykrs2rULu3btCtky9e2330a8/7XXXhvx9lhdffXVEARB9zan0xlI7O3pNSgpKUF2djYA4LPPPgv8/MSJE9i7dy8ArWxuuB4eFosFP/zhD6Oas9FojDj2nXfeAaBtrwlOFtfj/919+eWXIYUJ/FsBN2/ejKqqqqjmFXw/ALoJ973hT1ouLS3F2WefHXbczTff3O0+euL9u5FI/vmlp6fj+9//fthxP//5z7vdR8+CBQuQmpqauAkitJCCIAgYOXIkrr32WlRXV6OwsBD/8z//g6VLl+ret62tLfDnSNvWnE4nysrKwn4Fl+qO1VdffYWnnnoKFRUVcR/D/5qXlZWFvDe68m9VjJbX60V1dTV2794d+DxknX2GgJ4/D2MV6+ev/+9zS0sL/vWvf8X0WP7PoSuuuCJi7yJRFHHuuecCCP3sJGQwoRwdQvrJxIkTw96WmZkZ+HPwCQmgnRz4xZL/Ultb2+1njY2NePTRR/HGG2/gwIEDIf+Q640NJyUlBWPGjIl6LtGYMmVK2Nu++eYbqKoKAPjJT34Sdf+N4NcguKt7pBMkAJgxY0ZUxx83bhzMZnPY2/2/u3379oU9ke9KkiQ0NzcjNzcXAHDDDTfgb3/7G5qamjB58mRceeWVKC8vxwUXXBCxatbo0aNxwQUXYMuWLXjsscewbt06XHXVVZg7dy7OOeccWK3WqObTldfrDeQ4RQpyAK3sscFggCRJIa9/V/H+3Ugk//zOOOOMiCWo8/LyMGrUKBw5ciTic4r0fk6GSy+9NOLFh+Cgq729PSlzGDlyZLccJFmWUVdXh48//hj33nsvPvroI5x//vlYs2YNzj///JiOH/zeC86z0jNz5swej9fe3o4nnngCr776KiorK6EoStixkT4Po9Wbz9/vfve7SE9PR2trKxYuXIi5c+diwYIFmD17NqZNmxb2IpGiKNixYwcA4JlnnsEzzzwT1Vz1/v0gZDCgFR1C+kmkE0ueP/VXs+s/tvX19XE9ntvtDvn+66+/xsSJE/Hwww9j//79Ef+RBbSO5uGkp6fHNadIIiWzJuI1CC69nZOTE/F+Pd3u11MCbiLmffHFF+PJJ5+ExWKBx+PBa6+9hp/+9KcYN24chg0bhltuuSXs1ea///3vgSu0u3fvxh//+EdcfPHFSE9Px+zZs7Fq1apAueFoBb+O/mAsHIPBgKysLACIWCI63r8bieSfX0/PCUCgGWek55SM5Oznn38eFRUVqKiowLZt2/DKK69gzpw5AIDnnnsOP/jBD8L+vfb/HgBELLaRnp4Opm1zD3zdeOONcc9ZFEUUFRXh+uuvx9atW5GZmQmn04nrrruux5LqXbW0tASeX0+/p7y8vIi3HzlyBGVlZVi+fDl27tzZ43sr0udhNHr7+ZuVlYV33nkHRUVFYIzhk08+wbJlywJlor///e/jvffe63ac5ubmmF9noPu/H4QMFrSiQ8ggE/wP8Pbt26NueBhcTcrn8+FHP/oRmpqaYDAY8Otf/xpXXnklxo8fj4yMjMB2hkOHDqG4uBgAIv5DHO7qYW9EOmbwa/DMM89g1qxZUR0z2ZWAenod/POeOnUqXnrppaiPW1RUFPL9kiVL8MMf/hCvvPIKPvzwQ/z73/+Gw+HAiRMn8Mwzz+DZZ5/F8uXL8cADD3Q7ztatW7Fhwwa8+eab2LRpE3bv3g1JkrBlyxZs2bIFf/nLX/D+++9j/PjxUc/PL9pVqsEkUc8pGX9HRo8ejcmTJwe+P+uss/DjH/8YP/vZz/DCCy9gzZo1WLFiBW677bZu9w2uevfNN98kfG7RKCgowPXXX4/HH38cR48exccff4z58+fHdaze/p6uv/56HD58ONAP58c//jFKSkqQk5MDo9EIjuOgqmrg99hTYBJJoj5/L7jgAhw8eBBvvPEG3n//fWzevBnHjx+H0+nEW2+9hbfeegvl5eV48803AxcPgj87f/7zn+PWW2+Nas5GozHu50tIf6JAh5BBJvhKbE5OTkgAE62PP/44kN/w1FNPheQZBIu3MWOyBb8GVqs15GQvWsFBT0/lo6MtL90T/7xdLldccw6Wm5uL3/zmN/jNb34DVVWxY8cOvPXWW3jyyScDTUHPOussXHnlld3ue/HFF+Piiy8GADQ1NeGjjz7Cs88+i48//hhVVVW4+uqroz75DX4d9RqNBpNlOVDSOHgL2kCUmZmJmpqaHp8TcGpbz0B4ThzH4cknn8SGDRtQXV2N+++/HzfeeGO3uU2ePBlZWVloamrCli1b4Ha7496+2BvB2xQrKipiCnSCV5J7+j1Fun3v3r349NNPAUD3AoFfoj4PE/n5azabce211wa2KR4+fBhr1qzBX//6V+zfvx/r1q3DXXfdhcceewxA6HuUMdbrzyFCBjraukbIIDN9+vTAn//973/HdYzKysrAnyP1lwjOBxpIpk2bFriCG+9rUFpaGvhzT4UaEvU6+H93hw4dSuied57nccYZZ+CPf/wjNmzYEPj566+/3uN9s7KycPXVV2PDhg2BPkM7duwI5D70xGQyYdy4cQCAL774IuLYb775BpIkAcCAP8Hyz2/79u0Rt/rU19fj6NGjIffpb1arFffeey8AwOFw4L/+67+6jeE4Dtdddx0AreDAiy++2Kdz9At+bWPdUmU2mwPvvS+//DLi2Ei3J+rzMNpVpWR+/o4ePRpLly7Fl19+GbgIFvw5YDQaA5998X52EjKYUKBDyCAzb968wJXXJ554Iq4tFMEnFOESkVVVxerVq+ObZJLl5OTgnHPOAQC88sorca24DBs2LLA96x//+Ae8Xq/uOI/HE2hG2Fv+QIIxhscffzwhx+zqjDPOCKyyxJow7V/lifW+8+bNA6CdwOk1KvX7n//5n273Gaj882ttbcWbb74Zdtxzzz0X+Ds4kJ7TDTfcgBEjRgDQVg30VgeWLVsWKJ5x55134vDhw306RyD0ZH748OEx39//mldUVERchXz++efD3hbN5yEArFq1KuJcgguRhPs8ifbxevv5a7fbAwUa9AoZANpK1rp16+J+DP/zjfRcCelvFOgQMsikp6cHSsZu3boVt912W6ACmZ66urqQE0wAgaugQPhSw3feeSe2b9/e+wknib8DudPpxA9+8AO0traGHev1erFy5cpuifa//OUvAQDHjx/HHXfcoXvf//iP/8DJkycTMuf58+cHqj898sgjPa64VFRU4N133w352WuvvRYxEfqrr74KFAgI7sS+Y8eOQLUlPYyxQKlejuMwatSoiHMLtnjx4kCRgF/84hdwOp3dxqxfvx7PPfccAK0CVk9VsvrbokWLAhcUfvvb3+qWUf7222/x0EMPAdDyn773ve/15RQjMhgM+P3vfw9Aq06nF1iPGDECTzzxBABt5ef8888PbOEKhzEW8e9aLLZv345XX30VgLbSEE+g+Mtf/jKwkvKLX/xCN3B4+eWX8f7774c9RjSfh08//XSPZZyDq2BGKv2eiM/fdevWoaamJuztDocjcNEh+HMAAG699VakpKQA0N7nwStMetasWYOdO3d2+7n/+cZS5p6QvkY5OoREsGPHjqh6jlx00UWBq6d94Q9/+AM2bdqEL774Ao8//jg2btyIm2++GdOmTYPNZkNLSwsqKyvx0Ucf4YMPPkBZWVnIPvDy8nLk5uaivr4ed999N44cOYKFCxciOzsbBw8exOrVq7Fhwwacd955A3Z7w2WXXYZbb70Vjz/+ODZv3oySkhLccsstOP/885GVlYX29nYcPHgQW7ZswZtvvomWlpZu1aKWLl2KF154Abt27cKKFStw8OBB3HzzzRg2bBiOHz+OZ599FmvWrMHMmTMDJw29TXp+5ZVXMHPmTDQ3N+Pqq6/GSy+9hKuvvhrjxo2DIAior6/HN998g3fffReff/45fvvb32LBggWB+99+++245ZZbcOWVV2L27NkYP348bDYbmpqa8Omnn+Kvf/0rAC35Pfh3vmPHDixatAhnnXUWFixYgDPOOAP5+fmQJAmHDx/GCy+8gA8//BCAdsU3ltLlZWVl+O1vf4tHHnkE3377Lc444wzcfvvtmD59Otrb2/Huu+/iiSeegKIoMBqNUZe07U85OTl45JFHsGTJEhw/fhxnnnkm7rjjDsyaNQuyLOOjjz7CI488ApfLBY7j8Oyzz0ZdGKSv/OxnP8MDDzyA2tpa/PWvf8Xvfve7br18br75Zpw4cQL3338/Tp48iQsuuAAXXXQRFixYgLKyMmRmZkJRFNTW1mL79u14/fXXAyfGgiBETFLXKyOuKArq6urw0UcfYeXKlYHVgP/4j/+IqsJdV1OnTsWSJUvw5JNP4quvvsKMGTNw++23o6ysDA6HA//4xz/w7LPPYsaMGWG3gk2fPh2TJ0/Grl278Mwzz6ClpQXXX389CgoKcPz4cbz00kv45z//2ePn4fTp02E2m+HxeHDPPffAYDBg5MiRgYsARUVFsFgsCfn8/fvf/44FCxbgkksuwfz58zF58mRkZmaira0Nu3btwpNPPhkIzm+55ZaQ++bl5eHFF1/ED37wA9TU1GDGjBm46aabcOmll2LYsGGQJAnHjx/Htm3b8M9//hOHDh3Cu+++261E+qxZs3D48GG88847eOaZZ3DeeecFVnnsdntcv09CEq5P25MSMggEd02P9uutt94K3N/fFfzGG2+MeOxwHdn90EPXaafTyb7//e9HNb8LL7yw2/3Xrl3LzGZz2PvMnTuX7dq1K/D9Cy+80O0YN954IwPARo4cGfG5RCuW14cxxlRVZffffz8TRbHH18BmszG3293tGEePHmXFxcVh7zd//nz2wQcfBL7//PPPux1jzpw5DACbM2dOVM9z3759bPLkyVH97u6///6Q+0bTdd5kMnX7fQV3bY/0NWvWrG5d2KN5joqihHSp1/tKS0tj69at071/Iv9u9CSWx3rwwQcZz/MRX+sXX3xR976HDx+O+PcnHvfdd19Mf0f+67/+KzD+oYceCjvuzTffZGPGjInqPcJxHPvOd77DKioqdI8VzXs0+Fi33norU1U13peE+Xy+iJ+Fo0ePZlVVVRHfN9988w3LyMgIe4yysjJ28uTJHt97v//978MeI/j31dvPX/9nb09ft9xyC1MURXeu77zzDsvMzOzxGDzPs48//lj3NTOZTLr30fv3j5D+QFvXCBmkUlNT8cYbb2DLli34+c9/jgkTJiA1NRWiKCIzMxNnnXUWlixZgvfffz9wpT5YeXk5vvrqK1x33XUoLCyEwWBATk4O5syZg2effRYbNmyI2DF9IOA4Dvfeey/279+P3//+94EeEoIgIDU1FZMmTcK1116LF198ETU1NbBYLN2OMWLECHz77be4//77MXnyZFgsFqSnp+Occ87BU089hQ8++CBky1taWlqv5z1+/Hjs2LEDr7zyCq666iqMGDECFosFRqMRBQUFmDt3Lu6++258/fXXgaRyv08++QSPP/44rrrqKpSVlSEnJweiKMJut2P69On43e9+h927d+Omm24Kud9PfvITvP/++7jttttw/vnnY/To0bBarTAajRg2bBi++93v4uWXX8aWLVtCqtpFi+d5rFy5Eps3b8a1116LESNGwGQywW63Y9q0aVi+fDkOHDgQd/ng/rJ8+XJ88803uPnmm1FcXAyLxQKbzYaSkhLceuut2Lt3L2644Yb+nmZYixcvDlTaeuyxx8L2Q1m4cCH27duH119/HT/72c8wadIkZGdnB95bo0ePxne/+108/PDDqKqqwgcffBBX8QWe55GWlobp06dj6dKl2L59O1asWNGrlVKDwYA33ngD//d//4cLLrgAaWlpsFqtKCkpwfLly/H111/32NB42rRp2LFjB2655RaMHDkSBoMBmZmZmDlzJv7yl79g27ZtUa1y/ulPf8Lq1atxwQUXBD6L9PT28/exxx7DSy+9hJ/+9KeYMWMGioqKYDQaYbFYMH78eNx4443YsmULnn766ZDeU8EWLFiAw4cP4y9/+Qsuuugi5OXlwWAwwGKxYPTo0bjiiivw6KOP4siRI7jwwgt1X7PPPvsMP/nJTwJ/3wkZaDjGelEMnhBCTgMPPPAA7rnnHoiiiLa2tpCkY0IIIYQMTLSiQwghETDG8NprrwHQrmBSkEMIIYQMDhToEEJOa0eOHInYv+Pee+8NJFR3LWZACCGEkIGLtq4RQk5r//mf/4kXXngB11xzDc477zwUFhZCkiTs2bMHL774IjZu3AgAmDRpErZv30770AkhhJBBgspLE0JOe9XV1fjTn/4U9vaJEydizZo1FOQQQgghgwgFOoSQ09rPfvYzpKWlYf369Th48CAaGhrgdruRmZmJqVOnYuHChfjpT38asV8IIYQQQgYe2rpGCCGEEEIIGXIGxYqOqqo4efIkUlNTe92VnBBCCCGEEDJ4McbQ1taGwsLCsL2igEES6Jw8eRLDhw/v72kQQgghhBBCBohjx45h2LBhYW8fFIFOamoqAO3J2O32fp4NIYQQQgghpL84nU4MHz48ECOEMygCHf92NbvdToEOIYQQQgghpMeUFmoYSgghhBBCCBlyKNAhhBBCCCGEDDkU6BBCCCGEEEKGHAp0CCGEEEIIIUMOBTqEEEIIIYSQIYcCHUIIIYQQQsiQQ4EOIYQQQgghZMihQIcQQgghhBAy5MQV6KxcuRKjRo2C2WzG2WefjW3btoUdK0kS/vCHP6C4uBhmsxlTp07F2rVr454wIYQQQgghhPQk5kDntddew7Jly3Dfffdh+/btmDp1KsrLy1FfX687/u6778YzzzyDv/71r9i9ezduueUWLFy4EN98802vJ08IIYSQ04eqMlQcd2DT/gZUHHdAVVl/T4kQMoBxjLGYPiXOPvtsnHXWWXjyyScBAKqqYvjw4fj1r3+NO+64o9v4wsJC3HXXXViyZEngZ1dddRUsFgteeumlqB7T6XQiLS0NDocDdrs9lukSQgghZAjYerART2+qQlW9C5LCYBA4FOemYPGcYswam93f0yOE9KFoY4OYVnR8Ph++/vprzJs379QBeB7z5s3DZ599pnsfr9cLs9kc8jOLxYJPP/007ON4vV44nc6QL0IIIYScnrYebMTytyqwp8YJm0lEbqoJNpOIPTVtWP5WBbYebOzvKRJCBqCYAp3GxkYoioK8vLyQn+fl5aG2tlb3PuXl5Xj00Udx4MABqKqKDz/8EG+++SZqamrCPs7DDz+MtLS0wNfw4cNjmSYhhBBChghVZXh6UxVcXhn5djPMBgE8z8FsEJBvN8HlVfD0piraxkYI6SbpVdcef/xxjBs3DhMnToTRaMTSpUuxaNEi8Hz4h77zzjvhcDgCX8eOHUv2NAkhhBAyAFWedKKq3oUMqxEcx4XcxnEc0q0GVNW7UHmSdn8QQkLFFOhkZ2dDEATU1dWF/Lyurg75+fm698nJycHbb7+N9vZ2HD16FHv37kVKSgrGjBkT9nFMJhPsdnvIFyGEEEJOP81uHySFwSjon7KYBB6SytDs9vXxzAghA11MgY7RaMSZZ56JDRs2BH6mqio2bNiAc889N+J9zWYzioqKIMsy3njjDVx55ZXxzZgQQgghp41MqxEGgYNPUXVv9yoqDDyHTKuxj2dGCBnoYt66tmzZMqxevRovvvgi9uzZg8WLF6O9vR2LFi0CANxwww248847A+O/+OILvPnmmzh06BC2bNmC73znO1BVFb///e8T9ywIIYQQMiSVFtpRnJuCFreEroViGWNodUsozk1BaSHt/iCEhBJjvcPVV1+NhoYG3HvvvaitrcW0adOwdu3aQIGC6urqkPwbj8eDu+++G4cOHUJKSgouu+wy/N///R/S09MT9iQIIYQQMjTxPIfFc4qx/K0K1Dq9SLcaYBJ4eBUVrW4JKSYBi+cUg+e5ng9GCDmtxNxHpz9QHx1CCCHk9BbSR0dlMPDUR4eQ01W0sUHMKzqEEEIIIX1t1thsnDMmC5UnnWh2+5BpNaK00E4rOYSQsCjQIYQQQsigwPMcyoal9fc0CCGDRNL76BBCCCGEEEJIX6NAhxBCCCGEEDLkUKBDCCGEEEIIGXIo0CGEEEIIIYQMORToEEIIIYQQQoYcqrpGCCGEEEJOO6rKqFz5EEeBDiGEEEIIOa2ENKBVGAwCNaAdimjrGiGEEEIIOW1sPdiI5W9VYE+NEzaTiNxUE2wmEXtq2rD8rQpsPdjY31MkCUKBDiGEEEIIOS2oKsPTm6rg8srIt5thNgjgeQ5mg4B8uwkur4KnN1VBVVl/T5UkAAU6hBBCCCHktFB50omqehcyrEZwXGg+DsdxSLcaUFXvQuVJZz/NkCQSBTqEEEIIIeS00Oz2QVIYjIL+KbBJ4CGpDM1uXx/PjCQDBTqEEEIIIeS0kGk1wiBw8Cmq7u1eRYWB55BpNfbxzEgyUKBDCCGEEEJOC6WFdhTnpqDFLYGx0Dwcxhha3RKKc1NQWmjvpxmSRKJAhxBCCCGEnBZ4nsPiOcVIMQmodXrRISlQVYYOSUGt04sUk4DFc4qpn84QQYEOIYQQQgg5bcwam42HFpahpCAVbq+MepcXbq+MkoJUPLSwjProDCHUMJQQQgghhJxWZo3NxjljslB50olmtw+ZViNKC+20kjPEUKBDCCGEEBInn0/Bqs2HcLS5HSMzbbhl9hgYjUJ/T4tEgec5lA1L6+9pkCSiQIcQQgghJA53vbkTr311HHJQc8knPj6Aq2cMw4Pfn9KPMyOEABToEEIIIYTE7K43d+Llbce6/VxWWeDnFOwQ0r+oGAEhhBBCSAx8PgWvfXU88D3Hnfrye+2r4/D5lH6YHSHEjwIdQgghhJAYrNp8KLBdjeuSu+7/XlYZVm0+1MczI4QEo0CHEEIIISQGR5vbEzqOEJIcFOgQQgghhMRgZKYtoeMIIclBgQ4hhBBCSAxumT0GYme/FcZCb/N/L/Icbpk9po9nRggJRoEOIYQQQgYFVWWoOO7Apv0NqDjugKqynu+UBEajgKtnDAt8z9ipL7+rZwyjfjqE9DMqL00IIYSQAW/rwUY8vakKVfUuSAqDQeBQnJuCxXOKMWtsdp/Px186umsfHZHnqI8OIQMEx1jXRdeBx+l0Ii0tDQ6HA3a7vb+nQwghhJA+tPVgI5a/VQGXV0aG1QijwMOnqGhxS0gxCXhoYVm/BDuAVmp61eZDONrcjpGZNtwyewyt5BCSZNHGBrSiQwghhJABS1UZnt5UBZdXRr7dDK6zfrOZF5Bv51Hr9OLpTVU4Z0wWeJ7r4WiJZzQK+H/zxvX54xJCekY5OoQQQggZsCpPOlFV70KG1RgIcvw4jkO61YCqehcqTzr7aYaEkIGKAh1CCCGEDFjNbh8khcEo6J+ymAQeksrQ7Pb18cwIIQMdBTqEEEIIGbAyrUYYBA4+RdW93auoMPAcMq3GPp4ZIWSgo0CHEEIIIQNWaaEdxbkpaHFL6Fo/iTGGVreE4twUlBZSsSJCSCgKdAghhBAyYPE8h8VzipFiElDr9KJDUqCqDB2SglqnFykmAYvnFPdLIQJCyMBGgQ4hhBBCBrRZY7Px0MIylBSkwu2VUe/ywu2VUVKQ2q+lpQkhAxuVlyaEEELIgDdrbDbOGZOFypNONLt9yLQaUVpop5UcQkhYtKJDCCGEkEFBVRkO1rtQcbwVB+tdUNUB3/OckCFDGYR/32hFhxBCCCED3urNVVi5sQptHRJUaFdq73+vEkvmFuPm2cX9Ni9VZbTKRIYsWVHh8spo88hIMYnIsA2u6oYU6BBCCCFkQFu9uQp/XrsPisogChxEDlAZ4HBL+PPafQDQL8HO1oONeHpTFarqXZAUBoPAoTg3BYvnFFPeEBnU3D4tuHH7lG7VDgcT2rpGCCGEkAFLllWs3FgFRWUwihxEngfP8RB5HkaRg6IyrNxYBVnW77OTLFsPNmL5WxXYU+OEzSQiN9UEm0nEnpo2LH+rAlsPNvbpfAjpLUVlaHX7cKzZjVqHB+1eeVAHOQAFOoQQQggZwN7dWYO2DgmiwIHnQk9beI6HKHBo65Dw7s6aPpuTqjI8vakKLq+MfLsZZoMAnudgNgjIt5vg8ip4elMV5RCRQaHDp6De6UF1sxvN7T5IYZrzDkZxBTorV67EqFGjYDabcfbZZ2Pbtm0Rx69YsQITJkyAxWLB8OHDcdttt8Hj8cQ1YUIIIYScPk60urWcnDBpLzwHsM5xfaXypBNV9S5kWI3guNCJcRyHdKsBVfUuVJ509tmcCImFojI43BKONbtR4+iAawis3uiJOUfntddew7Jly7Bq1SqcffbZWLFiBcrLy7Fv3z7k5uZ2G//KK6/gjjvuwPPPP49Zs2Zh//79uOmmm8BxHB599NGEPAlCCCGEDE1F6Vbw0HJy9IIdlQFc57i+0uz2QVIYjIL+9WKTwMOhMjS7fX02J0Ki4ZEUOD0S2r2DO/cmWjGv6Dz66KO4+eabsWjRIkyaNAmrVq2C1WrF888/rzt+69atOO+883DNNddg1KhRmD9/Pn7yk5/0uApECCGEELJgSgFSLQbICoPKQrfUqEyFrDCkWgxYMKWgz+aUaTXCIHDwhdni41VUGHgOmdbBVaGKDE2qyuDokHC8xY2TrR1weYbm6o2emAIdn8+Hr7/+GvPmzTt1AJ7HvHnz8Nlnn+neZ9asWfj6668Dgc2hQ4fw/vvv47LLLgv7OF6vF06nM+SLEEIIIacfUeSxZG4xBJ6DT2aQVVULcFQVPplB4DksmVsMUey7tOPSQjuKc1PQ4pa6nTAyxtDqllCcm4LSQnufzYmQrryygoY2L6qb3WhyeeHr44IdA0FMW9caGxuhKAry8vJCfp6Xl4e9e/fq3ueaa65BY2Mjzj//fDDGIMsybrnlFixfvjzs4zz88MO4//77Y5kaIYQQQoYof+lofx8dBdp2tTSroV/66PA8h8VzirH8rQrUOr1ItxpgEnh4FRWtbgkpJgGL5xRTPx3S5xhjcHllOD0yvJLS39Ppd0nvo7Nx40Y89NBDeOqpp3D22Wfj4MGDuPXWW/HHP/4R99xzj+597rzzTixbtizwvdPpxPDhw5M9VUIIIYQMUDfPLsaiWaPx7s4anGh1oyjdigVTCvp0JSfYrLHZeGhhWaCPjkNlMPAcSgpSqY8O6XM+WYXTI8HlkaEmYVuaR1KwZmcNUi0irpxWlPDjJ0tMgU52djYEQUBdXV3Iz+vq6pCfn697n3vuuQfXX389fv7znwMAysrK0N7ejl/84he46667wPPdP6BMJhNMJlMsUyOEEELIECeKPBaeMXBOsmaNzcY5Y7JQedKJZrcPmVYjSgvttJJD+gRjDO0+Bc4OCZ4krN4wxrDzhAPrK+uwcV8DOiQFo7Ks+O7Uwm7VBgeqmAIdo9GIM888Exs2bMD3vvc9AICqqtiwYQOWLl2qex+3290tmBEEAQBOm0QoQgghhAxNPM+hbFhaf0+DnEYkRUWbR0abR4KShF5NNY4OrK+sw/rddahxhLaDOdLkxtdHWzBjVGbCHzcZYt66tmzZMtx4442YMWMGZs6ciRUrVqC9vR2LFi0CANxwww0oKirCww8/DABYsGABHn30UUyfPj2wde2ee+7BggULAgEPIYQQQgghJLx2rwynR0KHL/GrNx0+BZv2N2BdZS2+Pe7QHWMzClgwtRDpg6iaYMyBztVXX42Ghgbce++9qK2txbRp07B27dpAgYLq6uqQFZy7774bHMfh7rvvxokTJ5CTk4MFCxbgwQcfTNyzIIQQQgghZIiRA6s3MmQ1sVXTVMaw41gr1lXWYcv+Bnh0qrJxAM4cmYHy0jxcPqUQhemWhM4h2Tg2CPaPOZ1OpKWlweFwwG6nUo2EEEIICSXL6oApVEBIb7l9WnDT7pUTfuzjLW6sq6zDh7vrUN/m1R0zPMOC8tJ8XDIpDzmpWt58htWIDNvAWM2JNjZIetU1QgghhAwtqsoGVAL+6s1VgdLTKrQmgfe/V9kvpacJiZekqHAlafXG5ZWxcZ+2Na3ypH5/yhSTiIsm5qK8NA8T81MHTcGBSCjQIYQQQkjUth5sDJRUlhQGg8ChODelT0oq6wVYz316CH9euw+KyiAKHEQOUBngcEv489p9AEDBDhmw/JXT2pKQe6OoDNurW7B2Vy3+XdWk2zCU54CzRmWivDQfs4qzYBxiq6AU6BBCCCEkKlsPNmL5WxVweWVkWI0wCjx8ioo9NW1Y/lYFHlpYlrRgRy/AGp1txbfHHVBUBqPIgee0kzSeA3hOhU9mWLmxCotmjR5029gG2qoZSSyfrKLNI8HllRNeOe1oU7u2NW1PHZpcPt0xo7NtKC/Nw7ySPGQOkO1oyUCBDiGEEEJ6pKoMT2+qgssrI99uDmxrMfMC8u08ap1ePL2pCueMyUr4CXm4AOvb4064vApEHoEgx4/neIiCirYOCe/urBlQ/Xd60p+rZiR5VJXB1Zl7401w3xtnh4SP99Zj3e467Ktt0x1jN4uYV5KH+aV5GJebMiS2pvWEAh1CCCGE9KjypBNV9S5kWI0Ap5WjlVUVIs/DbOSRbjWgqt6FypPOhPaViRRgWQ1C5xVxQBQYOISeuPEcoAA40epO2HySrT9XzUhyeCQlUFhATWANMFlR8eWRFqyrrMVnh5ogKd2PLfAczhmTifJJ+Th7TCYMwuBa2ewtCnQIIYQQ0qNmtw+SwuBTVNQ4PPDKChgDOA4wiQKyUoyQVIZmt/5WmXgFB1hdr0D78wkYEJhLMJVp5XGL0q0JnVOy9OeqGUksRWVwebS+N5KS2MICVQ0urK+sw0d76tDilnTHjM1NwXdK83DRxNxB1fcm0SjQIYQQQkiPMq1GqIzhREsHAO1KMcdrAYZHUnCipQNpFgMyE3xS5Q+wjDpXotMsIk46tIBGVVXwQY3IVaZCVhjSrAYsmFKQ0DklS6SgjuO4pK2akcRx+2S4PDLafQoS2cGlxe3Dhj31WF9Zh4MNLt0xGVZDYGtacU5Kwh57MKNAhxBCCCE9KslPhcJYUOK/diLOcQB4Bp/MoDCGkvzUhD5uptUIg8DBp6gw80LIbTzPI8NqQFO7BFkFwKngO6uuyQqDwHNYMrd40BQiiBTUAYBJ4OFIwqoZ6Z1klYWWFBWfHWrC+so6fHG4WbdogUHgMKs4G+WleThrVCYEWukLQYEOIYQQQnq0p7YNAgeIAgdZBUSegYO2bUxWtZ8LnDYukasNpYV2FOemYE9NG/LtfMhKB2MMHMdjdLYVLe0S2jwSFGjb1dKshkHXRydSUAcAXkWFgecSvmpGYpesstCMMeyvc2FdZS0+3lsPp0e/YWhJQSrmT8rHhRNyYLcYEvb4kQzG7ZIU6BBCCCGkR81uH3iOR2GaBU3tXnhlNZAXYzHwyLKZ4JaUhK828DyHxXOKsfytCtQ6vUi3GmASeHgVFa1uCSkmAQ9+rwwzR2Xi3Z01ONHqRlG6FQumFAyalRy/noK6VreEkoJUlBaG7wRPkssrK3B55ISXhW5yefHhnnqsr6zFkSb94hnZKUZcMikP5ZPyMSKrb/LOzAYBNqMIm0mAOAgLGVCgQwghhJAe+VcbjCKPUVk2eCT1VNU1Aw+PrMKgqElZbZg1NhsPLSwLlFx2qAwGnkNJQWpIyeXBVEJaTzRB3eI5xYPyyvpglqyy0D5Zxb8PNmLd7jp8daQZenGTUeRxwdhszC/NwxkjMvpka5rZIMBmEmEzDs7gJhgFOoQQQgjpUehqgwkWowBA217VF6sNs8Zm45wxWUO+iWa0QR1JvmSUhWaMYXeNE+sr6/DJvga4vPpb08qK7Jg/KR9zJuQgxZT80/WhFNwEo0CHEEIIIT0aCKsNPM8NuGpjqsoSHnzNGps9JLbiDUaKyuDyymjzSPDJiSssUO/04MM9dVhXWYfjnZULu8pNNaG8NA/zJ+WjKMOSsMcOx2QQkDKIt6VFg2OJrH2XJE6nE2lpaXA4HLDbaV8qIYQQ0l+2HmwMrDZInasNxbkpp+VqQ8hroTAYhMS8Fsk6LgnPIylweiS0exNXFtojKdhyoBHrK2uxvboVekc1G3jMHpeD8tI8TB2eHqhmmCxGkUeKSYTNJA7q5qHRxgYU6BBCCCEkJslYxRhsth5sxPK3KuDyysiwGmEUePgUFS2dq1sPLSyLKyhJ1nFJd8lo6skYw84TDqyvrMOm/Q1wh6nINm14OspL8zB7XE7nNtDkMQinghvjEFkVjDY2oK1rhBBCCIlJf20hGygBlqoyPL2pCi6vjHy7OVAdzcwLyLfzqHV68fSmKpwzJium+SXruCSUR1Lg7JAS2tSzxtGB9ZV1WL+7DjUOj+6YwnQzyifl45JJechPMyfkccMxCLyWc2MSYBKTG0gNZBToEEIIIWTAG0jbuSpPOlFV70KG1RhSAhoAOI5DutWAqnoXKk86YwoIk3VckpzVG7dPxqb9jVhXWYudxx26Y2xGAXMm5KB8Uj4mF9m7/V4TSeR52ExaUQGz4fQNboJRoEMIIYT0s4GyUjFQBW/nshgEiAIHxoA9NU4sf6uiz7dzNbt9kBQGY5gcB5PAw6GymHsKJeu4p7NE596ojGFHdSvWVtbi0wON8OgULOAAnDkyA+WleThvbHZSgw6B52A1ikg1U3CjhwIdQgghpB8NpJWKgci/navF7YOsaGWs/Y1KTSIPSVH7fDuXv6eQT1Fh5rufXHoVFQaei7mnULKOe7pRVYa2BFdOO97ixrrKOny4uw71bV7dMSMyrZg/KQ+XTMpDTqopIY+rh+c4WE0CUkwiLAYhqatEgx0FOoQQQkg/CZd4vqemrV9WKgaiypNO7D7phMsjQ2U4VbmKAYpPAc8Bu086+3Q7V2hPIT7kRLM3PYWSddzThVdW4OxIXN8bl0fGxv31WLurDrtrnLpjUkwiLpqYi/LSPEzMT01a0MFxHGxGbVua1UjBTbQo0CGEEEL6ASWeR6fJ5YWjQ4LSed7K+f/TGfQoDHB0SGhy6V9lT4Zk9RQaCL2KBhvG/H1vZHgk/QpnsVBUhq+PtmBdZS0+PdgISekeMPEccNaoTJSX5mNWcVbSKplxHAerP7gxCPR7jwMFOoQQQkg/oMTz6DS1+yCr2slmyMvEdcY7DJBVhqb2vs1bmTU2Gw8tLAtsO3R09hQqKUjt1bbDZB13qJEUFW0ebXuaovZ+9eZIUzvWV9bhwz11aHLpv5dGZ9tQXpqHeSV5yLQlZ/sgx3EwGzrLQRtFCm56iQIdQgghpB9Q4nl0nB1S4M/+3Jzg7/XG9ZVZY7NxzpishBeSSNZxhwK3T4azQ4bbJ/f6WI4OCZ/srce63XXYV9umOybNYsDFE3MxvzQP43JTkrZlzGzQVm5STCIE+j0nDAU6hBBCSD+gxPPocDzn36kGIDS4CYwBwDig4rijzwOD/uopdDpRVIY2j4Q2j9zr0tCyomLbkWasr6zD1qqmwGphMIHncM6YTJRPysfZYzJhCHMxordMBgEpRq3XjZikxzjdUaBDCCGE9ANKPI/O9OHpMAg85M4TXNb5xXV+AVqw8c6OE/ifzYeGROU6qsSnSWRjz6p6F9btrsWGPfVoceuv/o3LTUF5aR4umpiL9CRdYKDgpm9xLFEtYZPI6XQiLS0NDocDdvvp/YFPCCFk6DhVdU3RTTynqmta0YYrV36KypNOMAYI/KktbP6L+waBg91iCKlc1zJIX8NwlfgG6/OJlb80tLOj9409W9w+bNhTj3WVtahqaNcdk2E1YF5JHspL8zAmJ6VXjxcOBTeJF21sQIEOIYQQ0o9Crt53Jp6fjlfvI9l6sBFLXtmu9dDpcpvIc7CZBAzPsHZbFat1elFSkIoXF80cFPktqspw4wvbsKfGGVKJDxiczycWHklBm6f3paElRcVnh5qwvrIOXxxu1i1UYBA4zCrORnlpHs4alZmUnBh/cGM1CUnb+nY6izY2oK1rhBBCSD+ixPPo6Z3+KowhxWQYEpXrTrdKfKrK4PJppaG9vSgNzRjD/joX1lXW4uO99XB69AsVlBSkYv6kfFw4IQd2iyHuxwuHgpuBhwIdQgghpJ8NxIR2VWUDIvhSVYaHP9iD1jB5FYwBTS4PMmwGcAid32CrXHe6VOLzSAqcHglur9Kr1Zsmlxcf7qnH+spaHGly647JTjHikkl5KJ+UjxFZ1rgfKxwKbgY2CnQIIYQQEmIgJcNXnHBgb21bYDUnOJTx/8wjM3T4FFiNoac1wZXrBkrgFslQrsSnqAwujwynp3e5Nz5Zxb8PNmLd7jp8daQZei10jCKPC8ZmY35pHs4YkZHwrWkU3AweFOgQQgghJCA4Gd5iECAKHBgD9tQ4sfytij5Pht9e3RLoTs8F/tMp6CS33SuHBDrBlescHT7c+MK2ARG4RTIUK/F1+DpXb3pROY0xht01TqyrrMMn++rR7tXf5lZWZMf8SfmYMyEHKabEnuJScDM4UaBDCCGEEADaNrGnN1Whxe2DrDA4OqRAhTOjwENSVDy9qQrnjMnqs9WQGofn1DddHpLjTvXVcXTISDEr3SrXzR6Xjbvf3tWtitmemrZ+Cdwi4XkOi+cUY/lbFah1enUr8S2eUzzgVqK6SlTfmzqnBx/ursP63XU43tKhOybPbkL5pHxcUpqHonRL3I+lh4KbwY8CHUIIIXEbDNuBSPQqTzqx+6QT7V7t6jvPc/AvKngkBZzMYfdJZ58mwxfYzYE/+4Ou4O8D49LMcHtlODor15UUpOKXs8fgmc2H4PLKIVXMzLyAfDuPWqe3zwO3nswam42HFpZh5ScHUXnSCZ+iwijwKC20Y8mFYwdMUKanw6egzdO7vjcdkoItBxqxvrIW31S36hagMBt4zBmfg/LSfEwZlgaeS9zvjoKboYUCHUIIIXEZSHkcJDGaXF44PRJUlQEcICvsVHNODmAqg9Mjocnl7bM5nTEyAwaeg9SZjKF3/mzgOfz3j6ZC5PmQoHuwVjGrPOlA5UknnB5tRc3DKag86UTlSceA+7slKypcXrlXqzcqY6g47sC6yjps2t+AjjAV2KaPSEf5pDxcMC4HFmP3HKZ4UXAzdFGgQwghJGbhmhoOxO1AJHotbgmKwqACIfkvDEEBhsLCdpZPhrKiNEwsSMWuE07dq/scgIkFqZg6LL3bqsxgrGK2enMV/rx2HxSVQRQ48BygMsDRIeHPa/cBAG6eXdyvc2SMwe3T+t50SPGv3tQ4OrC+UtuaFrJFMUhhuhnlpfm4ZFIe8oNW93qLgpvTAwU6hBBCYuLP4xhM24FIdOxWMSSY0NsmxjrH9RWe53DnpSW47fUdaHJ5wdipLWwcB2SnmHDnpSW677XBVsVMllWs3FgFRWUwihx4TjsB5zmA51T4ZIaVG6uwaNZoiGLfn5z7ZBVtHgkur6zbiDMabp+MTfsbsa6yFjuPO3TH2IwC5kzIwXdK81FaaO+2Ghcvk0GAzSjAZhIpuDlNUKBDCCEkJoN1OxDpmdMthyT4612o5zhtXF+aNTYbj/1oGp7aWIV9tW2BvJUJ+an41dzwWyUHWxWzd3fWoK1D6lzJCT0R5zkeoqCirUPCuztrsPCMoj6ZUyKaeqqMYXt1C9746gS2HztVRS8YB+DMkRkoL83DeWOzYTYkZmuaUeSRYhIpuDlNUaBDCCEkJoNxOxCJTobVAIHnoOqciPoJPIcMa+K7yvvJsop3d9bgRKsbRelWLJhSAFHkMWtsNs4ZkxVT8YtEVTHrq6IbJ1rdUAGIYQ7Nc4DSOS7ZOnwK2rxSoDBFPI41u7F+dx3W7KxBa4f+dscRmVbMn5SHSyblISfV1JspB/iDG6tRhLEfVr7IwEGBDiGEkJgMtu1AJHpZKabOClbhT2x5jkNWSmJOSLtavbkKKzdWoa1DggqAB3D/e5VYMrc47rwUfxUzf+GM4Kps0RTO6MuiG0XpVvDQcnL04qjOGhEoSrcm9HH9ElFYwOWR8cm+eqyrrMPuGqfuGJ4DDAIPu1nE/7uoGGeMzOzNtAFox/Ov3FBwQ/wo0CGEEBKTwbYdiERvXLYNXjnyCa5XVjEu25bwx+6ahC/6k/DdWhL+kcZ2HG12Y29tGySZwSBymJifil/N7bnk8qyx2ZgxIgOrNh/C0eZ2jMy04ZbZY2DsoXJXXxfdWDClAPe/VwmHWwLPqSHb11SmQlYY0qwGLJhSkLDHDC4s4PbFtyVRURm+PtqCdZW1+PRgo+7WNEDLvbGbDbCZBHAc0Ojy4e9fHse0ERlxlYg2CDxsJhE2kwCTmLgqbGTooECHEEJITIZKU8OBJNx2rb727KeHox73/+aNS9jj9pSE75UZXtl2DCLPnVpr8gFfHG7GgfodeOxH0yJua9NbKXp+6+GIK0X9UXRDFHksmVuMP6/dB5/MIApqoOqarDAIPIclc4sT8t7wyf7VGynuwgKHG9uxvrIWH+2pR1O7/lZVgedgN4nIsBogdtnummo24FhTOw7WtWN8fkpUjynyPGwmraBAovJ4yNBFgQ4hhJCY9XY7EDklGdu14nW0uT2h46IVKQmf69xKxwAojMEg8PBvrpNVFQ1tXtz1dgUK7GbsrnHCJ2vBktZgcxwqTzoirhQB+uWa+6vohn8u/veEAm27WprV0Ov3BGMssDXNE2dhAUeHhI/31mN9ZR321bXpjkmzGHBxSS6GZVjwt61HkJVi1F2xMQoc2hiDwxM5n0/keVhNAlIouCExiivQWblyJR555BHU1tZi6tSp+Otf/4qZM2fqjp07dy42bdrU7eeXXXYZ1qxZE8/DE0IIGQDiSQ4noXrargX0bc+UkZmntqTp/RaZzrhEiJSErwatNnBA4ISZA2Dgta1khxvdONx4KkHfLQFbq5pReeIryAxxlWvuz6IbN88uxqJZoxO2ytfbstCyomLbkWasq6zDZ1VNkHWOIfAczhmTie+U5mPm6EwYBB77a10wCjwkhcGk88v1KQwGjkOauXs+n8BzsBpFpJjEhDYHJaeXmAOd1157DcuWLcOqVatw9tlnY8WKFSgvL8e+ffuQm5vbbfybb74Jn+/Uh0BTUxOmTp2KH/7wh72bOSGEkH7H8xyVkI5T1+1aAAfWmYRuFNEvPVNumT0GT3x8ALKqraDo9dEReQ63zB6T0MeNlIQffE7dLYjmQm8PxgC0erRVC0Mc5Zr7u+iGKPK9KiHNGEO7T4GzQ4p79aaq3oW1lbXYsKc+bNW0cbkpKC/Nx8UTc5HWpRrf2DwbhmfZcKjBhewUI7ig8JmBoc0jYUxOCsbmaYEzz3GBlRuLQUhY/xxy+oo50Hn00Udx8803Y9GiRQCAVatWYc2aNXj++edxxx13dBufmRlaSePVV1+F1WqlQIcQQoaAviq7OxT5t2sJPCApDGpQCV+eAwQefd4zxWgUcPWMYXh52zEA+n10rp4xrMck/lhFSsIPrgAn8BxUxgINQ1U1tHCCXmAGdK4K6cSKkco1D9aiG71dvWlx+/DRnnqsr6xFVYP+FsUMqwHzSvJQXpqHMTnhc2t4jsM1M4fj0Q/3o9HlQ6rZAKPAwadoQY7VKOCamSM6CxSIsBopuCGJFVOg4/P58PXXX+POO+8M/IznecybNw+fffZZVMd47rnn8OMf/xg2W/hlb6/XC6/XG/je6dQvT0gIIaT/9GXZ3aHoRKsbKtMPJlR2qpRwX/RMCfbg96cAAF796hiCKwwLPPDjGcMDtydSxCT8oDn4ZBWMaaFP1yLYXU+Pe2p8CkQu1zyYim74V2/aPBI6fLGv3vhkFZ8fbsK6XXXYdqRZN0AyCBxmFWejvDQPZ43KhBDl854+IgPLLhmPV7Ydw7GmdrQxbbvauLwU/OKCMbhoYt6AeA3J0BRToNPY2AhFUZCXlxfy87y8POzdu7fH+2/btg27du3Cc889F3Hcww8/jPvvvz+WqRFCCOlDfV12dzCItXJaQZolQrcaDesc19cun1KIw43t2HnSCUlWYRB5TCm04/IphUl7zLBJ+BYRAseh2S0FAhPd0+II58oqtPLMsZZrHuhFNyRFhbMjvtUbxhj21bVhXWUdPtlbD6dHv7R0SUEqykvzceGEHKSa42sUO31EBqYOT0d1Uwe8ioICuwVThqVRgEOSrk+rrj333HMoKysLW7jA784778SyZcsC3zudTgwfPjzZ0yOEEBKFWMruqiobEGWTky2eymmjsqNr+hjtuEQJBLEeGTajAGbQtm3tr3clPYjVS8K/fHI+rnpmK1o6pMBqTqx4IO5yzf1VdCPcttDert40urz4aHcd1u2uw9Em/dXCnBQTLpmUi/mT8jEiq3fvP5NBQIpR63UzNje1V8ciJFYxBTrZ2dkQBAF1dXUhP6+rq0N+fn7E+7a3t+PVV1/FH/7whx4fx2QywWRKTtdlQgghvRNt2d0/rtmNt745MSDKJidTvJXTdh53RHX8nccdODPOzvGx5lD5g9iGNi86JCW0EAAHdPiUhPeO6aprEn7FcQeaXD5kWY1odvvCFx/oXO3x72nzD0sxCfj1hcV4evPhuMs193XRja0HG/HUxoMhzVEn5KXg+nNHYVJBGmQ1clPXrrySgn9XNWF9ZS2+Otqi+xqaRB4XjMvG/El5mD4iI+qtaXqMIo8UkwibSYQhTNU6QvpCTIGO0WjEmWeeiQ0bNuB73/seAC0RcMOGDVi6dGnE+/7jH/+A1+vFddddF/dkCSGE9L9oyu7WdEj429YjUBkGRNnkZOmp0WWkymm1Dk9UjxHtuK7iyaGqPOnEt8da0R60UuDPhVEZ0O5T8O2x1oT3jomk2e1Du0+B2ysDDBD5Uzk6/vN9/2k/C/xHI3DArRePw82zi/Gz84sHxeri1oONuO31HWhu92krWIwBXq056t7aNiy/rATTR2T0eBzGtCB3/e46fLKvHu1e/dWfsqI0lJfmYc74HNhM8W/0MQinghvjAHxdyekp5nf0smXLcOONN2LGjBmYOXMmVqxYgfb29kAVthtuuAFFRUV4+OGHQ+733HPP4Xvf+x6ysrISM3NCCCH9oqeyux2SDHfniXKsJ/+DTaRGlz2VLy6wm6N6jGjHBYs3h6qhzYO2LrkaXS/+t3lkNLR5APRNoJNuMcAjKVoOCgco6qlAp7OXKAQOsBgFuH1KoCKb3WzAkgtPrdj0tlxzX1BVhoc/2IN6pxccp5XyBsdpzVJVhuZ2Cc9sqsJT152p24ATAOqcHny4uw7rd9fheEuH7pg8uwnlk/JxSWkeitLjzwGj4IYMdDEHOldffTUaGhpw7733ora2FtOmTcPatWsDBQqqq6vB86Fv9n379uHTTz/F+vXrEzNrQggh/aansrv1bT4wxNe7ZLCJ1OgSiFy+ePrIDBh4DlKEJHIDz2H6yJ6v3geLJYeq6/azXSecURVI2HXCiYtK8noYmTiKyrRVm6DJMQT19uE4vPjTmahu6hjwKzaRbDvcjL01beCgBTlcUHNUjtfyig43urG/zoWJ+afyXTokBVsONGJ9ZS2+qW7V/R2aDTzmjM9BeWm+VgggzjLORpGHzUjBDRkc4lqjXLp0aditahs3buz2swkTJmhLr4QQQga9nsruijzX2cE+zP0jnPwPNsGNLjmwwMk3x3VurYpQvrisKA3DMi043Bj+dRiWaUFZUWwrJ9HmUOlvP4s29yO2HJFYdM0ramz3RhV8OT3yoAycJUVFm0eGyyPj06pGyCqDKED3dycIDLLCsLvGifF5Kag47sC6yjps2t+AjjBNQaePSEf5pDxcMC4Hljj7H9HKDRms+rTqGiGEkKEhUtndyUVpeHZTlW6XeyDyyf9g42902eqWIHeejvu3Vfn/nB6hfHGKSezWD8aP67w9VtHkUDlUhma3r9ttaqQazXGMi5VeXlGK2QCmskC1tK54DgBjaG2XkjKnZHH7ZDg7ZLh9p7YKcv7nF/wmCtZZZGHboSa88fVx1ITJ3ypKt2D+pDxcUpqH/Di2PgIU3JChgQIdQgghcQlXdldVGf6+rVq3y300vUsGE1HkcfnkfLy87VhIsBL858sn5+tuoao86USTy4fCdAta3T54ZSUQHJpEAelWI5pcvpgT/3vKofIqKgw8h0yrsdttaVEGVtGOi0W4vKI6R0e3bWvBVKY1M82wxtfjpS/Jnas3bR5Zt3JaSaEdosBBURg4nkFV2almp535SQCw7UhLt/vajALmTshFeWkeSgpSUVXvxrFmN5xuGWPzbFFtVTMIPGwmrRS0SYxv9YeQgYQCHUIIIXHTK7vLd/Ym0e1yH2XvksFCVRmqWzpgFnl45O4nrmaRR3VLB1SVdcuH8a+85KYakWEzwONTIasqRJ6H2ciDqUC9y6u78hJJTzlUrW4JJQWpKC20d7svF2Up4GjHRStSXlFWihGOMM0s/XhwyLB1D9wGig6fAqdH6iyWEH4j3vi8FIzOsmJ/fTt8cpdxYVazzhyZgfmT8nH+2CyYDAK+qW7BHW/swqHGU6tiY7JTcM3Zw3WrtYk8D5tJgM0kwmyg4IYMLRToEEIISbiwXe5j6F0yGFSedGL3SSdkVQvggkMZBkBWGXafdOquygSvvJi6FG0Ai7zyEklPOVQpJgGL5xTr9sGZOiwt7FY6P65zXCL1lFc0GCkqQ5tHQptHhqREl9PEcxyK0i3YX98ecdzITCvml+ZhXkkeclJP9R38proFD72/Bw5/c9XOfLFvj7fgSJMrUJpa5HlYTQJSKLghQxwFOoQQQpJCr8v9YKyEFUmTywunRwJjDAaRR3Cow8AgySqcHglNLm+3+/pXXr495oCiqvApauDE1CjwEHgeU4en6a689CRSDlWkPjo8x0HgOcgRKsEJPBd3xa5wIuUVyV2ChK7BJKCVRoh15SsWsTRe7fApaPNIaO9h9SaYyyPjk331WLurFntq2yKOtRoFrL7+zG5/j1TG8MymKjS3S+AACAIHjtOCHUXRSlOv3nIIb9xyHmxmOv0j0Yu18fBAQu90QgghSTMYepf0RotbCmxL47pkj3PgwPMcVJWhxd09UZ7nOcwel43PqpqgqAyiwEHgtS1+bp8CgVcxe1x23CcU4XKoIh2vtUOCzSii3SdBZyceRB6wmUS0diQ28T9SXlFwnOPvm+PP1dd6M3VuyUtSMYJoGq/Gs3qjqAxfHW3G+so6fHqwEZKiHxRxAHge4KE9b49PwSf7G3BxSS4O1rXD4fEhzWyEoqo43OTWSlOLp96PPAfwIiDJWmnqgw0uTB2e3uvXhZwe4mk8PJBQoEMIIYTEKd1m0IIZxsAY65YPozItCEq3dU+UV1WGzQcaYTMJkBUGn6JCUbWTeatRgChw2HygET87f0zcwY5eDlUkmVYjbCYBKWYBzS4vvDILBBUmkUNGiglgiHk7XU8i5RUFL/KYRD5kSxbHAZKsgue5pBQj6Knx6n0LJqG0MC2m1ZvDje1YX1mLj/bUo6k9/CqUwHMQuNCteypTwaBtUVu/uw7Hmtohda7WGQ2CdiLKazlLfhynBT2ioEJSVOyobqVAh0Ql3sbDAwkFOoQQQkicsm0m2M0inB4Zksog8gjkuMgqA89xsJtFZNtM3e7rz0vJTTXDJPJo7ZAgKSoMAo90iwEeWY3Q7yY5/AHH10da4FVYoFsOA+BVtFWTM0dlxLWdLpJIeUWODhl8Z18ifzELnteCHVnRgku72YCslO6vcW+EK5Bg4njkpBhR6/TiyU+q8NCVk7HxQAPqnB7k2c24aEIuBCE0MHV0SPh4bz3WV9ZhX53+1rQ0iwHjclPw9dEWbRUGDIqqbUkLrFx1jv3icDMAwG42wC5wkBSG+jZte6TKtAaq3XT+iA2OHUekn/Wm8fBAQoEOIYQMEoN5n/RAksjXsbTQjkmFabp5NmZRy7OZVKifZ+PPS/EpKmocHnhlJXDfVreErBQjpDD9bpKF5zmMyLBgy4HGbrepDHBLCkZkWJLyvguXVzS5yA5Hh4TqZjdkRdVWmTpfJ5PIQRR4TCq0Jzz46logQWUMiqqt0oEBqWYRe2sc+P7TW9EhKVChbS978pODuHbmCHz/jCJ8cbgZ63fX4bOqJt28J5HncPaYTJRPysfZYzLBg8P3V21Fm0dG8E42hSHwvcABqqoi126GwPPgOQ5mg/bzQ01uKExb+Qku684Yg6IwiDyP6bSaQ6LQu8bDAwcFOoQQMggM9n3SA0WiX8fglYg2j4wMmxECx0FhDB0+BalmMWyFs0yrESpjONHSAUDbqsR1rlR4JAUnWjqQZjEkfJtYJLKsYs2u2m4J/8Hfr9lVi/u/OzkpRSXC5RV9fqgJt72+A80+pXOboDYnnwKkmsNXkeuNZrcPPlmF3czBJ6vdtqa5vBLafdqal8hrgQZjQJtHxjObD+F/PzsCj6SfrzM+LwXzJ+Xj4om5SOuy5W5Yuhl7al1h58UByEoxwyQKISegVpMY2FokKwyiwIJWF7UtbxPyU1BW1D8npXShZnDpTePhgYQCHUIIGeCGwj7pgSBZr2PXlQivqpWFnlRojxhAleSnQulcJTCKpyqZcRwAnsEnMyiMoSQ/Ne7nHOvJ5bs7a9DWIXUWRuC65cMoKkNbh4R3d9YkrchEuLwin6ydwPvDDcYApjB49aom9FKHTwE6m7d2SApMOhXOWjqLHxh47Qq3wrTXG+gsGtAlyMm0GTGvJBflpfkYnW3TfVxJUrG/LnyQAwAyA4x897LbHMehIM2M6mZ3Z3PRU4EZz3HITDHizktL+iW4oAs1g09vGg8PJBToEELIADZU9kn3t2S/jvFUONtT2waBA0SBg6wCIh98BV77ucBp4+LZGhLPyeWJVjdUACKnVY3jOIQs5/Acg9I5rq+oKsPDH3T2hkHnyknntBSm5b88/MEe/GvJ+b36O9C1clpRhhnDs2w41OBCdooxpKpem0cKBFwyA1iYctwCz+GCsdkon5yHGSMzIfQwv1e/PAa94mtc53/8i0r1Lh+KMrqfwhlEHpk2A4ZlWHGitSPwe5+Yn4pfzR3bL0EFXagZnHrTeHggoUCHEEIGsKGyT7q/Bb+OgHbFXlZViDwPs4FPyOsYa4WzZrcPPMejMM2CpnYvvPKp/B6LgUeWzQS3pMS1NSTek8uidCt4aPk4eufkaueWsaJ0a8xzilfFCQf217kCJ/ldAwHGgP11LlSccMRVTSxc3xue43DNzOF49MP9aHT5kGo2wMADLp+CRtep34lesTV/wPrjs4bhZ+ePiXoutW0dp44RISZq9yq6Vf5a3RImFabhhRvPwp7atn7fJkYXagav3jQeHkiGTtc2QggZgqLZJ93XCeuDUSDxX1ZxpKkdR5vbcbylA0eb23GkqR2SrPb56+jfGmIUeYzKsmFkpg3DMiwYmWnDqCwbDCIf19aQrieXZoMAnudgNgjIt5vg8ip4elNVYJtVsAVTCpBqMUBWGFQWuvVKZdrWsVSLAQumFPTqucdiR3WrFgSGuZ0B8Mpa2eRoKSpDq9uHY81u1Dg64PLKuuWhp4/IwLJLxmNYhhVNLi8ON7lR5/TqrroA2mqTobMfEg9gRKb+FrVgRpFHps2I4ZlWlOR3CZRZ0FcQi1ELEjokBarK0CEpqHV6AyefosijbFga5ozPQdmwtH47GY3lQg0ZePzbcksKUuH2yqh3eeH2yigpSB00K3G0okMIIQPYUNkn3d+0xH8VJx0+rfwu39k1HkCHpOKkowN2s9inr2Pw1pC8VCM8stJZXprBJHJxbw3pzSqgKPJYMrcYf167Dz6ZgeeUwG0q07ZiLZlbnJRCBOHILLocnGjGhVu90eOVFPy7qgnrK2tRedKBMLvTwHFaOWeus+eNylTIqlaV7aIJubr3MQg8UkwibCYRxqDX8pbZY/DExwcgq6z7SlHn9yLPYcWPp+G5T4+EVKcrKUgdcDkvQyWh/XQWz7bcgYQCHUIIGcCGyj7p/qYl/mt9V0IS/6HlxmiJ/+hV4n+s/FtDlryyHbtr2kIu2p8AkG41RLU1pGvBgcZ2b69OLm+eXYzPDzVhw96GbisXc8Zn4+bZxTE8y95r88i9GqeoDC6PDKdH61MUCWPaa7l+dx0+2VePdq+iO66sKA2ZVgO2HGiEygDGaS+UqjLIqrayc+3MESH9dASeg80kIsUkwmzoftECAIxGAXPGZ2PD3oawc5wzPhtzJ+Rh9rjcAX/ySRdqhoZYt+UOJBToEELIADZU9kn3Ny3xX6skJqsAz6ngwIGBBVYqBI6LO/E/XpUnHYEk+2AMWpJ95UlHxCv0Ww824qmNB7G3tg2SzGAQORSlW6AyNe6Ty9Wbq7Bpv9ZHJ7jvpcKATfsbsXpzVd8GOz2svIQb55EUOD1SIJ8lkjqnBx/ursP63XU43tKhOybfbsb8SXm4pDQPRekWAMDrXx7Dy9uq0e6RAwUSUs0irp05Aj86azgEnoPVqAU3FqN+cBNMVRl8CoNZ5OCRu8/ZLHLwKQyqyiKefA6UUs50oYb0Nwp0CCFkgAvXSHEgblUZqLTEfw5ZNiMaXT5I6qnEB4HjkJVihNo5rq/IsoqVG6sCCf7Bp7UctK1iKzdWYdGs0bpbxbYebNR6y7T7Tp3j+7QACQAkBRiRaYnp5NI/J0VlMAoAx/GBAgkCUyEpLOKckqGhLbrfSUObL6bVmw5JwZb9DVi3uw47qlt1c4DMBh5zxuegvDQfU4alBVYC/X501nBcdcYwfLyvHnVOD/LsZsybmItUq0ELbgxCt+2Dkfi3HQ7PtMEgAI1tEnyKCqPAIzvVAJ+CHotmDKRSznShhvQ3CnQIIWQQGOz7pPubvzlnc7uvW66FyhgaXV6kW41ItxhQcdzRJ6/xuztr4HRrQUmYdAw43fo9a/wllxvavFqOCM+HNodkQIcko8bhQYbNGPXJpb+PDs9rJa4ZUwMrFRwH8DyS3kenqxx7dNuarCYe1c3uiKs3KmPYedyBdZW12Ly/ER1S961pHIBpI9JRXpqPC8ZlwxJmm5mfIHAoL82H1STEFdwEC85p4XkOeWmhj20Ci7jtcCCWcqYLNaQ/UaBDCCGDxGDcJy3LKt7dWYMTrW4UpVuxYEpBnyay+5Xkp8IrK4GcE39fEjAtOFAY0O6T8V9r9+Bwo7tProQfb2lHT+nzaue4ripOOLCv1gUOgIE/tSXI/72kqAADCu1GHG4+1U9lSpEdSy8aH7GPjqJT5Yuhc2dY5+pTX/bR4aMsECvLCBvknGjtwIeV2ta0WqdHd0xRugXlpXm4ZFIe8uzmHh+P4zhYjVpwYzXGH9wE601Oy0Au5UwXakh/oUCHEEJIUqzeXIWVG6vQ1iFBhVZu9/73KrFkbnGfJ7RX1jjhDepUzwL/OcUjqdh1wolcu7lProQzFt1Jnt64b461an2ABE63spogcPDJDJU1rkAw5ZWBL460YPTOk2GfS0GapcuxgufR+X+dcclUkp8S1bgxOaG9fdq9Mjbtb8C6yjpUnHDo3sdmFHDhxFzMn5SH0kJ7j8EKx3GwGATYTAJsRjHhJ+q9yWkZ6D23BuOFGjL4UaBDCCEk4VZvrsKf1+6DojKIAgeR03JOHG4Jf167DwD6NNjZUd0KhTGIvDYP1rmS49+S5d/OZjUKgYpYyb4SPjY7uqabeuM4f5DWWfFL6SxHzHFaYQW5M5G964qRogIvbzsGAHjw+1O6Hbc426aV3e5cuQl5THS+Zpw2rq/sqXVFNe5QgxvnFDPsONaKdZW12HKgEV65+5oZzwFnjszA/En5OH9sFkw9bE0DAItRCBQVEJK4CtGbnJaBXsp5oKzuktMLBTqEEEISKiShXeTAc9rJDM9p1c58ct8ntPsXRXiegwgusBVLO6nXKl0B6NaOPplXwp/acijqcZdNC82HmTYiHQaBh09WwbrUgJbCdbMM8vcvj+G+K0ph7FIJzOGVYTUKWqUyBAVUOLUAZjUKcHijK/ncWx0+Be1eKaqxX1c3472KGjS4vLq3j8y0Yn5pHuaV5CEn1dTj8cwGATaTCJtRgBgmeEiGeHNaBnIp54G0uktOLxToEEIISSh/QrsonApy/HiOhyiofZ7QPn14OkSeh6Ko4IXOPjqdMY3cuZzDAbDqXN1P1pXw+jC5ItGMKytKQ4bVgFqn/kl9T1QGPLW5Cr+ZNz7k55lWI9LMBphFAa1uCUpQzovQGfSZRD6pJ8uyosLlldHmkSEpKjJTTN2q0unZecLZ7Wf+pp3lk/MwIS+1x61pJoOAFKMIm6lvg5uu4slpGailnAfa6i45vVCgQwghJKFOtLqhAhDDnJPxHKCgbxPay4rSMCE/BZUnnZBUNaRKmdp5Mm8UeN1eJ8m6Ep5pNaI+itLJ4RLP23q5qrLzeGu3n/lPlnceb4XJwMErAQwMHDiYDBxkVcWkXHtSTpbdPhnODhluX+jzumhCLh7/aD/cUk+lGzQ8B8wcnYny0nycOyYLxh5WDQ0CjxSTCJtJ7HFsX4o1p2UglnIeiKu75PRC7ypCCCEJVZRuBQ90K+Ps5+8bU5QeXY5KIvA8hzsvLUFOqgk8x0FRGWRVy23heQ4GgdPN1fBfCS/OTUn4yf31s0bGPe7dnTXw+BQIvHbSGI90S/cAiuc5zB6XjXavgg6fCp4HDAIHngc6fCravQpmj8tO2MmypKhobvehusmNWoenW5ADAByPqCqajcm2YfGcMXj9l+fioYVlmDM+J2zgIvI80iwGFKZbMDzTigybcUAFOfHyb3srKUiF2yuj3uWF2yujpCC1X0pLB6/uAlrpc0lRIata0CoKXGB1l5BkoBUdQgghCbVgSgHuf68SDrcEnlNDtq+pTIWsMKRZDVgwpaBP5zVrbDYe+9E0PLXxIPbWtgVKLk/MT8Wc8Tl4+YvqPr0SXpQWXaCnN86/ambkOXBdco4UVYVODn431509otvPVJVh84FGWI0CFJXBK6uQVRYopSzwPDYfaMTPzh8T9+vBGEO7T0GbR0KHr3sfGz+HW8KGvfX417cn0B5hnN+y+eMxqSB8MCrwXKCggN7K3VAxkEo5+9+nUBkknQsfAqetqvbl6i45vVCgQwghJKFEkceSucX489p98MkMoqCC79yXLysMAs9hydziftmqMmtsNmaMyMCqzYdwtLkdIzNtuGX2GBiNAkoL0/q0qWGGTUsej1Q8wCBwyLB1X3kJXjUTee5UXyAAHM8HrpiHwwGYXNh9W5S/RHGqWURLu0+r5gaAYwyMMaSYhbgLM3hlBW0eGe1eGUqY5T5ZUfHF4Wasq6zD54eaAvlT0di8v6FboMNzXEIaeQ42A6WUc1G6FWDaVlU9Sj+s7pLTCwU6hBBCEs6fXLzykyo4PRLkztWGNIsBSy7sv0pLqzdX4clPDqKtQw5Uf3ru34ew9MKxuHl2ca+uhKsqi/m+apgGlz3dHmnVrKcgB9BOLtfsqu1WDKLZ7UO7V0GbVwrZesgAuCUVHocHqWZD1IUZVJXB5dMKC3il8KsyB+tdWFtZi4/31KO1I7oqa111eLXjcxwHm1GrmJaoRp7JEs97pj/FOt9LJ+Xhth6OyTrHEZIMFOgQQghJitLCNJQWpqLypBM+WUtGLi1MRanOSkJfWL25Cg9/sDfkBF4F4OiQ8fAHewFoAVo8V8K3HmwMrAb5t8QV56ZEXA1qcnmh9BCTKKo2rquIq2adx+TRvY8OABh47Uq63nahdIuhW5ATTGVAm0dCusUQcd4eSYHTI2llqsMEa83tPmzYU4d1u+twqKFdd0ymzYgCuxmVNd0rqnWVl25Grt0Mq0Ho84T7ePrDxPOeCdbXQVI88/1gd12PFfO4znF9VYGRnF4o0CGEEJJwWw82YvlbFXB5ZeSkmmEUePgUFfvq2rH8rYo+T4yWZRUrNhyIeAK/YsOBuKo/BT/XDKsx8Fz31LRFfK67dMoh69l1womLSrpf8Q6smnX2J1HQWSLbKKDDp0AQOBi50PwdDoDCGDjGdLcLSZIS9jXyU5k2ritFZXB5ZDg9EqQwEZxPVvHZoSasq6zFtsPNuo9lEDicV5yN8sl5mDEyE58dbMC97/b8Wp01Ih0ppr49rYm3P0y875ng+/cmSIpVvPM90eoGOECAFlx3RTk6JNko0CGEEJJQqsrw9KYquLwy8u3mwNYhMy8g386j1unF05uqcM6YrD678v6vb0+i3Rs5ob3dq+Bf357EVWcOi/q4vXmuHBdd/kmkcTfPLsaiWaNDVhQunZSHc/7rY21bm6j1LvLn7/RUDOKlL45GNaeXvjiKGWOyAGhNPds8Etp9+qs3jDHsq2vDul11+HhfPdo8+mWxJxWkorw0H3Mn5CDVfGrF6MsjrVHNafOBJpSXFUY1NhHi7Q/T278fvQ2SYtWb+fpzyXieg4HXqh36g26h83tV1Q+6CUkECnQIIYQklD+hPcNq7JYfwXU2nYw3oT1e3xxriXpcLIFOb57rsAxbVI/R0zhR5Ltt+4m3GMS++rao5rS3zolWty/Q1FNPQ5sXH+2pw/rKOhxt1r9in5NiwiWTcjG/NB8jMvVPdr1yzxXXAMAj9a6vUCx60x+mN++Z/riI0Jv5huaSaWW9A8+lHyswktMHBTqEEEISqtntg6QwGAUejDF4JK1vhsjzMBt4mAQeDpVFndCeCBadHjm9GecX/Fz1RHquF4+P7qp7tOOChdvWlmY1RNxWlaHTW0ePxSCiub37c/JKCj492IT1u2vx9dEW3a1pJpHHBeOyUV6aj2nD0yGEOSE3CDxsJhHj8lOxfk99j3MaFiZQSobg/jDBhSAAbQVNFNRAfxi9gg/xvmf64yJCb+Y7kCswktMDBTqEEEISKtOqlU1u7fCh1e2DV1ahMu1qt0nkkWY1wsBzyLRGd1KdCONyUxI6zs//XH2KCjPfPUjyKmrY5/r7t3ZF9Ri/f2sXVl1/ZkzzAvS3tfWUKH/ltEJsPdTc47Evnpgb+DNjWlL8uso6bNxXH7bnTVlRGr5TmofZ43NgC5NLI/AcbCat1425M+gsSjf3OB8AyLObohqXCP7+MGKYRROe00oq6+We9OY905ugI169mS8Qf9BNSCJQoEMIISShSgvtyEoxYtcJZ0i1JZUBsk+F2+fB5CI7SgvDN3dMNKcnupLF0Y7zKy20ozg3BXtq2pBv50OusjPG0OqWUFKQqvtcq5v1K43FO06P3ra2YF0rd43LTY3quKOzU1Dr9ODD3drWtBOtHbrjCtLMuGRSHuZPykNhukV3TE+9brYcaIpqTlsONOGas0dFNba3gvsY6S1IqRH6w/TmPdPboCMevZmvXzxBNyGJQIEOIYSQhHN55bAlZVnn7X1p18koK5xFOc6P5zksnlOM5W9VoNbpRbrVAJPAw6uoaHVLSDEJWDynWDdfYniGFbtres6JGZ6RnC1ZepW77Kbotu7994f7cahRPwCzGATMGZ+D8tI8lA1LA98lcFEZQ1V9OzyyggK7BWeOTIcQZoUCAFjE4sSxj0uESH2Meso96c17JhFBR6x6M9+uxxmbm4LMFCMyrcYB3S+IDB0U6BBCCEmoihMOnGjxgOe0ssaAFtz4T2s4DjjR4kHFCQemDk/vkzl5pJ6baMYyLtissdl4aGFZIGhwqAwGnkNJQWrEcr+Lzh2Jdbvrejz+onNHxjynngRX7kq3GCDyPDyygv1h+tl01TXI4QBMG5GO8tJ8XDAuWzfXiec4VJ504OUvqnG4wQVZRVRlkSfmpWLtrp5fp4l50a1GJUJvc0/ifc8kKuiIVbzz9dt6sBFPbTyIvbVtkGQGg8hhYn4qfjV3bJ+WmSenHwp0CCGEJNQ3x1q14gMCB57jwFhQHxdOu6IvKyq+OdbaZ4HOsCjzPKId19Wssdk4Z0xWTA0cPznQGNWxPznQiHPG5YS9PdbGkf7KXW0eCTkpJqjQfidGgQ/bZDSconQLykvzcMmkPOTZu792HMfBahRgM4n4troFj6zbF3NZ5LIo3yPRjkuU3uaexPOe8d+vN0FHvOKd79aDjbjt9R1odvmgMm3ljfNy+OJQMw7U78BjP5pGwQ5JGgp0CCGEJFSg7QvTTnQ5DqeWc4DAMk+UbWQSYkJ+dFt5oh2nh+e5mCpdtfui274XaVysjSMZY/jySDP217YhxWToVhWN66mNfac0i4g/XjkZpYV23epfFoMAm0mAzSiC5zmoKsOqzYfg8srIs5vglRjafTJEnkee3Yg6py9sWeRsmwkCp99w0k/gtPyViuOOmE7Ce6u3uSexvmf84g06eivW+aoqw8Mf7EF9mxehLZYYFAbUt3nx8Ad78K8l59NWNpIUFOgQQghJqGkj0mEQeMiKCp5n4IKiHAYGRWUwCDymjUjvszllp5oCW4vC4TltXF+ZPjwDL31eHdU4PdE0jpwxIgOrNh/CoUYX8u1mXDW9CEea2uFTVKSatVMAxhjckgJnh4xod+7dfP5oTC4KPeE1GwSkmEXYjGK3ktH+ssgmUcDRpg54ZSWwymcSBaRZIpdFFgUeihx+cjzH4f53K1Hv9EYV8CVSTwUfkiXeIKkvVZxwYG9NWyDICbneAe2ax96atj7dxkpOLxToEEIISaiyojSMz0tB5UknfLIKgefAgQsEOQAwPi8FZUV9d5KWbTPBLPJwRziTN4s8sm19F+gsKCvA7/7xbcQFFK5zXFfRNI687bVv0OjyhayErN5yCOcWZ8HAc3D7FHgkBU6PDDlSBNiFwAHlpdqcDAKPVLNWDlqMUFCg2e1Du0+Bu7NIhcBz4HjtRNcjKfBKCqwmUbcscmuHBLNBgKyouqs6PACFMRxqaEee3Rz1ljiSfNurWyB1vre4wH80HNOCHUll2F7dQoEOSYq46vqtXLkSo0aNgtlsxtlnn41t27ZFHN/a2oolS5agoKAAJpMJ48ePx/vvvx/XhAkhhAxsPM/hzktLkGYxgDFAUhh8igpJYWAMSLMYcOelJX26VWVCbgp8kfY+AfApDBNi7KPTG3vq2sD38K8wz2vjuorUOJIB8Egy6tp83QIDhQGfHmxCs1vCSYcHzW4ppiAHAH45ZwwyU4woyrBgeKYV6VZjxCAHANItBngkBSpjgdwtDtr/RYGDyhg8koJ0i6HbfTOtRoid7xW9d4wKLWDKSTHBbBDA8xzMBgH5dhNcXgVPb6qCGuNzjIWqMlQcd2DT/gZUHHck9bEGm1qH59Q3XX95XJhxhCRQzCs6r732GpYtW4ZVq1bh7LPPxooVK1BeXo59+/YhNze323ifz4dLLrkEubm5+Oc//4mioiIcPXoU6enpiZg/IYQQ0qM1u2p7PAFVVYY1u2r7bBvSN8daofSwVUxRoVu0oWvjSMYYVAYoKoOsKnB59Zt2+ukFNxaDAJ7T8m/OHJGBT/Y1wBXU/DPVJOCWOcVYctG46J5gF4EcjeASfP7vg2/voiQ/FUrn8zMKHBR26hAcGCRV2wJnMYZWeuM4DunWyFvieivWHKnTTYFOgYrejCMkVjGv6Dz66KO4+eabsWjRIkyaNAmrVq2C1WrF888/rzv++eefR3NzM95++22cd955GDVqFObMmYOpU6f2evKEEEIGHn8CsqNDAgethLD/iwPg6JDw8Ad7+vTK94lWNxgHGHgtFydQ6hra9wZe+0avk32yyD1FORHG+RtHemQFkqLCp6iQFRWMMbS0R9/01GIQkGIUkGYVkWoSMLHAjt/On4C5E3MxNtcGs8hD5LRtfZML7Zg+Qj9fqCetHRIsBgECz0FSGVTGOoMzBknVyjFbjAJaO7rPfU9tG4TOin1ehUFWWWdAx0JyivRKg5sEHpLKdLfE9ZY/R2pPjRM2k4jcVBNsJjGwZW7rweiq6g1l00dmwNC5GscYThW6YKcCWwPPYfrI+N5XhPQkphUdn8+Hr7/+GnfeeWfgZzzPY968efjss8907/POO+/g3HPPxZIlS/Cvf/0LOTk5uOaaa3D77bdDEPQbk3m9Xni93sD3TmdsDdwIIYT0n4oTDuyvc2lBjoEPKUYg8AySrGJ/natPE5D9nezBcTDyXCARmusMehTGwDGm28k+Warqe24WqjdOVlQUpZtRlGHBwXoXMq0GtHRIkBQGgQfcPazm+J0zJgMPfK8MB+va4fD4kGY2YlxeCvbWOHHX2xVo7ThV7U2WVXx+uAV7X9mOldecEfNqRabVCJtJQIpZgKNDgldWA6+/xcDDbjEATBvXVbPbB4+kRiwkwRjgUxQ4nRJ8igqjwCMnxQivopVe1jtub0STIxWuitzppKwoDRMLUlF50tlZWhohVf14DphYkNqn+Xrk9BLTik5jYyMURUFeXl7Iz/Py8lBbW6t7n0OHDuGf//wnFEXB+++/j3vuuQf//d//jQceeCDs4zz88MNIS0sLfA0fPjyWaRJCCOlHO6pbISmnihAE48BpV/UVFTuqW/tsTgumFCDVYoCsMKhMhaKqkFXt//5O9qkW/U72yVLn8PY8KGic2yejzunBsZYOtHZI+PFZw+GVFFQ1utHcLqHNI6PVLfeYi+Q3Ic8OnuMwPj8Fs8fn4LxxWRiZacVfPtwXEuT4MQAtbgl3vV0R82pcaaEdxbkp8MoMIzOtGJlpw7AMC0Zm2jAy0wqfzFCcm4LSwu7lve1mEW5f5OCNAahu7kB9mxetbgn1bV5U1rTheLM77HF7I1KOVNctc6czf75eTqoJBoEDzyHwZRA45KSa+jxfj5xe4ipGEAtVVZGbm4tnn30WZ555Jq6++mrcddddWLVqVdj73HnnnXA4HIGvY8eOJXuahBBCEoQF7wvTw3UZ1wf8newBwKcAstrZxV7VvgcQsZN9MqTZoltlMJsEVDe5UevwoN0rg3Xu+fnXNyciVpHrybnFWciymTAi04qCNAtSzQZUnHTgSOOp7Xv+Jq/B5/JHGt349nhrxGPLsoq3tp/Akx8fwFvbT0BVGRbPKUaKSUBdmw/gAJtRBDigrs2HFJOAxXOKdU94DzW0R9PaR5dHVmEUuISfSHfNkeoqmVvmBptZY7Px8/NHw2oUTm1ZY4DVKODn54+mXCaSVDFtXcvOzoYgCKirqwv5eV1dHfLz83XvU1BQAIPBELJNraSkBLW1tfD5fDAau3/Qm0wmmEx9V+KTEEJI4kwfng6R56EoKniBhVzxZoxBURhEnsf0Pi4ne6Qx/Akz67y9L11z9nC89c2JHseVl+ZBVrWAprndhw176rC2sg6HezFfk8DBYhSRZg2tcra2ojbwGnVZqADHaSeprHNcuHyd1ZursHJjFdo6JKjQrqje/14llswtxkMLywLJ+w5V21ZWUpAaMXm/xtGBKPuY6tq0vxE+nwKjUX+7fDz8OVI+RYWZ735cr6ImZcvcYLT1YCNe/qIaIs+hKMMCntOq7Ll9Cl7+ohqlhWkU7JCkiSnQMRqNOPPMM7FhwwZ873vfA6Ct2GzYsAFLly7Vvc95552HV155Baqqgu+so7l//34UFBToBjmEEEIGt7KiNEzI1/roSKoKkecDJ6qyqoIBmJDft310fD4Fr311POKY1746jvuuKE3oCXEkXJRn7rKsYtP+BqyrrMW2w80Rc1WiYTcLSDEZdE/C26WgLWLs1P+6roeEjAuyenMV/rx2HxRVKyMtdjZpdbgl/HntPtz+nQl4cdFMVJ50otntQ6bViNJCe8QVl6J0a9SBTtejaO85hlWbD+H/zYuvWpwe/1a8PTVtyLfz3YL5VreEkoLUhG+ZG2yCc5kK0iwhr1OahVEuE0m6mNfoly1bhtWrV+PFF1/Enj17sHjxYrS3t2PRokUAgBtuuCGkWMHixYvR3NyMW2+9Ffv378eaNWvw0EMPYcmSJYl7FoQQQgaM4H35PMcFKmQpKgPP9c++/FWbD/XYL8Z/QtxXXvqiOqpxt7+5C/e/uxufH4o9yEkxAOlmETajgAyLARPzrDCKYti8leBVti5FskICDb3VOFlWsXJjFRSVwShyEHkePMdD5HkYRe19sHKj1tOmbFga5ozPQdmwtB7fB5dPzg8ZwwV9dcPpDzjanNjVOp7nAlvxap1edEgKVJWhQ1JQ6/RG3Ip3OgnOZQIHdPgUtHkkdPgUgAPlMpGki7mPztVXX42Ghgbce++9qK2txbRp07B27dpAgYLq6urAyg0ADB8+HOvWrcNtt92GKVOmoKioCLfeeituv/32xD0LQgghA8qssdl47EfT8NTGg9hb2xboMTIxPxW/mju2z7eqRHuim+gT4nA6fAoONbqiGts1QMtNNSHfbsbOE44e7+uWAF6RwRjglRW4GmVk2YyBk3BVZSGrKwvKCnDnmzsjFjQwChyunFrY7efv7qxBW4fU2RA09Doqz/EQBRVtHRLe3VkTU6+iffUuWI0CnJ7uBRKiNTLTFvd9w5k1NjuurXinE38uk09RUePwwCsrgWp7JlFAVoqRcplIUsUc6ADA0qVLw25V27hxY7efnXvuufj888/jeShCCIlb15O4nrbIkMSaNTYb54zJGhC/g2EZloSOi4ekqHB5ZLR5ZMiqCrMY/RY5k8jjgnHZ+E5pPqaNSIciM1z21y3oGo/482i6/DQk38Z/s16zy9HZth63iIX7/Z1odUMFIIb59fIcoCD2XkXNbh9MooC8VAGN7T4oQYEf37k1To//dRB5DrfMHhPTY0ZrIL3HB6JMqxEqYzjR0gEAWiVGXvvdeCQFJ1o6kGbR30ZJSCLEFegQQshARx3LBwae55LSkT5W+anRFbiJdly0VJWh3acFN56gvBbGGIrSLABaejzGheOzsWz+BNhMp/7JNpoEfG9aId745mTI2OAgxyRwGNtZ0lnuzJUyiRzq2nx4+IM9cHpktHtlZFiNMAo8fIqKnccdkBQWNieG5wBJVnVXZfy9ilSmjev2WjBtN1msvYr8if82k4icVBNaOyRIigqDwCPdYkB1szuw2tM9yAOunjEsqXlXA+U9PhCV5KdCYSywnZHvjLg5DgDP4JMZFMZQkp/avxMlQ1bf1dEkhJA+Qh3LBwdVZag47sCm/Q2oOO6IuTdLLDZF+TuPdpye4Ofz1ZFm1Dk8qG52o6HNGwhyap0e/N/nR3HD81/irW9P9nBEzQ/PGgGbSYTAc0g1G1CYbsGILCv+++rpuHbmcIhdogqBA6wGHiOybOB5HhajgFSzARajAJ7nkWYRsa/WBYdbQl6qCYwB7T5te5vZcOpYRkHrdSLyHAwCB5Oo/Z9Bf1Wma6+ikNemF72K/In/LW4JAJBhNSI31azlfQAwigLy7SYIXYIrkedw7czhePD7U2J6PJI4e2rbIHCAKHCdJd0ZGGNQGYOsaj8XOG0cIclAKzqEkCGFOpYPLOG2D/b1iptTpwFmb8Z1tfVgI57aeBAH61zwKQwiDwzPsuGamcMxscCOLfsbsG53Hb6JsUmqUeQwbVga0m1GWAxCt+aUD35/Cu67ohSrNh/C0eZ2jMy0oaQoFcvf2BW2xwtjWvU7gRdwtNkNr6wG8ib8V9wZAI7jIXR5PFlVw67K+HsV/XntPvhkBlFQA1vLZIVB4Lm4ehX5E/+Xv1WBWqcX6VYDTAIPr6Ki1S0hxSTgoYVlmDEiI+R1uGX2mD6roEf0Nbt94DkehWkWNLV7Q95rFgOPLJsJbkmhHB2SNBToEEKGlFg6ltN2k+QKF8zMHpeNl7+ohqvLtin/ittDC8sSHuyU5qdia1VTVONiwRjDx3vqcd87u9DuU2A3G5Bi5uCTVeyrdeKef1VC7kzG7sr/7oy0jqUqDA0uHwozwm/3MhqFkNLJFccdEXu8dEhaQrijQwaDVgnPPxd/vx5Aax/BB/XA86/KpFnDr8rcPFtryurvo6N0Ps80qwFL5hYHbo9VtIn/iSwhTXrPv+3QKPIYlWWDR1ID2yjNBh4eWYVBUSlHhyQNBTqEkCEluGO5oiiocXrhU1QYBR4FdhNMAg8HVflJOv/2QZdXhsUgQBQ4MAbsqXHiyyPNMAo8RmRa+2zF7bKphVj97yNRjYuGR1LQ5pHR5pHw1KYqtPsUZKcYIckMze0+OD1y2HLWwzIsmD8pD6rK8L+fHY34ODIDdlS3YmoMzVV76vHi9mqrVto2IkAF0+2VI6kAuNhXZW6eXYxFs0bj3Z01ONHqRlG6FQumFMS8ktMVJf4PPqHvRRMsRgGAFjxTvyHSFyjQIYQMKf4riIcbXXBLp65Ot0NBi1uC1cAj3WocklcQ5c4k8USeXMbDv32wxe2DrDA4OqTAdhWB4+CVVfA6XVCSueI2dVg6MqyGQJ6HngyrAVOHpYe9XVZUuLxaYQGpc4Vmf60LRxtd4MDheEsHOqTuKzcAYDEIuLgkF+WleZhUYAfHcXjty8hBjp9XDj9nPT1t9bIYBbh8SqB6GYfO5HB2anWJ5wCrQYBbUiB3/u7SLAYsuTC6VRlR5GMqIR3LcxtoK7FU3TG8aLYdUr8hkkwU6BBChpTSQjs6JCUkyAnmllSYJGXIXUFcvbkqsF1IhVZp5v73Knu1XShelSed2H3SiXavloAfXFJWUlQwAF5FhUdSO6/wnpKsFTdV1d8+FsynqFBVFnLSxRhDu0+ByyPD7TuVv6OoDN9Ut+CVbdVobA8fiFgNAjgeuPPSiZhVrG2v4jkOVpOA7Udbo5r7x3sb8Is5sW3JirTV69wxmXj0wwOhd+haphrA8EwLahwe+GStYlZpYSpKCwdWkNHfqLpjz6jfEOlPFOgQQoYUn0+JeNUeAFrcEnw+BWbz0PgIXL25Cn9euw+KyiAKHMTOrUYOt4Q/r90HAH0a7DS5vHB6JDDGYBB5+DNAOA4QBR6KrEJRGSRFhQWhgY5XUWHguYSvuL27swYenwKBB1Q19LyeA8DzgMenBMom+7emuX1ySN+W6iY31u2uxYe769Do0g/GjAIPu1mE3SxCZoDHJyMnxQyrUUSKWYTNqBUVCHf/ruqc3riec7itXv/acRKMaQUTVKYFoP6ta/4YT2HAsRYPCtLMgRyqfXXtScuhGoyCt2f2Va7ZYEXbDkl/GRr/yhNCSKc/vr8n6nEPfr8sybNJPllWsXJjVVCfCm2rGs8BPKfCJzOs3FiFRbNG99k2tha3dGplxJ8D0rn9KTjEUNTQFZZk7tn3N7M08hw4XiuR7J+TtmtL6+lxsKENx5rdga1pANDmkfDJvgasr6zF7hr9MrgcALtFRJrZAJOo5cUwMLS4JEzMT8FFE3Nh6PL6mwzR/T6iHadHb6tXus0AnuegMm0FAuBCfj9eWfsdZVpFmA1aIEpVC0NRdcfYDcRth2Too0CHEDKkHGluT+i4ge7dnTVo65AgCqeCHD+e4yEKKto6JN0Gj8niP5FWVK0ZIMOpFQP/F4NW9ctsFPtkz35wM8vgfiuss8OkwrQmmXazAZKirTh9eaQZ6yvr8O+qRkhK98ICPAecPToL4/NS8OHuWi0/h9OeoaQyODskpFlE/Pqicd2CHAC4alohHly7v8e5XzUtugIJ0cq2mWA3i50FEwCR154Lg1ZwwP/cjGLoKQJVLTwluLojOKDDp5yqJmbk6XUiZICgQIcQMqSMyrTh3+i5jPCoTFsfzCb5/CsVYpi4gOcABfoNHpMl22aCxcDD6VFCfu4PeACtoWVxbgrqnZ4+2bO/YEoB7n+vEq1uCVKYgs6pZhGjs2xYtakKH+2pR3O7/tay4hwb5pfm4+KJuci0aVvspo9Ix6tfHsPRxnZ0SAoMPIdJhfaIz2dmcQ6AngMdbVzilBbaMakwDd8ec0BRVfiUU71NDJ1bCy0GAWZj9+CMqhZq/NUdfYqKGocHXlkJvIYmUUBWihFSP75OVCCBEA0FOoSQIeWey0rw8rbqqMYNBcErFVxnmeDgLVnaz/UbPCZLSX4qeJ6HFmLpMxoEvH7zOdhX7+qTkzFR5HFpaR7+/uXxsGN4AL946Wvd29Iths6qafkYm5sCQCuyYDWKSDWLGJOTgu9OLYrp5LKsKA1lRXZUnHBGGGNHWVFiVwSCK2G1eWRk2IwQOA4KY2jrkCCpDOlWIzidynjJyqEabDKtRqiM4URLB4DQghseScGJlg6kWQz98jpRgQRCTqFAhxAypJjNIi6emIMNexvCjrl4Ys6QKUQQvFIhdwY6fv4tYukRGjwmw57aNqhhesj4qSrDvnpX0rf1qCpDu0+Gs0PCloONEcc6PHLI9yLP4dziLJSX5mHmqEyIgpZ7YzUKSDGJsHYWFfCLNQeB5znceWkJbnt9h27BgXy7CXdeWpKU4K9rJSyvqgUwZcPS4OiQUOPwgjHWrQdPtDlUQ31FoSQ/FQpjQblxpwpugNfyvRTGUBJjA9reogIJhIQaGv/SE0JIkOdumomf/e823WDn4ok5eO6mmf0wq+QQRR6XT87Hy9uOdduQ5f/+8sn5fdpPp8nlhdsXfjUHANw+BU2u+KqJRaPDp6DNK8HtVaAyht01Thxv8UR13wl5qSgvzcOFE3ORZjEAAMwGASlmESlGMaEn7LPGZuOxH03DUxursK+2LdDcdkJ+Kn41N7lX4MNVwvr8UFOv+p70tKIwFIKgPbVtEDhAFLjOPCcWuLAgq9rPBU4b11c5OlQggZDuKNAhhAxJz900Ex6PjD++vwdHmtsxKtOGey4rGTIrOX6qyrDzhCNwktUVB2DnCUe3/jDJ1NTugxzUjBI4VYzA/2dZZWgKkwMTL72GngDQ3O7D6s2HojpG+aQ83H7pRABavkqKSSsJbRCSFyj2Z+ldvVWo3vQ96WlF4dqzR+CTvfXYecIJSVFhEHhMKbJj6UXjBtVKQ7PbB57jUZhmQVO7F175VJ6TxcAjy2aCW1L6NEcnuEBC8EocQIUkyOlraP2LTwghQcxmcUiUkI6k4oQD++tc4DnAIPJQ1FOlnAWegySr2F/nQsUJB6YOT++TOTk8p/oYBQdfXQOx4HHxCtfQ0yer2FrVhPW7a7HtcDN62EkX4JEUpJoNSDWfKq3cFwZa6d14gq+eVhSqmzvw8Ad7Q34XXlnF1kPN2HniKzx7/YxBE+xkWo0wCByMIo9RWTZ4JPVU1TUDD4+swqCoPeboJHJ1y18gwRgmKKdCEuR0RIEOIYQMYjuqWyEpKsTOkyOe03rE+C/oCjwHSVGxvboFPMf1yYqBoJPE3ptxeryyFty4vKcaejLGsLe2Desq6/DJvnq0dcm5iUZxjhU5qaa45zWUxBp8RVpRAIB2rxQ24HR5FSx7fQe23nHxoNhWVVpoR3FuCvbUtCHfboLFKACdzW+jzWVKdNEAf/DlU1SY+e5BOhWSIKcjCnQIIWQQY53nhCrTmocGd7nnuFOBz0ufHcXqzYf6pArT1ChPjqMd56eqDC6ftjXNK53KAWpo8+LD3XVYv7sO1c36ZbStRqHHvCEASDHTSWC8Iq0otHsl6LQiClHr9OKbYy04c2RmkmaYOMGV62ocHliMQqByXYdPQapZjJjLlIyiAaHBFx93IQlChpK+y04lhBCScNOHp0PgOMgqC1wt54KDn86tbI0uD2wmEbmpJthMYuCEamsPlcjioYTpUxPvuA6fgvo2D442u9HY5oVXUuCVFGzYU4/f/3MnfrL6c/zPp4e7BTkmkce8klz85QdTcOH46E4aG5NYICESVWWoOO7Apv0NqDju6LFq3UAUvKLQVYs7um2KL3/Wc2n4gWLW2Gxce/YIKIyhprUD1S1u1LR2QGEM1549Imyg0nWLn9kggOc5mA0C8u0muLwKnt5UFfN7wB98pZgE1Dq96JAUqCpDh6Sg1ulNWjNeQgYyWtEhhJA+lOiKU6UFdhgNPCSvtlrBAv8JVZRugSBo21mSXYVp/a66qMeFu3ovKyraOrem+QsLMMaw64QT63bXYtO+BrSHWaGZMiwN5ZPyMHt8DmwmEQLPYcOe6OYUzapPom092NgvVdcSLdKKghzlSXtrAvK2+srWg414+YtqiDyHwnQLOE7ro9MhKXj5i2qUFqbp/v6SWTSgN4UkCBmKKNAhhJA+koxGfntq22AWeXgkBToX0gEAPAf4FMAStG0/mVWY2qXogoWu4/yFBdo8EjqCAo5apwcfVmpb0060dugeqyDNjPmT8nDJpLzOk04ONqNWEtpiEDAswxLVnHJS+nbr2taDjbjt9R1obveBsVOFJL447MOB+jY89qNpg+bkNHg7V9fS1EqUgc6UAVSQIZJwhRcAII2xiBcRElU0INxFk/6s4kfIQEOBDiGE9IFkNfLzl7k1i0LYFQ6e4yCrKvzJ0n7JqsI0uSi6Jon+cR5JQZtHRrtXhsq0E+IOn4LNBxqwrrIOO4616t7fYhAwd0IO5pfmoawoDTzHhe13U5RujWpO0Y5LBFVlePiDPWho84KDFijwnLYgp6oMDW1ePPzBHvxryfmD5iQ13IrC9OHp+LyH6ncCB/xqdnHfTbYXerMqk4iiAT1dNBloVfwI6S8U6BBCSJIls5FfptWIdq8EtxRmOQfatiGR7371OFlVmE5G2Zizqr4dx5rdga1pKmP49lgr1u+uw6b9DfDoPCcOwBkj0jG/NB/nj8uGxSBA5HktuDGJMIZpjFo2PLqTvmjHJYK/NDiYVlRCVlhIIQkw9Hlp8ESYNTYbM0dl4t2dNTjR6kZRuhULphTgvnd24eVtx8Le78dnDYfR2HclvXujN6syvS0akKyLJoQMRRToEEJIkiVzT/64bFvEIMdP5EJXdJJZhWnXcUdU4yqOtUKaqeJESwfW767F+t11qHPqFwMYlmFBeWkeLinJQ25nsGgzCUg1GTpL+0Z2pFG/GpveuL6q+rWjuhU+RQ3kVQUHOP6VD5+iYkd166AKdPRyjt7Yfhy/mqut1rz21fGQnB2R53D1jGF48PtT+mvKMefO9WZVJtIWv1a3FLFoQDIvmhAyFFGgQwghSZbMRn7Pfno4qnFHW7woyuCjPqHqDYX1HHgBWu7NL/72NQ42uHRvt5kEXDghF/Mn5aG00A6O42AyCEjV2ZrWkxpHBzpjiLC4znF9xZ+TA5yqlOefCActsV37GjwV2KLJObrvilKs2nwIR5vbMTLThltmj+nXlZx4cud6uyoTb9GAZF40IWQookCHEEKSLJmN/I42t0c1LtUkwu2V+6QKU5rZENW4I83dgwoOwFmjMlBemo9ZxVkwdW5Ns5kEpJoNYbem9aQo3Qqe00661aAVE0Ar1sB3VszqyxydVMup18kfEAR/rzduIOuacyQKfKASmayoITlH/2/euP6eLoD4t4H1ZlXGL56iAcm8aELIUESBDiGEJFkyG/mNzLQF/sx1XbLgTp0wX3POCFw8MS+pVZgkRYXLI6PVK8d8X4PAQeQ5pFkMuPqs4ThjZCasRm31xmIQul29jtWCKQW4/71KtOr0c/EHPulWAxZMKejV48QiM8UIgUegWp7ewo3Aa+MGA3/OEQfAIPLgoP3OOE77XpLVAZVz1NttYIko5Rxr0YBkXjQhZCiiQIcQQpIsEVd/w7ll9hg88fGBQGNQvVUBkefwq9nFSdkepKoMLp8Ml0eGp7NcdF6qOar7cgDsFgPSzP+/vXsPb6s688X/XXtvXSzL8iWOZTs4JHGuNrmRNJCUQoAQU9pM25k5ZVoeyjCF+bWEM6fNtDPQnimlnXJpOy09bQpThk57ZoaBOW1hyqVxaCBQbgUCgaA4DnEIudlyfNXNuuy91+8PWYody7HsSLIkfz/PYx4sLUlLzra8373W+74abJoCCKAnEMOv9hzHn6ycA8sUV29S0TQFa+ZWYNeBU+OOWTO3Ato5vOZk8zyqS22oKLFgYCiWsjS4qgAVJRZUl9qmPKdc2nt0ADHDhKaIZJCTICCgKgKxPMo5ysQ2sFyXcs7mRROiYsRAh4goB7LVyM9qVXHt2vOS1axSrQpcu/a8jAc5Q1ED/kgMwYiRzCExTInXj/ThUHfqnJsz1ZdbUWqzAgJQhYCiCMxyCnzQG8KBLn9Gcwx03cSeowNnHbPn6AB03ZxSsDPVPI+m+nK8fWwQumEgapgwZXwbnVVVoKkqmurLC+akVSbOucc7xxdnjJtmmdoGlstSztm8aEJUjBjoEBHlSLau/iaqVWW7mlVUNxGIxFdv4n154g6fCqDV48Xv27zoT7E1bDyKUKCpynDuTPxnkK0cgyfe6YRv6Oxz8w3F8MQ7nfjUhXMm9dyZyPPwhwWqrCpUIWBIiaGogTK7VlAnrasbKqApCgzDhKLKMasNhhEvc746D1ZzgMLdBpatiybZNtkVT6JMYKBDRJRD2br6+7EV9Xi/J4B3TvgQ0yUsmsCKOS58bEX9OT2vYcp4cBPREYmdbkg6GIph1wEvWj1evJfmCs6ZhnQTFWec6GTr5PLYQOiszSqBeJ7OsYH0ylAnH5PhPI+IGX//TfWuvD5pTWX5nHIsqXXCc9KHmGlCU5RkpTvdjJfRXlLrxPI5+VENrJC3geV6y9y5msqKJ1EmMNAhIipwZ64oJCpdHewOTqmBoJQSoaiBQERHKHp6a1rMMPHHw31o3d+FVw/3wUgROVhUgWqnDZ2DEzcNNYzRj8/myaU5UZQzyXEJmcrzSNVg81zyhaaDogjc/tFl+PJ/7UVvIAJ9uEdQoj9QdZkNt390Wd6cjBf6NrBcbpk7F2xwStOJgQ4RUQFLrCj0h6LQDYnBoViyKIFVVRAzzLQbCIZj8eAmGNGTQYyUEu91B7DT48WuA90YHGf715LaMrQ0uXH50ho8/tYJ/PKVDyacezhmYChm5OTk0mVL789duuMSMpHnkepq92/eOl6QV7s3LKzGTZfMx/bnOuALx49FCKDMZsFNl8w/5/ej62ZGA8JC3QZWKNjglKYbAx0iogLmOenD/pM+BCPxbWWqIiCU+IpORDcR0YH9J33jrihEdRPB4a1psRGlv/qCUfy+zYudHi8O96Tu1TOr1IqrmtzY3OzGvFnxMtdWTcHaeZVpBTrzqx0YGMpNbx9VSe9kON1xCeea51FsV7tfPtSD//jjUWiqwJyKkuTq4lDMwH/88Sia68un/H4efKED23d3wD8UgwlAAXDnkx5s3diImy9tnPKcC20bWCFhg1Oabgx0iIgKWG8gMnzlXEJT4ycSphlf0VEVQDckfOEYegOR5GMMUyIQ1hGIjs67ieomXu7oxc79XXjt/b6UOS0WVeCShdVoaa7FmvMroSrxssFOmwanXYNNU9EfjMFlV+ELG2OfYJjLruLuP1sJRYicnFyumlsBm6YgqptItTlNIB6krZpbMannPZc8j2K72j3e+wGAcinP6f08+EIH7t3RDsOMH+facOPXwVAM9+5oB4BzCnYKZRtYujK98jVVbHBK042BDhHRFORLBaH+UCyeVyKAmCFhjqgvHa9mFp9rbzAKfzheDjoUPd3QU0qJA11+tHq8eK69G/5w6mafzfUutDS7sXFxDZz2+J+OEquKMrsFpdbRDT2b611Y2VCJ1w73IZKiQYxNVbCyoRLL55Tn7Ge2fE45FrvjifJCAiMXbkwTgAAWuyefKH8ueR7FdrU7W+9H101s390Bw5SwagKKiP/jKQJQhImoLrF9dwdu3DC/4PKasiFbK19TUaiV7ah4MNAhIpqkfKogVFFqAQRSNpw0JYDhvixRw8Qp/+lVnVP+CJ7Z78XO/V4c7UtdaaymzBbfmtbkRkOVAwCgKQqcdg1ldg2Wca7SKorApYuq8dJ7PSnvjxkmLl1UndPAcGSifF8wCilPN1hVVYFZpdYpJ8pPNc+j2K52Z+v9PPFOJ/xDMWjq6SAnIV6i3IR/iqXBi022V74mq5Ar21FxYKBDRDQJ+ZZTke6V0PISC8IxAy8d6sEOjxdvftCfcguXXVPwkcWz0dLsxqqGCijDJyYOazy4cZyxepOKaUo8/NpRpIi94vcDePi1o/j8JQtyGuxsWFiNH356FbY/dwiekz5EDRNWVUFzvQtbL194Tv9uU8nzKLar3dl6PycGQjABaOP8KBUBGMPjZrJ8XPkq9Mp2VPgY6BARpSkfcyqkKVOu5oxkSuDfXzmCN48OIKynHrzyvHK0NNfi0sXVcFjjfxosqoIyuwanTYM2zlX6VN4+PoAjPadPOkf+JBLB1ZGeEN4+PoDVcyvTft5MEULAoiqQiL/HiQK3dE02z6PYrnZn6/3MqXBAQfw4TvVrNbxzE3MqHOf2Bgpcvq58sbIdTScGOkREacq3nArDlHjlcG9aY18+3DfmtlmlVmxZWYermtyoKy8BEH8fpcO5NyXWsVfl07FjX1cyoDkzhhCIV+GSw+NyGeiMtxp3oGt6VuOK7Wp3tt7PlhV1uPNJDwZDMSjCHHUSb0oTuiFR7rBgy4q6TL+lgpLPK1+sbEfThVl7RERpSicHIZblnAopJYIRHV5fGEf7QuOWfh6PEECpVYXTpsKmCSyfU4668hLYLCpmOW04v8qBGpd9ykEOAARjZ1RbkyO+zjYui85cjbNbVCiKgN2iotZlQyBi4P7nOybdMPRcJa52L6srQyiiozsQQSiiY1ldWcGVlgay8340TcHWjY1QFYGoLqGbZjzAMePbsVRFYOvGxhlfiGDkylcq073ylVjxvGzxbCw/L3eFSGhm44oOEVGapjOnIlUzTyAe+KRDUwSqnVY4bRoUISAh0ROI4b/eOI5rltehxJq5PwerGyrw769+MDy/M+6Uo8flysjVOAhgKGpAN01oigK7VZnWCmfFdrU7G+8nkUCfqCZmIH7SXu6wTEs1sXyU7ytf+VKpkmaWKf1l2759O773ve+hq6sLK1euxI9//GOsW7cu5dhf/OIXuPHGG0fdZrPZEA6Hp/LSRETTJtc5FTEj3szTHx7dzBMATvQPYef+LrR6vGk9V6XDApfdAiB+ZVURCqpKgWN9IRzqDmb05P4TK+vx9cf3IRwbP3nIblHwiZX1GXvNiSRW46KGic7BMCK6kay6ZtNUzHJas74adzbF1sclG+/n5ksbceOG+XnRHyYfJVa+7t3RjqguoakmlOGqa7oxvStf+VSpkmaWSQc6jz76KLZt24YHHngAF110Ee677z60tLSgvb0dNTU1KR/jcrnQ3t6e/D5TiZ9ERLmUi5wK05QIRHUEwjrCZ2ztCkR0PN9+Cjv3d2HfCd+knrfEokJVBVQhThdR0FT4wnrGT+4VRaCu3I73e8bPBagrt+f0am6VwwpTSpzoHwIAqIqAUOIrTuGYgRP9QygvsRRMhbOZStOUGV9C+mzyceUr3ypV0swy6UDnBz/4AW6++ebkKs0DDzyAp556Cj//+c9x2223pXyMEAK1tbXnNlMiojyQjQpCUkqEovGtaaGoMWo7mmFKvHm0H60eL1481IPoOFXTJuIqsYy5yJStrXaekz5EYiaqS63oC0VH5QwoIh50RGJmTreJLastgyHliNK78Z+FEAAUiaguYUiJZbVlOZkPUbbk08pXPlaqpJllUoFONBrFnj17cPvttydvUxQFmzZtwiuvvDLu4wKBAM4//3yYpokLL7wQd911F5qbm8cdH4lEEImcbmzn803uyiURUTZtWFiNtXMr8cALh/FBXxDnV5XiC5cugHWSCfzhmAF/WEcoOjrvBgA+6A2i1ePF79u86AmkXnGZN8uBC+dW4vdtXQhEjHGTkAXiOSkO2+mP/GyWL05sEyu1aQhGYgjrEnJ4HjZVoNSmIRQzcrpNrK3LD1UAmiqgm4CmyHgFOCD+vSqgivi4YtpCRjNTvqx85VulSpp5JhXo9PT0wDAMuN3uUbe73W4cOHAg5WOWLFmCn//851ixYgUGBwfx/e9/Hxs2bIDH48F5552X8jF333037rzzzslMjYgoZx58oSO5NcREvHzlz19+P62tIRHdQDBiIBDWoZujV2d8QzE8196NVo8XB7r8KR/vsmu4YmkNrr6gFotqnHjjg348196NWpcF/cEwInr85F0RgE0TcNqt6AlE0BOIYLYiclK+OL5NzMSx/sio4EsCGNIljvWHUOnI7TaxvlAUilBQX16C3mAEEd1M5uiUWBTMKrXlPPgiKnbpVKocnMbcOCp+Wa+6tn79eqxfvz75/YYNG7Bs2TL88z//M7797W+nfMztt9+Obdu2Jb/3+XxoaGjI9lSJiCb04AsduHdHOwxTQlMFtOFk38FQDPfuiOcinhns6IaJQERHIKKP2XpmmBKvH+nDDk8XXunoRcwYuyyjKgIXza9CS3MtLl5QBcuIk4bZTjtsmgJAQlFUKIoJKSWEEBBCgaYKVDosaKgqRbcvnJNmfctqyxCMjr/CZEogGDVyuk0sUTHPqimYN6sU4Zh5uuqaRUFYN2ExTOboEGXQdFaqJAImGehUV1dDVVV4vaOr/Hi93rRzcCwWC1avXo1Dhw6NO8Zms8Fms01makREWafrJrbv7hiR5xEPOBQBKCLe02P77g7cuGE+hCIQHKeoAAAcPhVIbk3rD8VSvt7C2U5sbnbjymU18bLIwzRFgdOuwWnTMG9WKWaX2eA56YMQ8fsEBCSAsG5gaNBAc70Lv/r/1qOty5+T0q77Tg4icpaKawAQiZnYd3IwZw1DR1fMsw33CYqfeGVzGx/RTJbrSpVEZ5pUoGO1WrFmzRrs2rULn/zkJwEApmli165duPXWW9N6DsMwsG/fPlxzzTWTniwR0XR64p1O+Idi0NR45TJTyuT2JyEEVEXCF4rh3/94FJctmT2mx81gKIZdB7xo9XjxXncg5WtUOiy4clkNWppq0VjjTN4uhECpTUWZzTKqmad55t4wAMnkk5FFAHJYvnjHvq4ze4OOIYfH5SrQyUXFPCIajb93NN0mvXVt27ZtuOGGG7B27VqsW7cO9913H4LBYLIK2+c+9znMmTMHd999NwDgW9/6Fi6++GIsXLgQAwMD+N73vocPPvgAN910U2bfCRFRlp0YCMVzciQQHc7xGBlbqCL+/dG+IKSMbwmLGSb+eLgPrfu78OrhvjFFB4B4M8/1jbPQ0uzGunlV0EZsTbNb1PjqjVVLeTLgOelDbyCKuvISDA7F4v1hzOHcE6uG8hILegPRnCb7+iPp7bdPd1ymZKNiHhGdHX/vaDpNOtC59tprcerUKXzjG99AV1cXVq1ahR07diQLFBw9ehSKcvqPdH9/P26++WZ0dXWhsrISa9aswcsvv4ympqbMvQsiohyYU+GAABBLEaxIALqMBzw1ZTYc9PrR6vHi2QPdGBxKvTVtSW0ZWprcuHxpDcpLLMnbVUXAadNQZrfAOkFJ2ESyb02ZFZWlFoSjI3JPrAqkCXQHIjlN9u0LpH6/Ux2XSRsWVuPiBbPYoZ0oh/h7R9NlSsUIbr311nG3qu3evXvU9z/84Q/xwx/+cCovQ0SUV65aWoN0/iw/8voxHOlN3SxzltOKq5a5sbnZjXmzSkfdZ7eoKBvOvUm3sfLIZF8rBMIxAzHDhEWVsGkCEUPmPNnXYU2vX0e64zItl9v4iCiOv3c0HbJedY2IqJBFdRPB4YppnhM+2C0KgtHxE+0lMCbIsWoKPtw4C1dfUIsL51ZCHXEVczKrN6kkkn33HOlHWB9d6ezkIGDXVKyZV5nTZN951WUAOtMcR0RElB0MdIiIzqAbZrzXTVRHZETFtMFwFFZNhVVTMBDSJ0y4b653oaW5FhuXzIbTNvrjdiqrN6koisDcyhL84b2eMfeZEgjFDMytLMnpFpEvXLoA/+fZ96CPV18a8bykL1y6IGdzIiKimYeBDhER4v1sglEdwYiOoejYctAAYJrxFZ6Ibo4b5CgCuLq5Fn+xrgHnVTrOuE/AadfgmuLqTSq6buKpd7tGbamTwKjvn3q3C3f+yQXQMvSaE7FaVVy79jz8x2vHxh1z7drzYLWO7atBRESUKQx0iGjGklIiGDUQjOgIRY0x5aABIBwz8OKhHrR6vHjzg/6UAY4A4LRrkFJisbsM2zYvhjJilcZmUeHKwOpNKiNLXquKiFeCS5a8jgdw/qEYnninE5+6cE5GX/tsvvOnK9DlC2PXgVNj7rty6Wx8509X5GwuREQ0MzHQIaIZRUqJoZiBQERHKGLATBHcSCnx7gkfWj1d2H3wFELjrPBoioBNU2DVBEwTcJZYcN1Fc6EIkVy9KbNrsGnZW7lIlLzWBCAgIARGLecoQsIYHpdLLx/qQcepICrsCkwo0A0JTRVQINFxKoiXD/WwrCwREWUVAx0imhHCw8FNMKKn7GUDAF2DYTyz34vW/V04ORBOOaau3I4ym4ZD3QHopoQeNRCMxnvoXLqoGhc3Vsdzb8bpe5NpcyocUBDPx0n1cuZwyes5FY6xd2aJaUrc/3wH+oJRGKZA1DAgJRAzAauqoC8Yw/3Pd+DiBbNYXpaIiLKGgQ4RFa2obiaDm5iRulLaUNTAC++dQqunC3uPDaYc47CquGzxbLQ0u9F20od/efF9mIgHNwLxnBhDAk+/24UVDRW4+dLGrL2nM21ZUYc7n/RgMBSDIkwo4nQejilN6IZEucOCLSvqcjYnz0kf9p8cxFAsXgVOU+IrTRJAWDehCBP7Tw7mtIkpERHNPAx0iKio6EY8uAlEdET11MGNKSX2HhvATo8XL7x3CuHY2HECwIXnV6Kl2Y1LFlbDblFhGBL/8N8eGBKwqPGtYnJ4JUUTQFSX2L67AzdumJ+zxH9NU7B1YyPu3dGOqC6hqSYUEV/J0Q0JVRHYurExZ/MBgJ5gBL6wDlNKWBQlmZckAFgUIGaa8IV19AQjOZsTERHNPAx0iKjgGaZMBjcjy0Gf6UT/EFr3d+GZ/V54falPss+rLEFLsxtXLXOjxmUfdd+z7d0IhnWoAtANAJDJCmdCAKqCaUn8T6wg/Z9d78EfOf3+y2wq/ubKRTldYQKAgWAMpimhKGJM8QUxnL9kmhIDwVhO50VERDMLAx0iKkjplIMGgEBEx+72U9jp6cK7J30px5TaVFyxtAYtTbVYVleWsjKaVVPgD8dgAjiz9JpEvNJZIh8m14n/APDq4d5RQQ4A+CMGXj3cm/NAp9JhgaLEgxmpSIgR1REkZDIIqnRYcjovIiKaWRjoUMaZpoTnpA99oSiqHFY017uYcExTNvJ4qiixYH51KUJRA0Ox1OWggXgQ9ObRfrR6vHjxUE/KLWyKANbOq8LVzW5saKxO2ddGCIFSmwqX3QK7RcX8aueE85UA6spLJv0+z8Xnf/FayjLOALDrwCl8/hev4aG/XJez+cxy2uCyWzA4FENUN4eLJMSzmeJFEwRcdgtmOW05mxMREc08DHQoo14+1IP7n+9AR3cAMUPCogo01jjxxcsaWUqWJu3lQz346e5DONQdiOefKEDDrFJ8dl0DVs+tHDP+g94gWj1e/L7Ni55ANOVzzpvlQEtzLTYtqxn3RNuiKnDZLXDaNagjgvQFs0vTmne64zIhHNbHDXISdh04hXBYh92em4/85noXmupdeP39PoT1eHAzchnMqgo01bvQXO/KyXyIiGhmYqBDGfPyoR587bF9CER0VDqssKoKooaJtk4/vvbYPtz1qeUMdihtzx3w4hv/7UEgoseDDptAzJA4fCqAHzxzENuuWozVcyvhG4rhufZutHq8ONDlT/lcLruGK5bW4OoLarGoxplya5oQAqVWFWV2C0qsqfve9IVSB09THZcJ3366Le1x3/nT5VmeTZyiCMytLMEf3ktdDCKsm5hbWcKVXiIiyioGOpQRib4ZgYiOWpc9eSJpV1TUuhR0+SLsm0ETCscMBCM6/GEdP3kufjxVO63JHA+bJlDttOKUP4L7dx9GfaUdr3T0ImaM3cKmKgIXza9CS3MtLl5QBYuauurYeKs3qXiOp87xSTXuyqXutMaeqyN9wYyOywRdN/H/9hw/65j/t+c47vyTC3JaDY6IiGYWBjqUEZ6TPnR0B1DpsKasslThsKCjO8C+GTRGVDcRHK6Yluh1c7ArgGO9QbjsllGJ7BHdgG9Ihy+sY2AogEOnAmOeb+FsJzY3u3HlshpUOqwpX/PM3Jt0pVgIOqdxmeC0pvcxnu64THjsreOIpgg+R4oaEo+9dRz/40NzczQrIiKaaRjoUEb0haKIGRLWca6a21QFg6bM6ZYeyl+6YSIYMeCPxFIWChgMRxEzJVyqgG5K+MMx+MI6IuP0xal0WLBpmRubm91onD1+wQCbRUWZXYPTqk1pZbG+Ir0iA+mOy4S18yrRut+b1rhceXzvybTHMdAhIqJsYaBDGVHlsMKiCkQNE3Zl7BXyiGHCoghUjXOFnYpfohx0IKwjfJZeNwBQatFgmhInBsIYOsvYC+dW4M8uPA8fmlcJbZwgW1UESm0ayuwabFr6qzepNFaXQoh4KenxCBEflytWNb33lO64TIgYZ//3new4IiKiqWCgQxnRXO9CY40TbZ1+1LqUUdvXpJQYCMWwrK6MVZZmGDPZ6+bs5aCB+HHyXncArR4vdrV54QvrKcfZNAWKABa7y/DdP18BZZx9YiXDhQVKrWrK4gNTMRjRYdMUhGOpV5YS8xuMpJ57NqyaWwGbpoy72pWY06q5FTmb06JqJ944MpDWOCIiomxhoEMZoSgCX7ysEV97bB+6fBFUOCywqQoihomBUAxOm4ovXtbIQgQzgJQSoWi8qEAwevbgBgD6glE8s9+Lnfu9eL8ndcK8KgTK7BpKrCrCMQMOq4rPrT9/TJCjKQqc9vjqzXjFB85FRYnlrKs5QHy1p6Ikd40wl88px2K3E56TvngF5+EVJxFvWwMIYLHbieVzcpcbd2WTG//5xtmLESTGERERZQsDHcqYDQurcdenlif76AyaEhZFYFldGfvoFDkpJcIxE4GIjlBUh2FOkIium3i5owetHi9eP9KHVMOtmoLmOhf8ER2DwQh0AIZhYsFs55g+Og5rPLgptWX/I82c4L1NdH+mKYrA7R9dhq0Pv4mBUCwZiEkZb9FZUWLB7R9dltOLDOk2AmXDUCIiyiYGOpRRGxZW4+IFs5Kd7KscVjTXu7iSU4SklBiKGQhGjLSCGykl2jr9aN3fhecOnEJgnO1dF9S7sLm5FhuXzIbTpsGUEoe8QQyGoyi3W7HQXQpFCFhUJV5YwKaNm5+Taf3BKExMEOhAoj84PUU3zpxZbkOu097rTt3PKNW4VI1fiYiIMoGBDmWcogiWkC5Skw1uAOCUP4Jn9nvR6unCsf6hlGNqymzY3OzG5iY3zqt0jLpPEQKLa+O5HOk09cym/lAMkICqxFdMpEzuDoMQp7eL9YdiOZuTaUrc/bu2cV+zPxTD3b9rw39vvSRnFxyeO3Aq7XGfXsuqa0RElB0MdIhoQkNRI+1taUC88eeLh+Jb0978oD/lyoJdU3Dp4tnY3OzGqoaKcYsKAEiu3pTZLRM29cymilILFEXAlPFtmRBiRD6MhC4lFEWgojR3OTr7Tgxif+fZG5nu7/Rh34lBrGyoyMmc0t2+l+ttfgm6buKJdzpxYiCEORUObFlRN2Mbl5qm5Ao8ERUtBjpElNJkgxspJfadGMROjxe7D55CKJq6dPCqhnJsbqrFpYur4ThLE8vpXr1JpbrUBpddgy+sQ5eAJgBFxFd1dBlffXLZNVSX5i73ZM8HfTDGL7gGADDM+LhcBTqVaQZ66Y7LpAdf6MD23R3wD8VgAlAA3PmkB1s3NuLmSxtzPp/p9PKhnmROZcyQsKgCjTVO5lQSUdFgoENESeHYcHATMaCbE5w9D+saDGPn/i60erzoHAynHFNXbkdLsxubm2pRW24/6/NZVAUuuwVOuzatqzepNNe70FRfjrePDcIwTUQNM7miY9cUqIqCpvrynJZR33fi7Ks5kx2XCbWus/8bT3Zcpjz4Qgfu3dEOw5TQVAFNAKYEBkMx3LujHQBmTLDz8qEefO2xfQhEdFQ6rLCqCqKGibZOP7722D7c9anlDHaIqOAx0CGa4SJ6POcmGNERm2hpYNhQ1MDzB0+h1dOFt48PphzjsKrYuHg2WpprccEc11l72QghUGpT4bJbYLfkx+pNKiPLqPuGorBpGgxTDgdkEq4SS87LqJdY0ttyle64TFBEeq+V7rhM0HUT23d3wDAlrJpIvrYiAEWYiOoS23d34MYN84t+G5tpStz/fAcCER21Lnvyd9OuqKh1KejyRXD/8x24eMEsbmMjooLGQIdoBooZJoIRHYGIjuhZGk2OZEqJvccGsNPjxQsHTyGc4nECwIXnV6Kl2Y1LFlZPGLTk8+rNeDYsrMaHG2fh0TeOQx+xpU9TBD56waycXwWvSXNVJN1xmdB8nguJNj7jEcPjcuWJdzrhH4pBUwUE4nlWidU4AQFNBfxDMTzxTic+deGcnM1rOnhO+tDRHUClwzrmAoQQAhUOCzq6A/Cc9LGwDBEVNAY6RDOEbpgIDufdRGKp82dSOdE/hNb9Xdjp8aLbH0k5pqGyBC3Ntdi0rGbCE+pE7o2rJL9Xb8bz4AsdePSN4/HtT0r8JFlCwjAlHn3jOOZVl+Z0+9OKNBuBpjsuE2qcdpRYFIRi4wfRJRYFNc7cBV8nBkLxnBwpETXlmIp56nCu1YmBUM7mNF36QlHEDAnrOGXZbaqCQVOiLzQ9ZdKJiDKFgQ5RETNNiUBURzCiY2ic4gCpBCI6drefwk5PF949mTq3w2nTcPnS2WhpqsWyurKzbk0DCnP15kzjbX8CAFWZnu1Ps8vsyQIJ43HZNcwuy11Qsay2DFaLetZAx2pRsay2LGdzmlPhgJBA7IxlJol4mXBTxgsTzKlwpHp4UalyWGFRBaKGCbsy9mJDxDBhUQSqHNZpmB0RUeYw0CEqMlJKBKPxnJtQ1ICU6ZXwNUyJN4/2o9XjxYuHelJuaVMEsHZeFa5udmNDYzWsE5zM52PltHMxcvvTmfklilCgqWbOtz8117uwsqECr3b0IpaiOp5FEVjZUJHTAgltXX6oQkBTBExTQlFOr56YZjzXSRUCbV3+nG2N+tgFtfjqr0SypPXIuDzxK6IoAh+7oDYn85lOzfUuNNY40dbpR61LGXWRQkqJgVAMy+rKcnrMEBFlAwMdyjj2Zci9RCPPQDge3JhpBjcA8EFvEK0eL55p86I3kHqryvzqUmxucmPTshrMck5cOjmTfW/y6XhKbH/Sxnl5RQAGcrv9SVEE5laW4A/jlACPmRJzK0ty+jPrC0WhCIE5lSXoDUQR0Q1gOB/GYVMxy2lFKGrkdGtUe3cADqt6euUrxY/LYVXR3h0o+ryUkUU1unwRVDgssKkKIoaJgVAMTpua86IaRETZwECHMop9GXJrsr1uEnxDMTx7oBut+71o7/KnHOOya7hymRstzW4sqnFOuDUtG6s3+XY8zalwQEF8m5MQ8bycREK7qoj47cjt9iddN/H42yfPOubxt0/izj+5IGfb6RJbo6yqgrlVdvT4Y4gaJqyqguoyC6IGYFHMnG6N6gtFYdNUuMtU9ASjo35fVEWgutQKc3jcTLBhYTXu+tTy5O/XoBlvgrusroyf10RUNBjoUMaM7MtQYlGhqfGu8W2dPvZlyKBEr5tgZHLBjW6YeP1IP1o9XXjlcC9ixtjHqorAxfOrsLm5FhcvqIJlnGTlkTK5ejNSPvb52LKiDnc+6cFAKDbm55f4vsJhwZYVdTmb03+/fRLByOn8q5H/AokZBiMG/vvtk/izNeflZE6JrVF7jvQjrBsYeZj2BCOwayrWzKvM6daoRPBVatMwu8yGgaEYYoYJi6qgosSCsG4iFNFnVF7KhoXVuHjBrLxZMSUiyjQGOpQRib4M/aEodENicCiWvNJtVRXEDJN9Gc5BOBbPuQlOopFnQsepAFo9XdjV1o3+UCzlmIU1Tlzd7MYVS2tQkeaJnsOqwVWiwWHN/MdIvvb50DQFa+ZWYNeBU+OOWTO3Iqd9WN461p/8/zMX3QRO55+8daw/Z4FOcjvdez1j7jMlEIoZOd9ONzovxYbKEcf5TM5LURRR9Fv1iGjmYqBDGeE56cP+k77klWVVERBK/CQropuI6MD+kz72ZZiEqG4mV27SbeSZMBCKYteBbrS+68WhU4GUYyodFmxa5sbmZjcaZzvTel5NSazeaNDSWO2Zqnzt86HrJvYcHTjrmD1HB6DrZs6CndI0A810x2WCrpt46t2u5OrSyLWvxG1PvduV0+10zEshIpp5GOhQRvQGIvCFY5BSwqIpEMOnM0IAQgViuglfOIbeQOo+LBSnG/HgZjKNPBNiholXD/dhp6cLr77fl3Jbm0UVWN84C1c31+JD86rS3mrmsMaDm1Jbbj4y8rXPxxPvdGJwnFWxhMFQbquufbS5Fj/7w+H4yk2itFnC8CEgRHxcriSq06lKfAVnZG0MIeJFG6ajOSfzUoiIZhYGOpQR/aHYcBlZkQxyEgQElOEys+NtnZrJDFMiGNURCOsIT6KRJxDfcvNedwCtHi92tXnH7aWytLYMLc1uXL6kBq4SS1rPnavVm1Tytc/H0f5gqmJdo8jhcbmyoqEC82Y58H5PKB7njJhg4n/nzXJgRUNFzuZ0YiA0KsARif8M96tJFG2YjuaczEuhmSCfqlUSTScGOpQRFaWWeDAjJaSUY/oymDIeBFWUpneSXezM4eAmGDEwFEu/101CbyCC37d1Y+d+L97vSX1SPctpxVXDVdPOn1Wa9nPnevUmlXzt89E9mN6KZLrjMkFRBL7zyeXY+vCbGAjFxmwTq3BY8J1PLs/pSU5deUlyHmI4wBm5uiRl/Nu68pKczWkk5qVQMcu3apVE04mBDmVEdakt2Z09ZkpoSvICLnRTQhECLruG6tKJe7AUq6k28kyI6iZe7ujBDo8XbxzpQ6qCa1ZNwSULq9HS7MaFcyvT3po2nas3qeRrPoXbld7xm+64TNmwsBrbP3shtj93CJ5OH2K6CYumoLnOha2XL8z5yc2C2aXJ3/8xh/mIVZ4Fs9MPwIloYvlYrZJoOjHQoYxornehqb4cbx8bhGGaiBpmsuqaXVOgKgqa6stnXEUjKSVCI4KbkY08TSlxyBvEYDiKcrsVC92lUM5IvJdSoq3Tj1ZPF55rP4VAJPXWtFKbimsuqMX16+fBmeZKjBACDquKMnt2Kqedq3zMp2ioOn0CPx4xPC7X8mlLli+sw6YpCJ8lz8ymKeNutSSiycvXapVE0yn/zm6oII28Au8P66gstUIVAoaUGIoaKLNrM6aikZQSQ8O9bkKR0cFNwltH+/Hwa8dwrDeI2PAJfMOsUnx2XQNWz61Ety+MZ9q8aPV4cbx/KOXrqEp8lazEomIoZuDljl5cvGAWVs+tPOv8En1vnLb8WL05m3w6eQdG99EZT3mO++iMlC9bsipKLMm6CKmCwsTtFWnmixHRxPK1WiXRdGKgQxlz5hX4iBlPGG+qd82IvcHpNvJ862g/fvDMQYSiBlx2C1yqQMyQ6Oj24x+fakO104ZD3YGUJ4iKADRFYHaZDQ6LmvxjVmpT0ROI4uHXjmFlQ8WYlaF8X705m3w5eQfifXS2bmzEvTvaYZgSI+MtU8aDz60bG3PaR2ekfEpAnmhn5iR3bhLRBPK1WiXRdJrSGc/27dvxve99D11dXVi5ciV+/OMfY926dRM+7pFHHsFnPvMZfOITn8Djjz8+lZemPJdvV+CzLTxi5SadRp6mlHj4tWMIRQ1UO62ABIZi8dLbgYgOUyJlZbpVDeVYeV4lnnrnBEptFtjOOJEWECizW3CsN4hD3iAW18b74hTS6k2huPnSRgDA9t0d8A/FkisX5Q4Ltm5sTN6fay8f6sFPd3egvcuPqGHCqipYUluGWzbm/iLDwFAMCsbf4icRD9oHhliFkShT8rVaJdF0mnSg8+ijj2Lbtm144IEHcNFFF+G+++5DS0sL2tvbUVNTM+7jjhw5gq985Sv4yEc+ck4TpvyXT1fgsyEci+fcBNMMbkY65A3iWG8QDouKvmAMvqEYYuOs/tSV29HS7MbmplrUltvx+pE+/PbteC+cVKyqgF9KDIajKLVpBbl6UyhuvrQRN26Yjyfe6cSJgRDmVDiwZUXdtK3kvHyoB1/+r73oC0YhpUzmx/3x/Sje6/bjh59eldNgx2XXEJmgyW1EN+GyT8/xqetm3vzbEWVKvlarJJpOk/4r84Mf/AA333wzbrzxRgDAAw88gKeeego///nPcdttt6V8jGEYuO6663DnnXfiD3/4AwYGBs5p0kS5FtENBCPxACc2wQnceIaiBnYd8KIvFIM+TnAjRDyQ+asPz8f/WHPeqD9U5XYrLEp8m5tNGxvsxEwJm6JgidsFt8s+pTlS+jRNyWmzy/GYpsTdv2vDKX8EAoCmKskSzrph4pQ/grt/14b/3npJzlZWD5863W8o8YryjP+Xw+MmyinLtAdf6EiuxpkAFAB3PumZ1tU4okzI12qVRNNpUoFONBrFnj17cPvttydvUxQFmzZtwiuvvDLu4771rW+hpqYGn//85/GHP/xhwteJRCKIRE73ofD5fJOZJlFGxAwTwYgOf3jqwY0pJfYeG8BOjxcvHDw1bhUqh0WFqyS+vSwaM7DqvMoxyaQL3aVomFWKw6cCqHZak41ZFUVAAdAXMrCsrgwring1jcbad2IQB70BCAAWTUkeF0LEv4/pJg56A9h3YhArc9Q0tHNw6HR56RG3n9njp3MwdaGNbHnwhY5kfpWmCmginls1GIrh3h3tADBhsJNPeVBEZ8rHapVE02lSgU5PTw8Mw4Db7R51u9vtxoEDB1I+5sUXX8RDDz2EvXv3pv06d999N+68887JTI0oI3TDRDBiIBDVEYkZU36e4/0h7NzvxU6PF93+1M0jLapAud2CMrsGi6pAQqInEMWC2U4sdI8tT6wIgc+ua8APnjmInkAMFQ4LSjQFUVPyat0MtvfoAGKGCU0RySAnQUBAVQRihom9RwdyFujMqXAkV5XGI0R8XK7ouontuztgmBJWTQAQkDKeK2TVgKgusX13B27cMH/cbWxsxEiFYKblyhKdTVY3SPv9flx//fV48MEHUV2d/h+B22+/Hdu2bUt+7/P50NDQkI0pEsEwJYLReLW0oejUg5tARMfu9m60erzwnEy9Cum0aVg+x4VD3QHEDBMOmwZVEQjrJvzhGBxWFZ9d1zCmalrChxfORqXDip+/9D4OnwoiENF5tW6Gk4lDZbxzGHHGuBz42AW1+OqvRLK0+siXTsQ+ihD42AW1OZvTE+90wj8Ug6oAMUPGc5mG5yYEoCqAfyiGJ97pTLklkY0YqZAUe64sUbomFehUV1dDVVV4vd5Rt3u9XtTWjv2D1dHRgSNHjmDLli3J28zh5G1N09De3o7GxrHbBGw2G2y23HYWp5lFSongiEaecoq1bg1TYs8H/Wj1dOGljl5EU2xNUwSwbn4VNjfVYkPjLFg1ZVQfHb+UsAiBBbOdyT46I6lKvKJaYuWnttyOjUtqeLWOAACrGyqgKQoMw4SiyjEJyIYhoSkKVudoNQcA2rsDcFjVZEPQVL9dDquK9u5Azk7GTgyEYMrTq0wCpwMwU8a/xPC4M7ERIxFRYZpUoGO1WrFmzRrs2rULn/zkJwHEA5ddu3bh1ltvHTN+6dKl2Ldv36jb/vf//t/w+/340Y9+xFUayikpJUIjgptUjTzTdaQ3iJ0eL55p86I3kLonwfzqUrQ0u7FpmRtVpaPLea6eW4mVDRU45A1iMBxFud2Khe7SUSs5JVYVZXYLSq3qmHwdXq3LjGKovrV8TjmW1DrhOelDzDShKUoyP0Y3TUgAS2qdWD4nd8dLXygKm6aixqWi1x+BMeJXTRXArDIbpERO+3nUlZeMLpAw4ldKyNP5RHXlJWMey0aMRESFadJb17Zt24YbbrgBa9euxbp163DfffchGAwmq7B97nOfw5w5c3D33XfDbrfjggsuGPX4iooKABhzO00NE2PPTkqJoRG9bs4luPENxfDsgW607veivcufcozLruHKZW60NLuxqMY55qRoJEWIZL+bhDNXbyh7iqX6lqII3P7RZcny0rohISEhIKAoAlVOK27/6LKcfi4k+nmU2jTMdloxGIoX9LCoCsodGiK6RCii57Sfx4LZpaMKJKTaTieGx52JjRiJiArTpAOda6+9FqdOncI3vvENdHV1YdWqVdixY0eyQMHRo0ehKDxBywUmxo5vKDoc3ER1GOOUck6Hbph4/Uh8a9orh3sRM8Y+l6oIXDy/Ci3NtbhoQdWUApQSqwqX3QJHitUbyrxMVN/KJxsWVuOmS+bjJ88dgn9IHz5xl3BYNdx0yfycfx6M7udhQ+WIFc14P49ozvt5+MI6HFYVweE8vFTXPEZutxuJjRiJiAqTkFNNTsghn8+H8vJyDA4OwuVioytg/MTY/uHqWzMxMTY8YuVmso08z9RxKoBWTxd2tXWjP5S6e/vCGieubnbjiqU1qJjCCQ5Xb6aHrptYe9fvMRiKwaoJKOL0z96UJqK6RLnDgje+tqlgtrElPg/iBS00KCJeCCAUNVBm16bl8+D0Z5SRsp9Hrue07/gg/r9/ewNR3UR/KDpmO12lwwqrpuCfr187ZvuZaUrc8K+voa3Tj5oyC3xDRnKFylWiotsfb8T4yxvXcUWdiCgH0o0N2Da9ADEx9rRwLJ5zE8xAcNMfimJXWzd2erw4dCqQckylw4JNy9zY3OxG42xnyjETOVvuDWVfovqWpo4OcgBAEQo01Txr9a18M/LzoK68ZHST2RI5bZ8H+dbPY+Qq05JaZ8pgpbHGmXKVKdGIcevDb6KtMzC6H9AAUOGwnHNpd25DJiLKPAY6BWimJ8ZGdAPBSDzAmWojz4SYYeKVw73Y6fHij+/3pdzmZlEFNjRWo6XZjQ/Nq4I6hZMPVRFw2jSU2S2wFsgqQbE6MRCCCUAb559REYCB1NW38lE+fx7kUz+PkV3ju/3xPlTlqgURw0S3f+I+VJ6Tg/ANxcZUkJOI5+95Tg5OOXjjNmQiouxgoFOAZmJibCaDGyklDnrjW9OePdCdck8+ACyrK8PmplpcvmQ2XCWWKb2W3aLCVcLVm3wyp8IBBfGcnFTntIkyw7lsZnku8v3zIJ8qBE51lSnRbNSUgG1Es9H4r7RMq9noeNifh4goexjoFKCZkhgb1U0EIzoCGQhuAKA3EMEzbd3Y6enCkd7UV+urnVZc1eRGS1Mt5s6a2okuV2/y25YVdbjzSQ8GQzEowhyTo6Mb8RydLSvqpnGW6ZspnweZMpVVppTbHZPDxZS3O3IbMhFRdjHQKUCjKxopYxoEDoRiOa9olCkx43Rwk6r55mRFdRMvHepB634v3jjSh1QF2Kyago8sjG9NWz23ckpb0wDAZlHhsmtw2jSu3uQxTVOwdWMj7t3RjqguoakmlOGqa7ohoSoCWzfGtzDtOz447VuuJjLy88BdJhDRJfThfjo2TRT050G2THaVKVvbHfN52yERUTFgoFOARu417/JFUlY0OtfE2FzSDRPBiIFAVEckZpzz80kpsb/Th50eL55rP4VAJPXWtAvqXWhprsVlS2bDaZvar4Ii4r1CXCUabNrYq+mUnxKloxN9dAzEL9CXOyzYurERzfXluOFfXyuInInE58GX/2svDnYHRpVNFgKoKrUW1OdBPsrWdsd833ZIRFToGOgUqHyraDRZhikRiOgIRnSEMxDcAEC3L4xn2rxo9XhxvH8o5ZiaMhtamt3Y3FSLOZVjO6CnK16pyYIym8YTyAJ186WNuHHDfDzxTidODIQwp8KBLSvq8NqRvgLPmZAj8kd4bGZCtrY7ctshEVF2MdApYPlU0SgdpikRjMa3pYVjJjLRwikcM/CH93qw09OFN48OjKmIBAB2i4LLFs/G5iY3VjZUQJnitjIhBEqt8eICdgtXb4qBpimjcioKMWciMWfDlFjsdiISG7F1zSLg9UXzbs6FJt3tjpMtRFDM25CJiPIBA50Cl08VjVJJBDfBiIGhmJGR4EZKiXdODGKnx4vnD55CKJp6RWhVQzk2N9XissWzUWKdemBiURWU2ePFBaaav5MK+2bkn0LMmRg5Z0UoKLECwOnjPR/nXIgm2u6YuH8yim0bMhFRvmGgQxmXjeAGADoHh7DT48XO/V50DoZTjqmvsKOlqRZXNblRW26f8msJIeCwqnDZLecUJI2HfTPyUyHmTBTinAvVeNsdJ7uSM1Khb0MmIspnDHQoI0xTIhSL97kJRTMX3ISiOp4/GN+a9vbxwZRjSq0qLlsyGy1NtbhgjuucKp5pigJXSbxymjbOieO5Yt+M/FWIOROFOOdCduZ2x0wotG3IRESFgoEOTVm2ghtTSuw9OoDW/V784eAphFOUmRYA1pxfiZZmNz68sPqcc2Yc1njlNIc1u78ShZgDMpMUYs5EIc6Zxsr3bcg0Pm5DJspfDHRoUrIV3ADA8f4QWj1ePLPfi25/JOWYuVUObG5y46omN2aX2c7p9TQlkXuTvdWbMxViDshMUog5E4U4Z6JiwW3IRPmNgQ5NKJvBTSCsY/fBbrR6vPCc9KUc47RpuHzpbFzdXIultWXn3IzTYY0HNw6rmvPGnpnKp9B1M6N5AnRaIeZMFOKciQodtyET5T8GOpSSlBLBaHaCG8OU2PNBP1o9XXipoxfRFFvTFAGsm1+FzU212NA4C9ZzPIlXFYEyuwVldg2WHK3epJKJfIoHX+hIVn4yASgA7nzSM+XKTzRWIeZMFOKciQoVtyETFQYGOpSUCG5CER3BDAc3AHCkN4idHi+eafOiN5B6xWJ+dSlamt3YtMyNqtJzT54usaoos1tQOg2rN6mcaz7Fgy904N4d7TBMCU0V0IZ7eQyGYrh3RzsAMNjJkELMmSjEORMVIm5DJioMDHQmoRgTDkcGN6GoATPDwc3gUAzPHehG634v2rv8Kce47BquXOZGS7Mbi2qc5xyQqIqA0xbve3OuK0GZdi75FLpuYvvueGNIqyaS3dkVASjCRFSX2L67AzdumM9tbEREWcSy7kSFgYFOmoop4VBKidCIbWmZDm50w8RrR/qw0+PFyx290M2xz68qAhcvqEJLUy0uWlCVke1kdouKMnu8NHQ+rN6MZ6r5FE+80wn/UAyaejrISVCEAk014R+K4Yl3OjNe/paIiE5jWXeiwsBAJw3FkHCY7eAGADq6A2jd34Vdbd3oD8VSjllU40RLsxtXLK1BRQb+AChCwGnX4MrD1ZuzmUo+xYmBEEwA2jhDFAEYw+OIiCh7WNadqDAw0JlAISccSikxFDMQiOgIRbIT3PSHovh9Wzd2errQcSqYckylw4JNw1vTFsx2ZuR1bRYVrgJYvTmbyeZTzKlwQEE8JyfVoWbKeH+hORWOjM2RiIjGYll3osLAQGcCIxMOAWAoakA3TWiKArtFycuEw6HocHAT1WGk2DZ2rqK6iVff70Xru168dqQv5WtYVIENjdVoaXbjQ/OqoGbgw14RAqW2eGNPm3ZuDUIL0ZYVdbjzSQ8GQzEowhy1fc2UJnRDotxhwZYVddM4SyKimYFl3YnyHwOdCSQSDqO6ic7BIUR0E1ICQgA2TcGsUhtieZBwGI4Z8IezF9xIKXHQG0CrpwvPHuiGL6ynHLesrgybm2px+ZLZcJVYMvLaVk2Bq8QCp1Wb0VfHNE3B1o2NuHdHO6K6hKaaUIarrumGhKoIbN3YyEIEREQ5wrLuRPmNgc4EqhxWmNLEycEoTAloioAQgAQwFDNxcnAILrs2LQmH4eEmnsFIfJUpG3oDETzT1o1WTxc+6E2d+1HttOKqJjdammoxd1Zmtk0JkaicpsFumXmrN+NJlI5O9NExEN+uVu6wsI8OEdE0YFl3ovzFQGcCy2rLYAxfMY+X9I1fpREANEUiqksYMj4uFxLBTShqIGZkJ7iJ6iZeOtSD1v1evHGkD6kWiKyago8sjG9NWz23MiNb0xLPW2a3oMw2s1dvzubmSxtx44b5eOKdTpwYCGFOhQNbVtRxJYeogBVj+wIiounGQGcCbV1+qEJAVQQME4Ai4ys6EjDMeJlkVQi0dfmzdkUnohsIRuIBTraCGykl9nf6sNPjxbPt3QhGjJTjls9xYXNTLS5bMhtOW2YOHyEESm0qXHYLV2/SpGkKS0gTFYlial9ARJRPGOhMoC8UhSIE5lSWoDcQRUQ3IM14jo7domKW04pQ1Mh4jk5UNxGM6AhkMbgBAK8vjGf2e7FzvxfH+4dSjnG7bNjc5MbmplrMqSzJ2GtbVAUuuwVOu5axFSEiokJSDO0LiIjyFQOdCSSagllVBfOqHQhHzdNV16wKwjETFsXMSI5OzDgd3ET17AU3QzEDL77Xg1ZPF946OoBUpQvsFgWXLZ6NluZarDivPLll71wV4uoNt5QQUTYUcvsCIqJCwEBnAqObgtlQYlUBxE/QM9EUTDdMBCMGAlEdkVjq7WKZYEqJfScG0fquF88fPIWhcV5rVUMFrm524yOLZg+/18wo1NUbbikhomwZ2b7gzH5gQoi8bF9ARFRIGOhMIBtNwQxTIhDREYzoCGcxuAGAzsEh7PTEt6Z1DoZTjqmvsKOluRZXNblR67Jn7LWFECi1qnCVFM7qzUjcUkJE2ZRoX2BVUxcSsakKBvOgfQERUaFioJOGTDQFM0yJYDQe3AxFsxvchKI6nj/Yg52eLrx9fDDlmFKrisuWzMbVzbVorneNuZp4Lgp19WYkbikhomxLbI2OGibsytiLQRHDhEUR09K+gIioGDDQSdNUmoKZyeDGwFDMgJSZb+SZfC0psffoAHZ4uvDiez0Ip8jxEQDWnF+JluZaXLJwFmwZXGVJrN6U2S0Z3fI2XbilhIiybfTWaGXUZ00mtkYTEc10DHQmIZ2mYKYpERrR6yabwQ0AHOsLYed+L57Z70W3P5JyzNwqBzY3uXFVkxuzy2wZfX2LqqDMrsFp06CNs/2iEHFLCRFlWza2RhMR0WkMdDJASolg1EAooiOYg+AmENax+2A3drzrxf5OX8oxZXYNly+pQUuzG0tryzK6NQ0AHFYNrhINDmtxHkLcUkJEuZCJrdFERJRacZ6lZsnIMsOVJRbMry7FUMxAKGrAzHJwY5gSez7oR6unCy8e6kHMGPt6igDWza9CS3Mt1i+YBauW2RUWTVHgtGtw2Ytr9SYVbikholyZytZoIiKaGAOdNCXKDB/y+hHVJVQFaJhVis+ua8DquZVZe90jvUHs9MS3pvUGU2+TWlBdipZmN65c5kZVaeZXGEqs8b43Dqua8ZWhfMUtJUSUS+lsjSYioskRMtv7rDLA5/OhvLwcg4ODcLlyfwV9ZJnhihILACBmSPjCMTisKrZdtTijwc7gUAzPHujGTo8X7V5/yjHlJRZcuawGLU1uLKxxZjwAURUBp02Dq8QCS5Gv3pzNqD46w1tK2EeHiIiIaPqkGxtwRWcCZ5YZBoCobsKmCVQ7regJRPHwa8ewsqECyjkEG7ph4o/v92Hnfi9e6eiFbo6NP1VF4OIFVbi6uRbr5ldlJQCxW9RkcYGZsnpzNtxSQkRERFSYGOhM4MwywyMXwAQEyuwWHOsN4pA3iMW1zkk//6HuAFo9XdjV1o2BoVjKMYtqnGhpduOKpTWoyELyuyLEcO6NJeN5PcWAW0qIiIiICg8DnQmMLDMspUR/KIpwzICmKnDZNVhVAb+UGAynX2a4PxTF79u6sdPThY5TwZRjKh0WbFrmRkuzGwtmTz6ASofNosLF1RsiIiIiKkIMdCaQKDPc5QtjYCgGY8SWslP+SHwVRBUot599pSWqm3j1cC9aPV788f1epNiZBosqsKGxGi3NbnxoXhXULGyPUoRAqS1eGtqmFX5jTyIiIiKiVBjoTKC53gWbRcHJwfCY+0wJDAzFcF6lHQvdpWPul1Ki3etHq8eL5w50wxfWU77GsroybG6qxeVLZsM1XOwg06yaAleJBU6rxvwSIiIiIip6DHQmYJoS3f7IWcf0BqKQJoDhBZKeQAS/3+9Fq8eLD/pCKR9T7bTiqiY3WppqMXeWI8OzjhNCoNQWLw1tt3D1hoiIiIhmjikFOtu3b8f3vvc9dHV1YeXKlfjxj3+MdevWpRz7m9/8BnfddRcOHTqEWCyGRYsW4W//9m9x/fXXn9PEc+WJdzoRjhpQFUBKjNpyJgAIAURiJlr3d8FuUbHT04U3PuhPuTXNqin4yMJqbG5248K5lVnZmpZ4nTK7BWU2rt4QERER0cw06UDn0UcfxbZt2/DAAw/goosuwn333YeWlha0t7ejpqZmzPiqqip8/etfx9KlS2G1WvHkk0/ixhtvRE1NDVpaWjLyJrLpxEAIJgDrcMBgmBJSxgMcAJAADAn8n13vIWqkbkm0fI4Lm5tqcdmS2XDasrOIlsi9KbNrXL0hIiIiohlv0g1DL7roInzoQx/CT37yEwCAaZpoaGjA//yf/xO33XZbWs9x4YUX4mMf+xi+/e1vpzV+OhuGPvbmCXzl/+2FEPHVnFQrNam4XTa0NNXiqmY35lSUZG1+zL0hIiIiopkkKw1Do9Eo9uzZg9tvvz15m6Io2LRpE1555ZUJHy+lxLPPPov29nbce++9446LRCKIRE7nxfh8vslMM6O2rKjDP/z3PgQixoRj7RYFly2ejZbmWqw4r/ycGoiejRACTq7eEBERERGNa1KBTk9PDwzDgNvtHnW72+3GgQMHxn3c4OAg5syZg0gkAlVV8dOf/hRXXXXVuOPvvvtu3HnnnZOZWtYoisDsMhsCkdRFBQDApin4X5sW4bJFs1FizV7gwdwbIiIiIqL05KTqWllZGfbu3YtAIIBdu3Zh27ZtWLBgATZu3Jhy/O23345t27Ylv/f5fGhoaMjFVMfwnPQhEjNRXWpFbzCKkTvXBIAymwa7RcGCWc6sBDmsnEZERERENHmTCnSqq6uhqiq8Xu+o271eL2pra8d9nKIoWLhwIQBg1apVaGtrw9133z1uoGOz2WCz2SYztazpC0URMyTcLjuqnBYc7wtDUwG7psLlsAAS6A1FMRiOZvR1LaoCl90Cp13LWnU2IiIiIqJipUxmsNVqxZo1a7Br167kbaZpYteuXVi/fn3az2Oa5qgcnHxW5bDCogpEDRM2TcOC2aWodZWgwmGFAoGoIWERAuV26zm/ViL3pq68BA1VDpQ7LAxyiIiIiIimYNJb17Zt24YbbrgBa9euxbp163DfffchGAzixhtvBAB87nOfw5w5c3D33XcDiOfbrF27Fo2NjYhEInj66afxb//2b7j//vsz+06ypLnehcYaJ9o6/XC7BMJRE2HdgCoUWC0C/nAMC2Y7sdBdOuXXsKgKyuwayuwMbIiIiIiIMmHSgc61116LU6dO4Rvf+Aa6urqwatUq7NixI1mg4OjRo1CU0wtFwWAQt9xyC44fP46SkhIsXboU//7v/45rr702c+8iixRF4IuXNeLL/7UXB70BmCP66AgBVJRY8Nl1DVOqsJboe+Ow5iRVioiIiIhoxph0H53pMJ19dADg5UM9+PJ/7UVfMApzRCMdIYDyEgu+ds0yrJ5bmdZzaUpi9UaDpk5q5yARERER0YyXlT46M5FpStz/fAcMU2JxjRPh2Iita5pAbzCKh187hpUNFWdd1XFY48FNqY0/ciIiIiKibONZ9wQ8J33o6A6g0mGFoigosYpReTRldguO9QZxyBvE4lrnqMeqioj3vbFrsHD1hoiIiIgoZxjoTCBRXto6TqBiVQX8Uo4qL11iVVFmt6DUqkJMIXeHiIiIiIjODQOdCYwsL21XxjbsTJSXrnRYUV5iQZndAqvG1RsiIiIiounEM/IJJMpL94diOLNug4SEP6yjscaJK5fWYJbTxiCHiIiIiCgP8Kx8Aony0k6bis7BMPpDUQQiOvwRHb2BKMpLNPzPKxZBZQ4OEREREVHe4Nl5GjYsrMZ1F81FzDBwciCMk4NhdA2GoZsmrrtoLjYsrJ7uKRIRERER0QgMdNLw8qEe/HR3B/xhA4nNaxKAP2zgp7s78PKhnumcHhERERERnYGBzgRMU+Lrj++L5+iccZ8E0B+K4euP7xvVSJSIiIiIiKYXA50JvH18AEd6QsnvhTj9lXCkJ4S3jw/kfnJERERERJQSA50J/O7druRKzpktcRLfy+FxRERERESUHxjoTGAoYmR0HBERERERZR8DnQmsmluR0XFERERERJR9DHQm8ImV9XDaVACAlMDIsmuJ/qFOm4pPrKyflvkREREREdFYDHQmoGkK/teVi6COyMeR8nS8owrgf125CJo2PT9KXTfx2Jsn8JNn38Njb56ArpvTMg8iIiIionyiTfcECsHNlzYCALY/1wFfOAYp44UIXHYLtl7emLw/1x58oQPbd3fAPxSDiXjUeueTHmzdOH1zIiIiIiLKB0JKmfcNYHw+H8rLyzE4OAiXyzVt89B1E0+804kTAyHMqXBgy4q6aVvJefCFDty7ox2GKaGpAooATAnohoSqCPz91UsY7BARERFR0Uk3NuCKziRomoJPXThnuqcBXTexfXcHDFPCqgkoIh5sKQJQhImoLrF9dwdu3DB/2gIxIiIiIqLpxLPgAvTEO53wD8WGV3JG/xMqQoGmCviHYnjinc5pmiERERER0fRioFOATgyE4jk5IvX9iogXSzgxEMrltIiIiIiI8gYDnQI0p8IBBfGcnFRMCYjhcUREREREMxEDnQK0ZUUdykos0A0JU44uJ21KE7ohUVZiwZYVddM0QyIiIiKi6cVApwBpmoKtGxuhKgJRXUI3zXiAY8YLEaiKwNaNjSxEMIJpSuw7PojnD57CvuODMMdbDiMiIiKiosCqawUq2dtnuI+Ogfh2tXKHhX10zvDyoR7c/3wHOroDiBkSFlWgscaJL17WiA0Lq6d7ekRERESUBeyjU+DyqbdPPnr5UA++9tg+BCI6Kh1WWFUFUcNEfygGp03FXZ9azmCHiIiIqICwj84MkS+9ffKRaUrc/3wHAhEdtS47hIiXqbMrKmpdCrp8Edz/fAcuXjALyngl7IiIiIioIPHSPxUtz0kfOroDqHRYk0FOghACFQ4LOroD8Jz0TdMMiYiIiChbGOhQ0eoLRREzJKxq6sPcpiqImRJ9oWiOZ0ZERERE2cZAh4pWlcMKiyoQNcyU90cMExZFoMphzfHMiIiIiCjbGOhQ0Wqud6Gxxon+UAxn1tyQUmIgFENjjRPN9SxwQURERFRsGOhQ0VIUgS9e1ginTUWXL4KhmAHTlBiKGejyReC0qfjiZY0sREBERERUhBjoUFHbsLAad31qOZbVlSEU0dEdiCAU0bGsroylpYmIiIiKGMtLU9HbsLAaFy+YBc9JH/pCUVQ5rGiud3Elh4iIiKiIMdChGUFRBJafVz7d0yAiIiKiHOHWNSIiIiIiKjoMdIiIiIiIqOgw0CEiIiIioqLDQIeIiIiIiIoOAx0iIiIiIio6DHSIiIiIiKjoMNAhIiIiIqKiw0CHiIiIiIiKDgMdIiIiIiIqOgx0iIiIiIio6GjTPYF0SCkBAD6fb5pnQkRERERE0ykREyRihPEURKDj9/sBAA0NDdM8EyIiIiIiygd+vx/l5eXj3i/kRKFQHjBNEydPnkRZWRmEENM9HSpQPp8PDQ0NOHbsGFwu13RPh4oAjynKJB5PlEk8niiT8u14klLC7/ejvr4eijJ+Jk5BrOgoioLzzjtvuqdBRcLlcuXFLykVDx5TlEk8niiTeDxRJuXT8XS2lZwEFiMgIiIiIqKiw0CHiIiIiIiKDgMdmjFsNhvuuOMO2Gy26Z4KFQkeU5RJPJ4ok3g8USYV6vFUEMUIiIiIiIiIJoMrOkREREREVHQY6BARERERUdFhoENEREREREWHgQ4RERERERUdBjpUVLZv34558+bBbrfjoosuwmuvvTbu2AcffBAf+chHUFlZicrKSmzatOms42nmmczxNNIjjzwCIQQ++clPZneCVHAme0wNDAxg69atqKurg81mw+LFi/H000/naLaU7yZ7PN13331YsmQJSkpK0NDQgC9/+csIh8M5mi3lsxdeeAFbtmxBfX09hBB4/PHHJ3zM7t27ceGFF8Jms2HhwoX4xS9+kfV5ThYDHSoajz76KLZt24Y77rgDb775JlauXImWlhZ0d3enHL9792585jOfwXPPPYdXXnkFDQ0N2Lx5M06cOJHjmVM+muzxlHDkyBF85StfwUc+8pEczZQKxWSPqWg0iquuugpHjhzBr371K7S3t+PBBx/EnDlzcjxzykeTPZ4efvhh3HbbbbjjjjvQ1taGhx56CI8++ii+9rWv5XjmlI+CwSBWrlyJ7du3pzX+/fffx8c+9jFcfvnl2Lt3L770pS/hpptuQmtra5ZnOkmSqEisW7dObt26Nfm9YRiyvr5e3n333Wk9Xtd1WVZWJn/5y19ma4pUQKZyPOm6Ljds2CD/5V/+Rd5www3yE5/4RA5mSoVissfU/fffLxcsWCCj0WiupkgFZLLH09atW+UVV1wx6rZt27bJD3/4w1mdJxUeAPKxxx4765i/+7u/k83NzaNuu/baa2VLS0sWZzZ5XNGhohCNRrFnzx5s2rQpeZuiKNi0aRNeeeWVtJ4jFAohFouhqqoqW9OkAjHV4+lb3/oWampq8PnPfz4X06QCMpVj6re//S3Wr1+PrVu3wu1244ILLsBdd90FwzByNW3KU1M5njZs2IA9e/Ykt7cdPnwYTz/9NK655pqczJmKyyuvvDLq+AOAlpaWtM+5ckWb7gkQZUJPTw8Mw4Db7R51u9vtxoEDB9J6jr//+79HfX39mF9cmnmmcjy9+OKLeOihh7B3794czJAKzVSOqcOHD+PZZ5/Fddddh6effhqHDh3CLbfcglgshjvuuCMX06Y8NZXj6bOf/Sx6enpwySWXQEoJXdfxhS98gVvXaEq6urpSHn8+nw9DQ0MoKSmZppmNxhUdIgD33HMPHnnkETz22GOw2+3TPR0qMH6/H9dffz0efPBBVFdXT/d0qEiYpomamhr87Gc/w5o1a3Dttdfi61//Oh544IHpnhoVoN27d+Ouu+7CT3/6U7z55pv4zW9+g6eeegrf/va3p3tqRFnDFR0qCtXV1VBVFV6vd9TtXq8XtbW1Z33s97//fdxzzz34/e9/jxUrVmRzmlQgJns8dXR04MiRI9iyZUvyNtM0AQCapqG9vR2NjY3ZnTTltal8RtXV1cFisUBV1eRty5YtQ1dXF6LRKKxWa1bnTPlrKsfTP/zDP+D666/HTTfdBABYvnw5gsEg/vqv/xpf//rXoSi89k3pq62tTXn8uVyuvFnNAbiiQ0XCarVizZo12LVrV/I20zSxa9curF+/ftzHffe738W3v/1t7NixA2vXrs3FVKkATPZ4Wrp0Kfbt24e9e/cmv/7kT/4kWY2moaEhl9OnPDSVz6gPf/jDOHToUDJoBoCDBw+irq6OQc4MN5XjKRQKjQlmEkG0lDJ7k6WitH79+lHHHwA888wzZz3nmhbTXQ2BKFMeeeQRabPZ5C9+8Qu5f/9++dd//deyoqJCdnV1SSmlvP766+Vtt92WHH/PPfdIq9Uqf/WrX8nOzs7kl9/vn663QHlkssfTmVh1jc402WPq6NGjsqysTN56662yvb1dPvnkk7Kmpkb+4z/+43S9Bcojkz2e7rjjDllWVib/8z//Ux4+fFju3LlTNjY2yk9/+tPT9RYoj/j9fvnWW2/Jt956SwKQP/jBD+Rbb70lP/jgAymllLfddpu8/vrrk+MPHz4sHQ6H/OpXvyrb2trk9u3bpaqqcseOHdP1FlJioENF5cc//rGcO3eutFqtct26dfLVV19N3nfZZZfJG264Ifn9+eefLwGM+brjjjtyP3HKS5M5ns7EQIdSmewx9fLLL8uLLrpI2mw2uWDBAvmd73xH6rqe41lTvprM8RSLxeQ3v/lN2djYKO12u2xoaJC33HKL7O/vz/3EKe8899xzKc+JEsfQDTfcIC+77LIxj1m1apW0Wq1ywYIF8l//9V9zPu+JCCm5XklERERERMWFOTpERERERFR0GOgQEREREVHRYaBDRERERERFh4EOEREREREVHQY6RERERERUdBjoEBERERFR0WGgQ0RERERERYeBDhERERERFR0GOkREM9DGjRvxpS99acqPP3LkCIQQ2Lt3LwBg9+7dEEJgYGBgSo9PZbLPmS4hBB5//PGMPud4urq6cNVVV6G0tBQVFRU5eU0iIorTpnsCRESUe7/5zW9gsVgy9nwbNmxAZ2cnysvL0xrf0NCAzs5OVFdXZ2wO+eiHP/whOjs7sXfv3rR/NkRElBkMdIiIZqCqqqqMPp/VakVtbW3a41VVndT4QhONRmG1WtHR0YE1a9Zg0aJF0z0lIqIZh1vXiIhmoJFb1+bNm4e77roLf/VXf4WysjLMnTsXP/vZz0aNf+2117B69WrY7XasXbsWb7311qj7R24z8/l8KCkpwe9+97tRYx577DGUlZUhFAql3Lr29NNPY/HixSgpKcHll1+OI0eOjHr8N7/5TaxatWrUbffddx/mzZuX/P7111/HVVddherqapSXl+Oyyy7Dm2++Oe7PITGPRx55BBs2bIDdbscFF1yA559/ftS4d999Fx/96EfhdDrhdrtx/fXXo6enZ9TP89Zbb8WXvvQlVFdXo6WlBfPmzcOvf/1r/N//+38hhMBf/uVfjjsPIiLKPAY6RESEf/qnf0oGMLfccgu++MUvor29HQAQCATw8Y9/HE1NTdizZw+++c1v4itf+cq4z+VyufDxj38cDz/88Kjb/+M//gOf/OQn4XA4xjzm2LFj+NM//VNs2bIFe/fuxU033YTbbrtt0u/D7/fjhhtuwIsvvohXX30VixYtwjXXXAO/33/Wx331q1/F3/7t3+Ktt97C+vXrsWXLFvT29gIABgYGcMUVV2D16tV44403sGPHDni9Xnz6058e9Ry//OUvYbVa8dJLL+GBBx7A66+/jquvvhqf/vSn0dnZiR/96EeTfj9ERDR13LpGRES45pprcMsttwAA/v7v/x4//OEP8dxzz2HJkiV4+OGHYZomHnroIdjtdjQ3N+P48eP44he/OO7zXXfddbj++usRCoXgcDjg8/nw1FNP4bHHHks5/v7770djYyP+6Z/+CQCwZMkS7Nu3D/fee++k3scVV1wx6vuf/exnqKiowPPPP4+Pf/zj4z7u1ltvxZ/92Z8l57Jjxw489NBD+Lu/+zv85Cc/werVq3HXXXclx//85z9HQ0MDDh48iMWLFwMAFi1ahO9+97ujntdms6GkpKSot+kREeUrrugQERFWrFiR/H8hBGpra9Hd3Q0AaGtrw4oVK2C325Nj1q9ff9bnu+aaa2CxWPDb3/4WAPDrX/8aLpcLmzZtSjm+ra0NF1100ajbJnqNVLxeL26++WYsWrQI5eXlcLlcCAQCOHr06FkfN/K1NE3D2rVr0dbWBgB4++238dxzz8HpdCa/li5dCgDo6OhIPm7NmjWTni8REWUPV3SIiGhMBTYhBEzTnPLzWa1W/Pmf/zkefvhh/MVf/AUefvhhXHvttdC0qf/ZURQFUspRt8VisVHf33DDDejt7cWPfvQjnH/++bDZbFi/fj2i0eiUXzcQCGDLli0pV5fq6uqS/19aWjrl1yAioszjig4REZ3VsmXL8M477yAcDidve/XVVyd83HXXXYcdO3bA4/Hg2WefxXXXXXfW13jttddG3Xbma8yePRtdXV2jgp0z+/C89NJL+Ju/+Rtcc801aG5uhs1mG1U0YDwjX0vXdezZswfLli0DAFx44YXweDyYN28eFi5cOOqLwQ0RUf5ioENERGf12c9+FkII3Hzzzdi/fz+efvppfP/735/wcZdeeilqa2tx3XXXYf78+WO2po30hS98Ae+99x6++tWvor29HQ8//DB+8YtfjBqzceNGnDp1Ct/97nfR0dGB7du3j6nstmjRIvzbv/0b2tra8Mc//hHXXXcdSkpKJpzr9u3b8dhjj+HAgQPYunUr+vv78Vd/9VcAgK1bt6Kvrw+f+cxn8Prrr6OjowOtra248cYbYRjGhM9NRETTg4EOERGdldPpxBNPPIF9+/Zh9erV+PrXv55WkQAhBD7zmc/g7bffPutqDgDMnTsXv/71r/H4449j5cqVeOCBB0Yl/wPxVZ+f/vSn2L59O1auXInXXnttTPW3hx56CP39/bjwwgtx/fXX42/+5m9QU1Mz4Vzvuece3HPPPVi5ciVefPFF/Pa3v002M62vr8dLL70EwzCwefNmLF++HF/60pdQUVEBReGfUSKifCXkmRueiYiIZogjR45g/vz5eOutt8b06CEiosLGS1FERERERFR0GOgQEREREVHR4dY1IiIiIiIqOlzRISIiIiKiosNAh4iIiIiIig4DHSIiIiIiKjoMdIiIiIiIqOgw0CEiIiIioqLDQIeIiIiIiIoOAx0iIiIiIio6DHSIiIiIiKjo/P9hbzMArxQxfgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (10,5))\n",
        "sns.regplot(x=y_test,y=y_pred)\n",
        "plt.title('Linear regression for RGB dataset', fontsize = 20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l13Z8seQPdv2"
      },
      "source": [
        "## Random Forrest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 337,
      "metadata": {
        "id": "jfYRP1tpPg6t"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Creating and training model\n",
        "RandomForest_reg = RandomForestRegressor(n_estimators = 10, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 338,
      "metadata": {
        "id": "TYT3t60XPLt0"
      },
      "outputs": [],
      "source": [
        "RandomForest_reg.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = RandomForest_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 339,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WdUJuyZLPfqT",
        "outputId": "480f2070-ecfc-4206-e184-cec99fa1fc8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Random Forest  0.859504           0.857071                   0.81416   \n",
              "\n",
              "       RMSE  \n",
              "0  0.057677  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d474b34-3311-471f-9a0d-978dff097562\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.859504</td>\n",
              "      <td>0.857071</td>\n",
              "      <td>0.81416</td>\n",
              "      <td>0.057677</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d474b34-3311-471f-9a0d-978dff097562')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d474b34-3311-471f-9a0d-978dff097562 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d474b34-3311-471f-9a0d-978dff097562');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 339
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(RandomForest_reg,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "rf_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "rf_score.insert(0, 'Model', 'Random Forest')\n",
        "rf_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mmbpd94TPsIc"
      },
      "source": [
        "## Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 340,
      "metadata": {
        "id": "R-9Nr1mcPmsb"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# Creating and training model\n",
        "ridge_reg = Ridge(alpha=3, solver=\"cholesky\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 341,
      "metadata": {
        "id": "EAWrWzRtPuYQ"
      },
      "outputs": [],
      "source": [
        "ridge_reg.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = ridge_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 342,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "hJzNBy2YPwTM",
        "outputId": "4d342fe0-d3f1-492b-9225-2fce97d03ad2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Ridge Regression  0.518053           0.509707                  0.492712   \n",
              "\n",
              "       RMSE  \n",
              "0  0.106825  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae113763-876e-42fa-8f3b-4d8aabf60378\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.518053</td>\n",
              "      <td>0.509707</td>\n",
              "      <td>0.492712</td>\n",
              "      <td>0.106825</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae113763-876e-42fa-8f3b-4d8aabf60378')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ae113763-876e-42fa-8f3b-4d8aabf60378 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ae113763-876e-42fa-8f3b-4d8aabf60378');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(ridge_reg,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "rr_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "rr_score.insert(0, 'Model', 'Ridge Regression')\n",
        "rr_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdWrPuG1P3pX"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 349,
      "metadata": {
        "id": "rQkTpkOZP0bF"
      },
      "outputs": [],
      "source": [
        "from xgboost import XGBRegressor\n",
        "# create an xgboost regression model\n",
        "XGBR = XGBRegressor(n_estimators=1000, max_depth=7, eta=0.1, subsample=0.8, colsample_bytree=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 350,
      "metadata": {
        "id": "khAVfbI6P6Ru"
      },
      "outputs": [],
      "source": [
        "XGBR.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = XGBR.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 351,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WrPeKkScP7t4",
        "outputId": "32e3da4d-ce70-4be9-8a72-2e05fa961718"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score      RMSE\n",
              "0  XGBoost  0.925724           0.924438                  0.880065  0.041937"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8a068f3-65a9-417b-9557-91a151ee5297\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.925724</td>\n",
              "      <td>0.924438</td>\n",
              "      <td>0.880065</td>\n",
              "      <td>0.041937</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8a068f3-65a9-417b-9557-91a151ee5297')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e8a068f3-65a9-417b-9557-91a151ee5297 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e8a068f3-65a9-417b-9557-91a151ee5297');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 351
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(XGBR,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "XGBR_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "XGBR_score.insert(0, 'Model', 'XGBoost')\n",
        "XGBR_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import xgboost as xgb\n",
        "\n",
        "# Assuming that XGBR is your trained XGBoost model\n",
        "# If not, replace XGBR with your actual XGBoost model\n",
        "# Also, make sure to import the xgboost module (import xgboost as xgb)\n",
        "\n",
        "# Save the XGBoost model to a file\n",
        "with open('xgboost_model_v3.pickle', 'wb') as model_file:\n",
        "    pickle.dump(XGBR, model_file)\n",
        "\n",
        "# Now, the XGBoost model is saved as 'xgboost_model.pickle' in the current directory.\n"
      ],
      "metadata": {
        "id": "Uzk8MIigQ3_E"
      },
      "execution_count": 352,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxjZIA2rQDv2"
      },
      "source": [
        "## Recursive Feature Elimination (RFE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 353,
      "metadata": {
        "id": "rsKFWoMRP9fi"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# create pipeline\n",
        "rfe = RFE(estimator=RandomForestRegressor(), n_features_to_select=2000)\n",
        "model = RandomForestRegressor()\n",
        "rf_pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 354,
      "metadata": {
        "id": "OEX70B2KQGHM"
      },
      "outputs": [],
      "source": [
        "rf_pipeline.fit(X_train, y_train)\n",
        "# Model making a prediction on test data\n",
        "y_pred = rf_pipeline.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 355,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vnNPJHegQIHc",
        "outputId": "659e9eaf-751f-43ee-c395-1607e9978046"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  R2 Score  Adjusted R2 Score  \\\n",
              "0  Random Forest with RFE  0.894574           0.892748   \n",
              "\n",
              "   Cross Validated R2 Score      RMSE  \n",
              "0                  0.842259  0.049963  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43901c49-359a-4fa0-8ee6-0e880dc5f3d7\" class=\"colab-df-container\">\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2 Score</th>\n",
              "      <th>Adjusted R2 Score</th>\n",
              "      <th>Cross Validated R2 Score</th>\n",
              "      <th>RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest with RFE</td>\n",
              "      <td>0.894574</td>\n",
              "      <td>0.892748</td>\n",
              "      <td>0.842259</td>\n",
              "      <td>0.049963</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43901c49-359a-4fa0-8ee6-0e880dc5f3d7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-43901c49-359a-4fa0-8ee6-0e880dc5f3d7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-43901c49-359a-4fa0-8ee6-0e880dc5f3d7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 355
        }
      ],
      "source": [
        "ndf = [Reg_Models_Evaluation_Metrics(rf_pipeline,X_train,y_train,X_test,y_test,y_pred)]\n",
        "\n",
        "rfe_score = pd.DataFrame(data = ndf, columns=['R2 Score','Adjusted R2 Score','Cross Validated R2 Score','RMSE'])\n",
        "rfe_score.insert(0, 'Model', 'Random Forest with RFE')\n",
        "rfe_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uF5QxfUYQQzy"
      },
      "source": [
        "##  Final Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import pandas as pd\n",
        "\n",
        "# Load your dataset and split into features (X) and target (y)\n",
        "\n",
        "# Split data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature Scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Hyperparameter tuning using GridSearchCV\n",
        "\n",
        "# Random Forest\n",
        "rf_params = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [None, 10, 20],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "rf_grid_search = GridSearchCV(rf_model, rf_params, scoring='neg_mean_squared_error', cv=5)\n",
        "rf_grid_search.fit(X_train_scaled, y_train)\n",
        "best_rf_model = rf_grid_search.best_estimator_\n",
        "\n",
        "# Gradient Boosting\n",
        "gb_params = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "gb_model = GradientBoostingRegressor(random_state=42)\n",
        "gb_grid_search = GridSearchCV(gb_model, gb_params, scoring='neg_mean_squared_error', cv=5)\n",
        "gb_grid_search.fit(X_train_scaled, y_train)\n",
        "best_gb_model = gb_grid_search.best_estimator_\n",
        "\n",
        "# Support Vector Regression\n",
        "svr_params = {\n",
        "    'C': [0.1, 1, 10],\n",
        "    'epsilon': [0.01, 0.1, 0.2],\n",
        "    'kernel': ['linear', 'rbf']\n",
        "}\n",
        "svr_model = SVR()\n",
        "svr_grid_search = GridSearchCV(svr_model, svr_params, scoring='neg_mean_squared_error', cv=5)\n",
        "svr_grid_search.fit(X_train_scaled, y_train)\n",
        "best_svr_model = svr_grid_search.best_estimator_\n",
        "\n",
        "# Evaluate Models and compare them\n",
        "\n",
        "# Store the best models in a list\n",
        "models = [best_rf_model, best_gb_model, best_svr_model]\n",
        "model_names = ['Random Forest', 'Gradient Boosting', 'Support Vector Regression']\n",
        "results = []\n",
        "\n",
        "for model, name in zip(models, model_names):\n",
        "    y_pred = model.predict(X_test_scaled)\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "    rmse = mse ** 0.5\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "    results.append((name, mse, rmse, r2))\n",
        "\n",
        "# Convert the results to a DataFrame\n",
        "results_df = pd.DataFrame(results, columns=['Model', 'MSE', 'RMSE', 'R-squared'])\n",
        "print(results_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzpnuazB74ab",
        "outputId": "5087475a-da89-4859-b8c3-683648baa65d"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                       Model       MSE      RMSE  R-squared\n",
            "0              Random Forest  0.001695  0.041167   0.933435\n",
            "1          Gradient Boosting  0.001565  0.039557   0.938539\n",
            "2  Support Vector Regression  0.006087  0.078019   0.760913\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 357,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stp3Us99u7cn",
        "outputId": "5e6751c9-07d6-4e8c-9344-f247e17f4a0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Model       MSE      RMSE  R-squared\n",
            "0    Ensemble  0.002137  0.046224   0.916076\n",
            "1       Ridge  0.009027  0.095012   0.645414\n",
            "2  Complex RF  0.001683  0.041029   0.933879\n",
            "3     XGBoost  0.001802  0.042450   0.929219\n",
            "4     Stacked  0.002162  0.046495   0.915087\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import xgboost as xgb\n",
        "from sklearn.ensemble import StackingRegressor\n",
        "import pandas as pd\n",
        "\n",
        "# Load your dataset and split into features (X) and target (y)\n",
        "# ...\n",
        "\n",
        "# Split data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature Scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Assuming you've imported the necessary libraries and loaded the data\n",
        "\n",
        "# Ensemble of Models\n",
        "ensemble_predictions = (best_rf_model.predict(X_test_scaled) +\n",
        "                        best_gb_model.predict(X_test_scaled) +\n",
        "                        best_svr_model.predict(X_test_scaled)) / 3\n",
        "\n",
        "\n",
        "# Feature Engineering\n",
        "poly = PolynomialFeatures(degree=2)  # You can experiment with different degrees\n",
        "X_train_poly = poly.fit_transform(X_train_scaled)\n",
        "X_test_poly = poly.transform(X_test_scaled)\n",
        "\n",
        "# Regularization with Polynomial Features\n",
        "ridge_model = Ridge(alpha=1.0)\n",
        "ridge_model.fit(X_train_poly, y_train)\n",
        "ridge_predictions = ridge_model.predict(X_test_poly)\n",
        "\n",
        "# Increasing Model Complexity\n",
        "best_rf_model_complex = RandomForestRegressor(n_estimators=500, max_depth=20, random_state=42)\n",
        "best_rf_model_complex.fit(X_train_scaled, y_train)\n",
        "rf_complex_predictions = best_rf_model_complex.predict(X_test_scaled)\n",
        "\n",
        "# XGBoost with Hyperparameter Tuning\n",
        "xgb_params = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5]\n",
        "}\n",
        "xgb_model = xgb.XGBRegressor(random_state=42)\n",
        "xgb_grid_search = GridSearchCV(xgb_model, xgb_params, scoring='neg_mean_squared_error', cv=5)\n",
        "xgb_grid_search.fit(X_train_scaled, y_train)\n",
        "best_xgb_model = xgb_grid_search.best_estimator_\n",
        "xgb_predictions = best_xgb_model.predict(X_test_scaled)\n",
        "\n",
        "# Stacking of Models\n",
        "estimators = [('rf', best_rf_model), ('gb', best_gb_model), ('svr', best_svr_model)]\n",
        "stacked_model = StackingRegressor(estimators=estimators, final_estimator=best_rf_model)\n",
        "stacked_model.fit(X_train_scaled, y_train)\n",
        "stacked_predictions = stacked_model.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate Models and compare their performance\n",
        "models = [ensemble_predictions, ridge_predictions, rf_complex_predictions, xgb_predictions, stacked_predictions]\n",
        "model_names = ['Ensemble', 'Ridge', 'Complex RF', 'XGBoost', 'Stacked',]\n",
        "results = []\n",
        "\n",
        "for model, name in zip(models, model_names):\n",
        "    mse = mean_squared_error(y_test, model)\n",
        "    rmse = mse ** 0.5\n",
        "    r2 = r2_score(y_test, model)\n",
        "    results.append((name, mse, rmse, r2))\n",
        "\n",
        "results_df = pd.DataFrame(results, columns=['Model', 'MSE', 'RMSE', 'R-squared'])\n",
        "print(results_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "McsrODoZ75-C",
        "outputId": "803eb21a-7c3d-4b92-98b8-1b3abf947ed8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Model       MSE      RMSE  R-squared\n",
              "0    Ensemble  0.002137  0.046224   0.916076\n",
              "1       Ridge  0.009027  0.095012   0.645414\n",
              "2  Complex RF  0.001683  0.041029   0.933879\n",
              "3     XGBoost  0.001802  0.042450   0.929219\n",
              "4     Stacked  0.002162  0.046495   0.915087"
            ],
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              "      <td>0.645414</td>\n",
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              "      <th>2</th>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-718c1bd5-9793-45ba-9e85-c5b344de1538 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 358
        }
      ],
      "source": [
        "results_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "rrtXeQ9076g-",
        "outputId": "636fd647-d3df-4499-f7d9-0e2ce641db56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "f, axe = plt.subplots(1,1, figsize=(18,6))\n",
        "\n",
        "results_df.sort_values(by=['R-squared'], ascending=False, inplace=True)\n",
        "\n",
        "sns.barplot(x='R-squared', y='Model', data = results_df, ax = axe)\n",
        "axe.set_xlabel('R-squared', size=16)\n",
        "axe.set_ylabel('Model')\n",
        "axe.set_xlim(0,1.0)\n",
        "\n",
        "axe.set(title='Model Performance for RGB dataset')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 362,
      "metadata": {
        "id": "JiFC6HaW9f6p"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "# Save the trained models to pickle files\n",
        "model_names = ['best_rf_model_v3', 'best_gb_model_v3', 'best_svr_model_v3', 'ridge_model_v3', 'best_rf_model_complex_v3', 'best_xgb_model_v3', 'stacked_model_v3']\n",
        "models = [best_rf_model, best_gb_model, best_svr_model, ridge_model, best_rf_model_complex, best_xgb_model, stacked_model]\n",
        "\n",
        "for model, model_name in zip(models, model_names):\n",
        "    with open(f'{model_name}.pickle', 'wb') as model_file:\n",
        "        pickle.dump(model, model_file)\n",
        "\n",
        "# Load the models from pickle files\n",
        "loaded_models = {}\n",
        "for model_name in model_names:\n",
        "    with open(f'{model_name}.pickle', 'rb') as model_file:\n",
        "        loaded_models[model_name] = pickle.load(model_file)\n",
        "\n",
        "# Example: You can use the loaded models for inference\n",
        "loaded_rf_model = loaded_models['best_rf_model_v3']\n",
        "loaded_gb_model = loaded_models['best_gb_model_v3']\n",
        "\n",
        "# Perform inference with loaded models\n",
        "loaded_rf_predictions = loaded_rf_model.predict(X_test_scaled)\n",
        "loaded_gb_predictions = loaded_gb_model.predict(X_test_scaled)\n",
        "\n",
        "# You can use the loaded models for evaluation or other tasks\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "65Mf06n4PvjO"
      },
      "execution_count": 360,
      "outputs": []
    }
  ],
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      "mount_file_id": "1pHrKXM90oE3UXgdrt3FTcQNPTZC-XIRB",
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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