Final_Version_Redda_Project_90__Accuracy.ipynb

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        "<a href=\"https://colab.research.google.com/github/IsinghGitHub/CellStrat/blob/master/Final_Version_Redda_Project_90__Accuracy.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": 333,
      "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 RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 334,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "B9E4vXwGgDt4",
        "outputId": "a0f06010-67b7-401d-a100-3a151bf99820"
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      "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       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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              "      <th>ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>AIT160</td>\n",
              "      <td>AIT</td>\n",
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              "      <td>Consultant</td>\n",
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              "      <td>70%</td>\n",
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              "      <td>#REF!</td>\n",
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              "      <td>70%</td>\n",
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              "      <td>42</td>\n",
              "      <td>16</td>\n",
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              "    <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</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ORA335</td>\n",
              "      <td>ORA</td>\n",
              "      <td>Marketing Assistant</td>\n",
              "      <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</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",
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          },
          "metadata": {},
          "execution_count": 334
        }
      ],
      "source": [
        "\"\"\" Load in the data and examine the head. \"\"\"\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": "markdown",
      "metadata": {
        "id": "7CqFCoz2rGUm"
      },
      "source": [
        "# 2 | Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 335,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGRvLHSogXo3",
        "outputId": "e7ce112f-fc7e-47f6-9e84-bb00f82de2cf"
      },
      "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": 335
        }
      ],
      "source": [
        "df.columns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDZmTvOgrjKr"
      },
      "source": [
        " ### 2.1 Rename columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 336,
      "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": 337,
      "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": 338,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPSf2ZbO_IYB",
        "outputId": "63ff7233-f5d9-4ab4-f15f-90765637d01c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                  0\n",
            "company id          0\n",
            "job                 0\n",
            "department          0\n",
            "mbti                7\n",
            "predictor1          0\n",
            "predictor2          0\n",
            "predictor3          0\n",
            "predictor4          0\n",
            "ctar                0\n",
            "aggregatetotpred    0\n",
            "decisionstd2        0\n",
            "individualperf      6\n",
            "gender              0\n",
            "age                 0\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": 339,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02RG8K7JBmI3",
        "outputId": "edb3a2b5-831e-4f85-8342-6f631825839e"
      },
      "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          0.000000\n",
            "mbti                0.233333\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      0.200000\n",
            "gender              0.000000\n",
            "age                 0.000000\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": 340,
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        "colab": {
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        "id": "iKXNq2Y_hBuf",
        "outputId": "5bafcef8-970f-47b1-e656-27483d0f7f89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ],
      "source": [
        "df.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 341,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpgDpWDKhEC6",
        "outputId": "c67b8342-7863-43f9-bbf0-a0bd009cfa93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 16 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   id                3000 non-null   object\n",
            " 1   company id        3000 non-null   object\n",
            " 2   job               3000 non-null   object\n",
            " 3   department        3000 non-null   object\n",
            " 4   mbti              2993 non-null   object\n",
            " 5   predictor1        3000 non-null   object\n",
            " 6   predictor2        3000 non-null   object\n",
            " 7   predictor3        3000 non-null   object\n",
            " 8   predictor4        3000 non-null   object\n",
            " 9   ctar              3000 non-null   object\n",
            " 10  aggregatetotpred  3000 non-null   object\n",
            " 11  decisionstd2      3000 non-null   object\n",
            " 12  individualperf    2994 non-null   object\n",
            " 13  gender            3000 non-null   object\n",
            " 14  age               3000 non-null   int64 \n",
            " 15  tenure            3000 non-null   int64 \n",
            "dtypes: int64(2), object(14)\n",
            "memory usage: 375.1+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 343,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAwepsY4ipbU",
        "outputId": "c551a853-a708-401f-baf0-444b2632552c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IT                                979\n",
              "Sales                             816\n",
              "Finance                           425\n",
              "Supply Chain                      339\n",
              "Marketing                          97\n",
              "Technical                          75\n",
              "After Sales                        61\n",
              "Payroll                            55\n",
              "General Management                 48\n",
              "Sales/IT                           36\n",
              "Sales/Marketing                    30\n",
              "Sales SPECIALIST CYBERSECURITE     28\n",
              "Sales/Finance                       6\n",
              "Human Ressources                    5\n",
              "Name: department, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ],
      "source": [
        "\"\"\" Investigate categorical columns that seem irrelevent. \"\"\"\n",
        "df.department.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 344,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RgZyIgEmxxk",
        "outputId": "5a2170fc-57ab-46c4-c1e5-d173baac603b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ISTJ    1016\n",
              "ESTJ     991\n",
              "ENTJ     273\n",
              "INTJ     218\n",
              "ESFJ     165\n",
              "ESTP      69\n",
              "ENFJ      52\n",
              "ISFJ      48\n",
              "ENTP      44\n",
              "ISTP      33\n",
              "ESFP      28\n",
              "INFJ      23\n",
              "ENFP      11\n",
              "INTP       9\n",
              "INFP       8\n",
              "ISFP       5\n",
              "Name: mbti, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 344
        }
      ],
      "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": 346,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTSiYEXgsR0G",
        "outputId": "2a6a415b-e162-4d44-d9b0-c3eb1ff39961"
      },
      "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": 347,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o32UvDPIsZL8",
        "outputId": "66a9b9c4-62a8-485c-9ecf-926c43b5e1c2"
      },
      "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": 348,
      "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": 349,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvQhKg2qq0wS",
        "outputId": "f126ae0a-9a12-4e68-89ec-5df606c7000e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical fetures:  ['id', 'job', 'department', 'mbti', 'predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar', 'individualperf', 'age', 'tenure']\n",
            "Categorical fetures:  ['company id', 'aggregatetotpred', '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": 350,
      "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": 351,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "A92s79nvnSBq",
        "outputId": "af250736-19e3-42a7-c6ea-e2e7745ca8b7"
      },
      "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       3  \n",
              "1            #REF!        Hired            70%      M   42      16  \n",
              "2            #REF!        Hired            78%      M   33       0  \n",
              "3            #REF!    Not Hired            40%      M   23       0  \n",
              "4            #REF!    Not Hired            60%      F   19       0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2db80a00-a588-4ab6-8cb0-4fcd372de436\" 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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <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",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AIT160</td>\n",
              "      <td>ENTJ</td>\n",
              "      <td>64%</td>\n",
              "      <td>33%</td>\n",
              "      <td>33%</td>\n",
              "      <td>42%</td>\n",
              "      <td>0%</td>\n",
              "      <td>#REF!</td>\n",
              "      <td>Hired</td>\n",
              "      <td>74%</td>\n",
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              "      <td>ABS045</td>\n",
              "      <td>ESTJ</td>\n",
              "      <td>63%</td>\n",
              "      <td>70%</td>\n",
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              "      <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</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</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</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</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-2db80a00-a588-4ab6-8cb0-4fcd372de436')\"\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-2db80a00-a588-4ab6-8cb0-4fcd372de436 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-2db80a00-a588-4ab6-8cb0-4fcd372de436');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-25de70d9-f6ea-4cc0-91a4-cb0960ab99df')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-25de70d9-f6ea-4cc0-91a4-cb0960ab99df 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": 351
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 352,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "SiH_Z6TDn0r2",
        "outputId": "8ee8db28-0db4-43db-ad97-7da3071501d1"
      },
      "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": 352
        }
      ],
      "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": 353,
      "metadata": {
        "id": "s4IExS-4n2sz"
      },
      "outputs": [],
      "source": [
        "#df_dum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 354,
      "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": 356,
      "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']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 357,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQOzasBu5uMj",
        "outputId": "97b2ea2a-f572-4b24-fd6c-7cf7519e968e"
      },
      "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": 357
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "metadata": {
        "id": "Ugdblph1kS9D"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns=columns_to_drop)\n",
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvpEsNnIqbz8",
        "outputId": "d7b19002-095e-4d21-bba2-ff45741f12e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'decisionstd2', 'individualperf', 'gender', 'age', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 359
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DkmWdnt8ZkEV",
        "outputId": "e7f3d0ed-bca8-4712-a6dd-10677d92cd32"
      },
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     predictor1 predictor2 predictor3 predictor4  ctar decisionstd2  \\\n",
              "0           64%        33%        33%        42%    0%        Hired   \n",
              "1           63%        70%        25%        33%    8%        Hired   \n",
              "2           72%        60%        25%        33%    8%        Hired   \n",
              "3           76%       -17%       -17%       -33%   -8%    Not Hired   \n",
              "4           65%        17%         0%         8%    8%    Not Hired   \n",
              "...         ...        ...        ...        ...   ...          ...   \n",
              "2968        49%       -13%       -17%         0%   17%    Not Hired   \n",
              "2975        65%        17%        33%        -8%    0%    Not Hired   \n",
              "2988        72%        27%         0%        33%  -17%    Not Hired   \n",
              "2991        36%        50%        17%        25%    8%    Not Hired   \n",
              "2992        38%        40%        17%        25%    8%    Not Hired   \n",
              "\n",
              "     individualperf gender  age  tenure  \n",
              "0               74%      F   34       3  \n",
              "1               70%      M   42      16  \n",
              "2               78%      M   33       0  \n",
              "3               40%      M   23       0  \n",
              "4               60%      F   19       0  \n",
              "...             ...    ...  ...     ...  \n",
              "2968            50%      F   31       0  \n",
              "2975            55%      M   22       0  \n",
              "2988            55%      F   40       0  \n",
              "2991            70%      F   30       6  \n",
              "2992            60%      M   33      12  \n",
              "\n",
              "[1346 rows x 10 columns]"
            ],
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              "<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>decisionstd2</th>\n",
              "      <th>individualperf</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <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>Hired</td>\n",
              "      <td>74%</td>\n",
              "      <td>F</td>\n",
              "      <td>34</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>Hired</td>\n",
              "      <td>70%</td>\n",
              "      <td>M</td>\n",
              "      <td>42</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>Hired</td>\n",
              "      <td>78%</td>\n",
              "      <td>M</td>\n",
              "      <td>33</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>Not Hired</td>\n",
              "      <td>40%</td>\n",
              "      <td>M</td>\n",
              "      <td>23</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>Not Hired</td>\n",
              "      <td>60%</td>\n",
              "      <td>F</td>\n",
              "      <td>19</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2968</th>\n",
              "      <td>49%</td>\n",
              "      <td>-13%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>17%</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>50%</td>\n",
              "      <td>F</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2975</th>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>0%</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>55%</td>\n",
              "      <td>M</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>72%</td>\n",
              "      <td>27%</td>\n",
              "      <td>0%</td>\n",
              "      <td>33%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>55%</td>\n",
              "      <td>F</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>36%</td>\n",
              "      <td>50%</td>\n",
              "      <td>17%</td>\n",
              "      <td>25%</td>\n",
              "      <td>8%</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>70%</td>\n",
              "      <td>F</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>38%</td>\n",
              "      <td>40%</td>\n",
              "      <td>17%</td>\n",
              "      <td>25%</td>\n",
              "      <td>8%</td>\n",
              "      <td>Not Hired</td>\n",
              "      <td>60%</td>\n",
              "      <td>M</td>\n",
              "      <td>33</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1346 rows × 10 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "          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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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uit_OjX5jZga"
      },
      "source": [
        "## Checking unique values in all the columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 361,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K-X7vqNQ2cH",
        "outputId": "bcb849b4-bbbb-4405-f3cc-c9660c1f4a6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values:\n",
            "predictor1 52\n",
            "\n",
            "predictor2 45\n",
            "\n",
            "predictor3 18\n",
            "\n",
            "predictor4 18\n",
            "\n",
            "ctar 17\n",
            "\n",
            "decisionstd2 2\n",
            "\n",
            "individualperf 50\n",
            "\n",
            "gender 2\n",
            "\n",
            "age 36\n",
            "\n",
            "tenure 20\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",
        "![image.png](data:image/png;base64,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)Heart Disease"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BSUydwwtnL4"
      },
      "source": [
        "## One hot encoding with Pandas (get_dummies)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 362,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze4Fjo05uu4g",
        "outputId": "f392baf9-6bed-4e48-a1ec-ead034dcdd9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'decisionstd2', 'individualperf', 'gender', 'age', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 362
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create dummy variables for categorical columns\n",
        "categorical_columns = [ 'gender','decisionstd2']  # 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())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ki0mQ2asaR0a",
        "outputId": "446c6d8f-7c21-40b4-f17c-98094d4462e0"
      },
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  predictor1 predictor2 predictor3 predictor4 ctar individualperf  age  \\\n",
            "0        64%        33%        33%        42%   0%            74%   34   \n",
            "1        63%        70%        25%        33%   8%            70%   42   \n",
            "2        72%        60%        25%        33%   8%            78%   33   \n",
            "3        76%       -17%       -17%       -33%  -8%            40%   23   \n",
            "4        65%        17%         0%         8%   8%            60%   19   \n",
            "\n",
            "   tenure  gender_M  decisionstd2_Not Hired  \n",
            "0       3         0                       0  \n",
            "1      16         1                       0  \n",
            "2       0         1                       0  \n",
            "3       0         1                       1  \n",
            "4       0         0                       1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dummies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SKnNaWwsb41u",
        "outputId": "be595953-962a-4c4e-ad2e-d7528ecb2d7d"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     predictor1 predictor2 predictor3 predictor4  ctar individualperf  age  \\\n",
              "0           64%        33%        33%        42%    0%            74%   34   \n",
              "1           63%        70%        25%        33%    8%            70%   42   \n",
              "2           72%        60%        25%        33%    8%            78%   33   \n",
              "3           76%       -17%       -17%       -33%   -8%            40%   23   \n",
              "4           65%        17%         0%         8%    8%            60%   19   \n",
              "...         ...        ...        ...        ...   ...            ...  ...   \n",
              "2968        49%       -13%       -17%         0%   17%            50%   31   \n",
              "2975        65%        17%        33%        -8%    0%            55%   22   \n",
              "2988        72%        27%         0%        33%  -17%            55%   40   \n",
              "2991        36%        50%        17%        25%    8%            70%   30   \n",
              "2992        38%        40%        17%        25%    8%            60%   33   \n",
              "\n",
              "      tenure  gender_M  decisionstd2_Not Hired  \n",
              "0          3         0                       0  \n",
              "1         16         1                       0  \n",
              "2          0         1                       0  \n",
              "3          0         1                       1  \n",
              "4          0         0                       1  \n",
              "...      ...       ...                     ...  \n",
              "2968       0         0                       1  \n",
              "2975       0         1                       1  \n",
              "2988       0         0                       1  \n",
              "2991       6         0                       1  \n",
              "2992      12         1                       1  \n",
              "\n",
              "[1346 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9ed22f0-0647-4366-a948-3582f65643b9\" 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",
              "      <th>decisionstd2_Not Hired</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</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2968</th>\n",
              "      <td>49%</td>\n",
              "      <td>-13%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>0%</td>\n",
              "      <td>17%</td>\n",
              "      <td>50%</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2975</th>\n",
              "      <td>65%</td>\n",
              "      <td>17%</td>\n",
              "      <td>33%</td>\n",
              "      <td>-8%</td>\n",
              "      <td>0%</td>\n",
              "      <td>55%</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>72%</td>\n",
              "      <td>27%</td>\n",
              "      <td>0%</td>\n",
              "      <td>33%</td>\n",
              "      <td>-17%</td>\n",
              "      <td>55%</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>36%</td>\n",
              "      <td>50%</td>\n",
              "      <td>17%</td>\n",
              "      <td>25%</td>\n",
              "      <td>8%</td>\n",
              "      <td>70%</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>38%</td>\n",
              "      <td>40%</td>\n",
              "      <td>17%</td>\n",
              "      <td>25%</td>\n",
              "      <td>8%</td>\n",
              "      <td>60%</td>\n",
              "      <td>33</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1346 rows × 10 columns</p>\n",
              "</div>\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deal with Percentage Columns"
      ],
      "metadata": {
        "id": "8MBmtfPEcv31"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of columns containing percentage values\n",
        "percentage_columns = ['predictor1',\t'predictor2',\t'predictor3',\t'predictor4',\t'ctar',\t'individualperf']\n",
        "\n",
        "# Loop through the percentage columns and convert to numerical values\n",
        "for col in percentage_columns:\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())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-vDdhuWcvie",
        "outputId": "282ec009-30b1-4349-b481-6cff6c4e0093"
      },
      "execution_count": 365,
      "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   \n",
            "1        0.63        0.70        0.25        0.33  0.08            0.70   42   \n",
            "2        0.72        0.60        0.25        0.33  0.08            0.78   33   \n",
            "3        0.76       -0.17       -0.17       -0.33 -0.08            0.40   23   \n",
            "4        0.65        0.17        0.00        0.08  0.08            0.60   19   \n",
            "\n",
            "   tenure  gender_M  decisionstd2_Not Hired  \n",
            "0       3         0                       0  \n",
            "1      16         1                       0  \n",
            "2       0         1                       0  \n",
            "3       0         1                       1  \n",
            "4       0         0                       1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 366,
      "metadata": {
        "id": "Nc_PZqkou8ub"
      },
      "outputs": [],
      "source": [
        "df_dummy = data_dummies.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "O6MYXufhvAli",
        "outputId": "d72e711f-8f81-40f2-8a8c-be5ec9eb2992"
      },
      "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",
              "2968        0.49       -0.13       -0.17        0.00  0.17            0.50   \n",
              "2975        0.65        0.17        0.33       -0.08  0.00            0.55   \n",
              "2988        0.72        0.27        0.00        0.33 -0.17            0.55   \n",
              "2991        0.36        0.50        0.17        0.25  0.08            0.70   \n",
              "2992        0.38        0.40        0.17        0.25  0.08            0.60   \n",
              "\n",
              "      age  tenure  gender_M  decisionstd2_Not Hired  \n",
              "0      34       3         0                       0  \n",
              "1      42      16         1                       0  \n",
              "2      33       0         1                       0  \n",
              "3      23       0         1                       1  \n",
              "4      19       0         0                       1  \n",
              "...   ...     ...       ...                     ...  \n",
              "2968   31       0         0                       1  \n",
              "2975   22       0         1                       1  \n",
              "2988   40       0         0                       1  \n",
              "2991   30       6         0                       1  \n",
              "2992   33      12         1                       1  \n",
              "\n",
              "[1346 rows x 10 columns]"
            ],
            "text/html": [
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              "    <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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              "      <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</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.65</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
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              "      <td>0.50</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2975</th>\n",
              "      <td>0.65</td>\n",
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              "      <td>0.55</td>\n",
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              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>0.72</td>\n",
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              "      <td>0.33</td>\n",
              "      <td>-0.17</td>\n",
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              "      <td>40</td>\n",
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              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>0.36</td>\n",
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              "      <td>0.17</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.70</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>0.38</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1346 rows × 10 columns</p>\n",
              "</div>\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-b34ff39a-0e2a-4d27-9cb6-1f6af9d1b99b 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-b34ff39a-0e2a-4d27-9cb6-1f6af9d1b99b');\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-53dcd874-70dc-485f-b41c-6ddefc1a0c06\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53dcd874-70dc-485f-b41c-6ddefc1a0c06')\"\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",
              "    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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-53dcd874-70dc-485f-b41c-6ddefc1a0c06 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": 367
        }
      ],
      "source": [
        "df_dummy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 368,
      "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",
      "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))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "lmVdiPci8Uwg",
        "outputId": "06ccadd9-672f-4ac7-ca6d-a361b801aee0"
      },
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ],
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7C+AOXTwfo19+nKQ3BrzKmjz/YkWKFNHUiRM0cdxY9e7VS999P0LbbpAyGv8e2dlGlQkN1rMvdlaZssFq3qqpmrZsoj8WLi/srv0n1QypoyHdxpgetgbbfLd1PuGshsx5T9/+/pHWH1ilLg16ydPdcnYGwNpYusb6xRdf5Cp38eJFZWVlydPT02y7p6enzp2zfCPn9QYMGCAfH588r3XfDczwuEPX5220sbGR0WjMd3sODg568sknNW3aNHXq1EnTpk1Tx44d72iRcoPBkCsfW0ZGhtnzrl27KiYmRsOHD1dgYKAcHR318MMPmxYRz4/k5GS1adPG4mwTb29v0/8XLZq/nLUDBw5U3759zbalJ9144Vdr4+7uJltbW8XGxpptj42NUwmPEnnWMxgM8vfzkySFh4UqMjJSEyZPVs0a1e9pf3HrstJSlW00ys7Z/N+/rVPRXLM+/pGZcknZRqN0zf58OT5WdkWKSQaDdM2xx66Yq4r4BOrMCi6M3S/cXIrJYDAo9roFyuMSE+XhnvuC9unz53XuYowGfjvStO2fY3njri9p8pDB8r1mTYfdhw4r6uw5ffhqr3s0AtwJNxcX2RoMir1ukdvYhASVKO6eq/zps+d09vwFvfvZl6Ztxiuff4P2HTVt1HD5epML+n7gZvquNl+8Ni42TiVK3PiupelTZmjqxGn6buRQlQ0NydVmUHCQWfnAoEDtZk0uoMC4uLrIYDDkWqA8IS4x113ht+r40QglxCfqnVffM20zGo06sOegls5fpmkLJ8rWlvsS7xfu7u55nI/FqsQN7kw1Px8LU+SJSE2YNFk1azAL937h4uYqg8GQa4Hy+LgEuVv4bXar3D2Kyy/A12ybX4CvNv7FovWFYe/J7Tox5+qaGv8sTO7i7KbEa2Z5uDi76XRM3lk0JCnLmKWLiTk3OJ+6GKmAUsFqUOlRzVr3yz3oOaxRtvJ/PbiwWbrGei8y6Hz55ZeaMWOG1qxZYzZB4G4j4HEPlS9fXpMnT1ZaWprpQ9y4ceNN63Xp0kXNmjXTvn37tGrVKn366ac3fI9/ZoP84/r3KFWqlM6dO6fs7GzZXMn1f+2i55K0fv16/fDDD2rZsqWknLVBrl1cPC8bN25U/fr1JUmZmZnatm2b+vTpI0mqUaOGZs+eraCgoDsK2OTF0tSqxMtpd/19CpO9vb3KhYdpy9Ztanjl72w0GrVl2zY91aH9LbdjzM7W5euCXChkRqPSLkariE+Akk9c/QFWxDdA8fssz8ZJjT4t15DyZtvs3Yor81KyWbBDktzCKikrLUXJUcfvft+RL/Z2dgoPCtT2/QdUr1ZO8NFoNGrbvgN6olnjXOUDvL01/vOPzLaN+22eUtPS1OfZTip93YXURWvWKSw4UGUD/e/ZGJB/9vb2Cgspo22796j+QzkpLYxGo7bt3qP2LVvkKh/g56tJw4eabRszdYZSUlP1Ro/uKl0y76A3Cpa9vb3CyoVp25Ztqn9lrR2j0ahtW7er/VNP5Flv6qRpmvzLFA39/muVq1AuV5vlK5TLNdM26mSUvLzM76YCcO/Y29upTGiw9uzYp9p1aknK2b/37NyrFm2b56vNytUqauhPX5pt+2Hoz/Lx91a7p9sQ7LjP5JyPhWvLtq1q2OCa87Gt2/TUldTOt8Jo5HzsfmNvb6eQsDLavWOPHqybk/7ZaDRq9469avn4o/lut3zFcJ05ddZs25lTZ1XKs9Qd9Rf5k56RpvQM8+tECSnxCvOtqNOxOb+zHO2dFFiqjNbtv731BGxsDKYACmDtLF1jtaRkyZKytbVVdHS02fbo6OgbLksgSd98842+/PJLrVixwixj0r3Ar6l76JlnnpGNjY169uyp/fv3a/Hixfrmm29uWq9+/fry8vJSly5dFBwcnCtl1bVefvllHTlyRP3799ehQ4c0bdo0TZgwwaxMw4YNdeHCBQ0ZMkTHjh3TqFGjtGTJErMyoaGhmjx5sg4cOKBNmzapS5cucnZ2vmlfR40apblz5+rgwYN69dVXFRcXpxdeeEGS9Oqrryo2NladO3fWli1bdOzYMf3xxx/q3r27srKybto2cjzTsaPmLViohYuXKCIyUl9+M1Spaalq0yonOPXh4E818sfRpvK/TJqsTZu36NTpM4qIjNSU6TO0eOkfeqx5/k7KcO/E7d0qt/Aqcg2tKAd3D3nWbSaDnb1pzQ2vBo+pZK2rC1XHH9glg6OTSj/cWPauxVXUv4xKVHtQcQdyB0jcQisp4cg+s9kgKHxPPdZMC9f8paV//a0Tp8/quwlTlZZ+WY/VrytJ+nz0OP08c44kydHBXmX8fc0exYo4y9nJSWX8fWV/TSD5Umqq1m7eplYsVn5f6/R4ay1YvlJLVq1RZNQpfTN6jFLT0tWqSSNJ0uBhIzR68lRJkqODg8oEBpg9ihUtoiLOzioTGJBrhikKV8dnntbC3xdpycKlioyI1NCvvlVqaqpatn5MkvTph59p9KifTeWnTpymcT+N17sfDJCXt5diLsYo5mKMUlJSTGU6P9tJq5av1vx5C3Qq6pRmz5qjv9dt0BNPtivo4eEOGBydVTQwVEUDQyVJTqV9VDQwVI4lCFxZi9btH9PKJau1ZvmfOnXytMaM+EXpaemmXPwjhvyoqeNnmMpnZGQq4likIo5FKjMjUzExcYo4Fqmzp3PSPDgXcVZAkL/Zw9HJUS4uLgoI4qaF+9EznTpq3vwFWrh4cc752NffKDUtTW1at5IkffjJYI388UdT+V8mTdKmzZt16vTpnPOxadO1eOlSPfYo52P3mzYdWmnF4lVavWytTp04pZ++H6v0tHQ1frShJGn4VyM1Zdw0U/mMjExFHI1UxNGc/Tv2Ypwijl7dvyWpdYeWOnzgiH6bNldnT5/Tn6vWafnilfkOkuLuW7t3qZpXf1yVAqrLu7ifnm34shJS4rXnxDZTmVdbvqt6Fa6m22n9wNMK8QqXR7GS8i7up9YPPK2y3uW07ejfpjIuzm7y9QhQSdec73hvDz/5egSoiGP+spoA9yMHBwfVrFnTbMHxfxYgv9ESC0OGDNHgwYO1dOlS1apV6573kxke91CxYsW0YMECvfzyy6pevboqVKigr776yrTId15sbGzUuXNnDRkyRIMG3Xih2oCAAM2ePVtvvfWWRowYodq1a+vzzz83BR2knFkgP/zwgz7//HMNHjxYHTp0UL9+/fTzz1dPvMeNG6devXqpRo0a8vf31+eff65+/frddIxffvmlvvzyS+3cuVNly5bV/PnzVbJkSUmSj4+P1q9frwEDBqh58+ZKT09XYGCgWrRoIYOBWNutat60ieLj4/XT2HGKiY1VWGhZfT/0G9MU6nPR0aaZO5KUlpamr4Z+q/Pnz8vR0VGBgYH6ZNAHat60SWENAXlIOn5Itk5FVLJGXdkWKaL0mAs6tfQ3ZaXmXPCyL+ZqFrDIvJSkU0t/U+mHGimofVdlpiQrbu92xe7ebNZuEd9A2bu4KuEQi5Xfbxo/9IDik5L0y+zfFZuQqLIB/hrS/w3TQubRMbFm+/OtWrVhi7IlNXmYxTDvZ00eqav4hESNnT5TsXHxKhscpKEfvicPd3dJUvSFizLk4/NH4WvSrLHi4+I17ufxio2JVdmwsvpm+NfyuDITKzr6vGyu+e0zb87vysjI0Afvmv/O696jm17o1V2SVL9RffV7t6+mTJyq4UO/V0BAgAZ/+YmqVLu3d0Ph7nIJKafKH11NTVim6+uSpOg1i3Xkh88Kq1u4DXUbPqzEhCTNnPSb4uMSFFQmUO99NsCU0urihRjZGK4eu+Ni4vTOK1fTVS34bZEW/LZIFaqU18dfv1/g/ceda960ac752JixV87HQvX9t0PNz8eu+TeQlpqmr74Zan4+9uEgNb+HucqRP480rKPE+ERNnzhL8XHxCg4J0gefDzSltLp4PkYGm6vf33ExsXq79wDT899/XaDff12gilUqaPDQDyVJoeFlNeCjtzVl3HT9OmW2SnuV0gu9u6pBk3rC/WHlrkVysHNUx3ovyNmhiI5HH9bopV8rM+vqLKwSrqVV1OnqOoouzq7q0vAluRVxV+rlVJ2JPanRS77WodNXz7nrlm+sx2pezcTxRpsPJElT1/yszUf+KoCRAQWjb9++6tq1q2rVqqXatWtr2LBhunTpkrp3zzmPef755+Xr62taA+Srr77SoEGDNG3aNAUFBZnW+vhnved7wSb7+sUdgFsQGRmp4OBg7dixQ9WqVSvs7pgkXjxf2F1AATo7b1JhdwEFyLUKF/P/S+xc3Au7CyhA2T6lb14I/xqHe956GhhYP9chwwu7CyhAQS5Bhd0FFKCoS6cKuwsoQD//MfTmhfCvMbzn5MLugtU5du5QYXch30K8wm+r/MiRI/X111/r3Llzqlatmr7//ntThqKGDRsqKCjIlIEoKChIJ07kXiPnww8/1EcffXSnXbeIGR4AAAAAAAAAAOCm+vTpY1rD+Xpr1qwxex4ZGXnvO3Qd8goBAAAAAAAAAACrxwwP5EtQUJDIhgYAAAAAAADgv86YbSzsLuAKZngAAAAAAAAAAACrR8ADAAAAAAAAAABYPQIeAAAAAAAAAADA6rGGBwAAAAAAAAAA+cRax/cPZngAAAAAAAAAAACrR8ADAAAAAAAAAABYPVJaAQAAAAAAAACQT6S0un8wwwMAAAAAAAAAAFg9Ah4AAAAAAAAAAMDqEfAAAAAAAAAAAABWjzU8AAAAAAAAAADIp+xsY2F3AVcwwwMAAAAAAAAAAFg9Ah4AAAAAAAAAAMDqEfAAAAAAAAAAAABWjzU8AAAAAAAAAADIp+zs7MLuAq5ghgcAAAAAAAAAALB6BDwAAAAAAAAAAIDVI6UVAAAAAAAAAAD5ZMw2FnYXcAUzPAAAAAAAAAAAgNUj4AEAAAAAAAAAAKweAQ8AAAAAAAAAAGD1WMMDAAAAAAAAAIB8ylZ2YXcBVzDDAwAAAAAAAAAAWD0CHgA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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wNsCMkYJs5ZJ"
      },
      "source": [
        "# 4 | Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 438,
      "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": 439,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NBYvs4vzD82a",
        "outputId": "063e06d6-2224-40ea-d34f-e42bad603abe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4   ctar  individualperf  \\\n",
              "0       0.673684    0.598802    0.773333    0.666667  0.600        0.436709   \n",
              "1       0.663158    0.820359    0.720000    0.606667  0.664        0.411392   \n",
              "2       0.757895    0.760479    0.720000    0.606667  0.664        0.462025   \n",
              "3       0.800000    0.299401    0.440000    0.166667  0.536        0.221519   \n",
              "4       0.684211    0.502994    0.553333    0.440000  0.664        0.348101   \n",
              "...          ...         ...         ...         ...    ...             ...   \n",
              "2968    0.515789    0.323353    0.440000    0.386667  0.736        0.284810   \n",
              "2975    0.684211    0.502994    0.773333    0.333333  0.600        0.316456   \n",
              "2988    0.757895    0.562874    0.553333    0.606667  0.464        0.316456   \n",
              "2991    0.378947    0.700599    0.666667    0.553333  0.664        0.411392   \n",
              "2992    0.400000    0.640719    0.666667    0.553333  0.664        0.348101   \n",
              "\n",
              "           age  tenure  gender_M  decisionstd2_Not Hired  \n",
              "0     0.394737    0.15       0.0                     0.0  \n",
              "1     0.605263    0.80       1.0                     0.0  \n",
              "2     0.368421    0.00       1.0                     0.0  \n",
              "3     0.105263    0.00       1.0                     1.0  \n",
              "4     0.000000    0.00       0.0                     1.0  \n",
              "...        ...     ...       ...                     ...  \n",
              "2968  0.315789    0.00       0.0                     1.0  \n",
              "2975  0.078947    0.00       1.0                     1.0  \n",
              "2988  0.552632    0.00       0.0                     1.0  \n",
              "2991  0.289474    0.30       0.0                     1.0  \n",
              "2992  0.368421    0.60       1.0                     1.0  \n",
              "\n",
              "[1346 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbfe618a-bd31-4993-9be1-d2442cf88704\" 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",
              "      <th>decisionstd2_Not Hired</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.673684</td>\n",
              "      <td>0.598802</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.436709</td>\n",
              "      <td>0.394737</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.663158</td>\n",
              "      <td>0.820359</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.411392</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.757895</td>\n",
              "      <td>0.760479</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.462025</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.299401</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.221519</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.502994</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.348101</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2968</th>\n",
              "      <td>0.515789</td>\n",
              "      <td>0.323353</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.386667</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.284810</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2975</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.502994</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.316456</td>\n",
              "      <td>0.078947</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>0.757895</td>\n",
              "      <td>0.562874</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.316456</td>\n",
              "      <td>0.552632</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>0.378947</td>\n",
              "      <td>0.700599</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.411392</td>\n",
              "      <td>0.289474</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.640719</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.348101</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1346 rows × 10 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-cbfe618a-bd31-4993-9be1-d2442cf88704');\n",
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              "          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",
              "        element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 439
        }
      ],
      "source": [
        "df_dummy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 440,
      "metadata": {
        "id": "PH32Aei66E2g"
      },
      "outputs": [],
      "source": [
        "# Remove rows with null and NaN values\n",
        "df_dummy = df_dummy.dropna()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 441,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZdxiQsD6RVL",
        "outputId": "a8a810a0-782f-4789-9d8a-21e65b2eace2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1346, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 441
        }
      ],
      "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": 442,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gKi-9lAVl0Q",
        "outputId": "19ccae6b-3188-4e73-bd1a-15efd6b27872"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "      predictor1  predictor2  predictor3  predictor4   ctar  individualperf  \\\n",
            "0       0.673684    0.598802    0.773333    0.666667  0.600        0.436709   \n",
            "1       0.663158    0.820359    0.720000    0.606667  0.664        0.411392   \n",
            "2       0.757895    0.760479    0.720000    0.606667  0.664        0.462025   \n",
            "3       0.800000    0.299401    0.440000    0.166667  0.536        0.221519   \n",
            "4       0.684211    0.502994    0.553333    0.440000  0.664        0.348101   \n",
            "...          ...         ...         ...         ...    ...             ...   \n",
            "2968    0.515789    0.323353    0.440000    0.386667  0.736        0.284810   \n",
            "2975    0.684211    0.502994    0.773333    0.333333  0.600        0.316456   \n",
            "2988    0.757895    0.562874    0.553333    0.606667  0.464        0.316456   \n",
            "2991    0.378947    0.700599    0.666667    0.553333  0.664        0.411392   \n",
            "2992    0.400000    0.640719    0.666667    0.553333  0.664        0.348101   \n",
            "\n",
            "           age  tenure  gender_M  decisionstd2_Not Hired  \n",
            "0     0.394737    0.15       0.0                     0.0  \n",
            "1     0.605263    0.80       1.0                     0.0  \n",
            "2     0.368421    0.00       1.0                     0.0  \n",
            "3     0.105263    0.00       1.0                     1.0  \n",
            "4     0.000000    0.00       0.0                     1.0  \n",
            "...        ...     ...       ...                     ...  \n",
            "2968  0.315789    0.00       0.0                     1.0  \n",
            "2975  0.078947    0.00       1.0                     1.0  \n",
            "2988  0.552632    0.00       0.0                     1.0  \n",
            "2991  0.289474    0.30       0.0                     1.0  \n",
            "2992  0.368421    0.60       1.0                     1.0  \n",
            "\n",
            "[1346 rows x 10 columns]\n",
            "\n",
            "Cleaned DataFrame (outliers removed):\n",
            "      predictor1  predictor2  predictor3  predictor4   ctar  individualperf  \\\n",
            "0       0.673684    0.598802    0.773333    0.666667  0.600        0.436709   \n",
            "2       0.757895    0.760479    0.720000    0.606667  0.664        0.462025   \n",
            "3       0.800000    0.299401    0.440000    0.166667  0.536        0.221519   \n",
            "4       0.684211    0.502994    0.553333    0.440000  0.664        0.348101   \n",
            "5       0.810526    0.359281    0.500000    0.440000  0.464        0.284810   \n",
            "...          ...         ...         ...         ...    ...             ...   \n",
            "2968    0.515789    0.323353    0.440000    0.386667  0.736        0.284810   \n",
            "2975    0.684211    0.502994    0.773333    0.333333  0.600        0.316456   \n",
            "2988    0.757895    0.562874    0.553333    0.606667  0.464        0.316456   \n",
            "2991    0.378947    0.700599    0.666667    0.553333  0.664        0.411392   \n",
            "2992    0.400000    0.640719    0.666667    0.553333  0.664        0.348101   \n",
            "\n",
            "           age  tenure  gender_M  decisionstd2_Not Hired  \n",
            "0     0.394737    0.15       0.0                     0.0  \n",
            "2     0.368421    0.00       1.0                     0.0  \n",
            "3     0.105263    0.00       1.0                     1.0  \n",
            "4     0.000000    0.00       0.0                     1.0  \n",
            "5     0.315789    0.00       0.0                     1.0  \n",
            "...        ...     ...       ...                     ...  \n",
            "2968  0.315789    0.00       0.0                     1.0  \n",
            "2975  0.078947    0.00       1.0                     1.0  \n",
            "2988  0.552632    0.00       0.0                     1.0  \n",
            "2991  0.289474    0.30       0.0                     1.0  \n",
            "2992  0.368421    0.60       1.0                     1.0  \n",
            "\n",
            "[1246 rows x 10 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",
      "source": [
        "df_cleaned"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BBvGSprgZCNy",
        "outputId": "cd1b7a82-6eb2-4db6-ae59-407d79d187e7"
      },
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predictor1  predictor2  predictor3  predictor4   ctar  individualperf  \\\n",
              "0       0.673684    0.598802    0.773333    0.666667  0.600        0.436709   \n",
              "2       0.757895    0.760479    0.720000    0.606667  0.664        0.462025   \n",
              "3       0.800000    0.299401    0.440000    0.166667  0.536        0.221519   \n",
              "4       0.684211    0.502994    0.553333    0.440000  0.664        0.348101   \n",
              "5       0.810526    0.359281    0.500000    0.440000  0.464        0.284810   \n",
              "...          ...         ...         ...         ...    ...             ...   \n",
              "2968    0.515789    0.323353    0.440000    0.386667  0.736        0.284810   \n",
              "2975    0.684211    0.502994    0.773333    0.333333  0.600        0.316456   \n",
              "2988    0.757895    0.562874    0.553333    0.606667  0.464        0.316456   \n",
              "2991    0.378947    0.700599    0.666667    0.553333  0.664        0.411392   \n",
              "2992    0.400000    0.640719    0.666667    0.553333  0.664        0.348101   \n",
              "\n",
              "           age  tenure  gender_M  decisionstd2_Not Hired  \n",
              "0     0.394737    0.15       0.0                     0.0  \n",
              "2     0.368421    0.00       1.0                     0.0  \n",
              "3     0.105263    0.00       1.0                     1.0  \n",
              "4     0.000000    0.00       0.0                     1.0  \n",
              "5     0.315789    0.00       0.0                     1.0  \n",
              "...        ...     ...       ...                     ...  \n",
              "2968  0.315789    0.00       0.0                     1.0  \n",
              "2975  0.078947    0.00       1.0                     1.0  \n",
              "2988  0.552632    0.00       0.0                     1.0  \n",
              "2991  0.289474    0.30       0.0                     1.0  \n",
              "2992  0.368421    0.60       1.0                     1.0  \n",
              "\n",
              "[1246 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f31a0cb8-316d-4499-becf-c4c639ee6548\" class=\"colab-df-container\">\n",
              "    <div>\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>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>decisionstd2_Not Hired</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.673684</td>\n",
              "      <td>0.598802</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.757895</td>\n",
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              "      <td>0.720000</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.462025</td>\n",
              "      <td>0.368421</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.299401</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.221519</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.502994</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.348101</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.810526</td>\n",
              "      <td>0.359281</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.284810</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2968</th>\n",
              "      <td>0.515789</td>\n",
              "      <td>0.323353</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.386667</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.284810</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2975</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.502994</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.316456</td>\n",
              "      <td>0.078947</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>0.757895</td>\n",
              "      <td>0.562874</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.316456</td>\n",
              "      <td>0.552632</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>0.378947</td>\n",
              "      <td>0.700599</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.411392</td>\n",
              "      <td>0.289474</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.640719</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.348101</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1246 rows × 10 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-f31a0cb8-316d-4499-becf-c4c639ee6548')\"\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-f31a0cb8-316d-4499-becf-c4c639ee6548 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-f31a0cb8-316d-4499-becf-c4c639ee6548');\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-3b488efd-e925-4434-90e8-861422a9e326\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b488efd-e925-4434-90e8-861422a9e326')\"\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",
              "    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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3b488efd-e925-4434-90e8-861422a9e326 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": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df_cleaned = df_cleaned.sample(frac=0.7, random_state=42)"
      ],
      "metadata": {
        "id": "GnnsrE_vsu4H"
      },
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 444,
      "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",
      "execution_count": 445,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bw3JSPD5BQEU",
        "outputId": "ddc8dbdd-e4eb-4270-c3f6-be7684366ca2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(872, 9) (872,)\n",
            "(374, 9) (374,)\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": 446,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMkhG5VCR3nG",
        "outputId": "a6165f18-0a20-4a35-9792-da9383f50d13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept:  -0.03860637349517393\n",
            "coef:  [0.13046593 0.29899394 0.0874667  0.07678713 0.02456529 0.03020602\n",
            " 0.10403082 0.01462831 0.01351924]\n",
            "RScore.  0.5878527081500928\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": 447,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "9xr8I-yOR_Hy",
        "outputId": "67fce7a0-31d0-4141-e299-8b5abbd93049"
      },
      "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": 448,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPgT_zFkSGRe",
        "outputId": "594646c4-1706-40c7-f6e9-c8132a918f01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.04935831235473789\n",
            "MSE: 0.00476387878414796\n",
            "RMSE: 0.0690208576022347\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": 449,
      "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": 450,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ft0BzzKOBmhO",
        "outputId": "9a67e958-bb92-4956-e7ee-4f3dae109687"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Linear Regression  0.587853           0.577662                  0.482437   \n",
              "\n",
              "       RMSE  \n",
              "0  0.069021  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c8a08b7-0d44-45b9-a7c3-fe61afac20b3\" 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",
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              "</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>0.587853</td>\n",
              "      <td>0.577662</td>\n",
              "      <td>0.482437</td>\n",
              "      <td>0.069021</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-7c8a08b7-0d44-45b9-a7c3-fe61afac20b3')\"\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-7c8a08b7-0d44-45b9-a7c3-fe61afac20b3 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-7c8a08b7-0d44-45b9-a7c3-fe61afac20b3');\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": 450
        }
      ],
      "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": 451,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ysFFiVXEBoit",
        "outputId": "f96cf010-41d6-4637-b7e5-c85ecb61dede"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear regression for RGB dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 451
        },
        {
          "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": 393,
      "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": 394,
      "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": 395,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "A2Q6wyo4Cb4t",
        "outputId": "73b83d38-c97c-46b2-e864-5a5cf7e15397"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Random Forest  0.823474            0.81911                  0.764551   \n",
              "\n",
              "       RMSE  \n",
              "0  0.045171  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2dcffced-8633-4030-bb60-4c545a97ea28\" 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>0.823474</td>\n",
              "      <td>0.81911</td>\n",
              "      <td>0.764551</td>\n",
              "      <td>0.045171</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-2dcffced-8633-4030-bb60-4c545a97ea28')\"\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-2dcffced-8633-4030-bb60-4c545a97ea28 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-2dcffced-8633-4030-bb60-4c545a97ea28');\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": 395
        }
      ],
      "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": 396,
      "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": 397,
      "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": 398,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "l5a0MRIaCmE3",
        "outputId": "ff847f1d-6153-4e06-cc64-be6ef103261e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Ridge Regression  0.568836           0.558176                  0.482446   \n",
              "\n",
              "       RMSE  \n",
              "0  0.070595  "
            ],
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              "\n",
              "  <div id=\"df-5153fc8a-ba34-4ee2-ad62-540ebc425661\" class=\"colab-df-container\">\n",
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              "      <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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.568836</td>\n",
              "      <td>0.558176</td>\n",
              "      <td>0.482446</td>\n",
              "      <td>0.070595</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      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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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-5153fc8a-ba34-4ee2-ad62-540ebc425661 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-5153fc8a-ba34-4ee2-ad62-540ebc425661');\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": 398
        }
      ],
      "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": 399,
      "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": 400,
      "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": 401,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "SIfmY1dfCug3",
        "outputId": "d2bc5dbd-517e-48d9-c3d1-757042c62d69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score      RMSE\n",
              "0  XGBoost  0.847903           0.844142                  0.828151  0.041929"
            ],
            "text/html": [
              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.847903</td>\n",
              "      <td>0.844142</td>\n",
              "      <td>0.828151</td>\n",
              "      <td>0.041929</td>\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-1fabf6b1-7e80-407a-ad22-d5095290f661 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-1fabf6b1-7e80-407a-ad22-d5095290f661');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 401
        }
      ],
      "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": 402,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "kSUq_6aAC-xE",
        "outputId": "8c0a5a34-6f1c-4212-9de8-6264c762fa18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0   Ridge Regression  0.568836           0.558176                  0.482446   \n",
              "1            XGBoost  0.847903           0.844142                  0.828151   \n",
              "2  Linear Regression  0.587853           0.577662                  0.482437   \n",
              "3      Random Forest  0.823474           0.819110                  0.764551   \n",
              "\n",
              "       RMSE  \n",
              "0  0.070595  \n",
              "1  0.041929  \n",
              "2  0.069021  \n",
              "3  0.045171  "
            ],
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              "\n",
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              "      <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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.568836</td>\n",
              "      <td>0.558176</td>\n",
              "      <td>0.482446</td>\n",
              "      <td>0.070595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.847903</td>\n",
              "      <td>0.844142</td>\n",
              "      <td>0.828151</td>\n",
              "      <td>0.041929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.587853</td>\n",
              "      <td>0.577662</td>\n",
              "      <td>0.482437</td>\n",
              "      <td>0.069021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.823474</td>\n",
              "      <td>0.819110</td>\n",
              "      <td>0.764551</td>\n",
              "      <td>0.045171</td>\n",
              "    </tr>\n",
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              "            style=\"display:none;\">\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\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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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ae05b5ec-4923-4731-8384-46cec5852b4f 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-ae05b5ec-4923-4731-8384-46cec5852b4f');\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": 402
        }
      ],
      "source": [
        "predictions = pd.concat([rr_score, XGBR_score, lm_score,rf_score], ignore_index=True, sort=False)\n",
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 403,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "Lulb2to9CwIX",
        "outputId": "27a43752-bc1f-47cb-cd61-2c61d89cb5a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ],
            "image/png": 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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": 404,
      "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": 405,
      "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": 406,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_1bD6XPy3H9",
        "outputId": "da393da0-e94f-40ef-9193-855a5094e172"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['predictor1', 'predictor2', 'predictor3', 'predictor4', 'ctar',\n",
              "       'individualperf', 'age', 'tenure', 'gender_M',\n",
              "       'decisionstd2_Not Hired'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 406
        }
      ],
      "source": [
        "df_cleaned.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 407,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BrwwUr9a1uup",
        "outputId": "aa7d95e6-9f5f-4029-b0fa-011a33a85280"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   predictor1  predictor2  predictor3  predictor4   ctar  individualperf  \\\n",
              "0    0.673684    0.598802    0.773333    0.666667  0.600        0.436709   \n",
              "2    0.757895    0.760479    0.720000    0.606667  0.664        0.462025   \n",
              "3    0.800000    0.299401    0.440000    0.166667  0.536        0.221519   \n",
              "4    0.684211    0.502994    0.553333    0.440000  0.664        0.348101   \n",
              "5    0.810526    0.359281    0.500000    0.440000  0.464        0.284810   \n",
              "\n",
              "        age  tenure  gender_M  decisionstd2_Not Hired  \n",
              "0  0.394737    0.15       0.0                     0.0  \n",
              "2  0.368421    0.00       1.0                     0.0  \n",
              "3  0.105263    0.00       1.0                     1.0  \n",
              "4  0.000000    0.00       0.0                     1.0  \n",
              "5  0.315789    0.00       0.0                     1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-292f3a81-723f-42d5-9cee-9ec33398e8a1\" 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",
              "      <th>decisionstd2_Not Hired</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.673684</td>\n",
              "      <td>0.598802</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.436709</td>\n",
              "      <td>0.394737</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.757895</td>\n",
              "      <td>0.760479</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>0.606667</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.462025</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.299401</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.221519</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.502994</td>\n",
              "      <td>0.553333</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.348101</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.810526</td>\n",
              "      <td>0.359281</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.284810</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-292f3a81-723f-42d5-9cee-9ec33398e8a1');\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",
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              "\n",
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              "\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart: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",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e52c31e-1044-4386-bf96-bbefd939f521 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": 407
        }
      ],
      "source": [
        "df_cleaned.head()"
      ]
    },
    {
      "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": 409,
      "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": 410,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "XhV3XZm-PCDi",
        "outputId": "78301c61-bd08-4a5d-d8ba-fb60a6ba244c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Linear Regression  0.587853           0.577662                  0.482437   \n",
              "\n",
              "       RMSE  \n",
              "0  0.069021  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10e27609-064c-46f0-9c92-1800aebf923d\" class=\"colab-df-container\">\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>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>0.587853</td>\n",
              "      <td>0.577662</td>\n",
              "      <td>0.482437</td>\n",
              "      <td>0.069021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      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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              "\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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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-10e27609-064c-46f0-9c92-1800aebf923d 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-10e27609-064c-46f0-9c92-1800aebf923d');\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": 410
        }
      ],
      "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": 411,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "7Ka-wRbaPGD9",
        "outputId": "9bacfe4b-778f-4d61-cfca-9538f538b17f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear regression for RGB dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 411
        },
        {
          "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l13Z8seQPdv2"
      },
      "source": [
        "## Random Forrest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 412,
      "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": 413,
      "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": 414,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WdUJuyZLPfqT",
        "outputId": "74cefb3c-fb28-4bb5-c722-c9069b5795fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Random Forest  0.823474            0.81911                  0.764551   \n",
              "\n",
              "       RMSE  \n",
              "0  0.045171  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f449238c-4203-4fca-8eca-1b8aa928b21b\" 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>0.823474</td>\n",
              "      <td>0.81911</td>\n",
              "      <td>0.764551</td>\n",
              "      <td>0.045171</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-f449238c-4203-4fca-8eca-1b8aa928b21b')\"\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-f449238c-4203-4fca-8eca-1b8aa928b21b 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-f449238c-4203-4fca-8eca-1b8aa928b21b');\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": 414
        }
      ],
      "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": 415,
      "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": 416,
      "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": 417,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "hJzNBy2YPwTM",
        "outputId": "7d877885-77cf-4f37-bff5-2b57c83f9420"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score  \\\n",
              "0  Ridge Regression  0.568836           0.558176                  0.482446   \n",
              "\n",
              "       RMSE  \n",
              "0  0.070595  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6b908ef-2542-4816-9581-9e64b2542316\" 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>Ridge Regression</td>\n",
              "      <td>0.568836</td>\n",
              "      <td>0.558176</td>\n",
              "      <td>0.482446</td>\n",
              "      <td>0.070595</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-b6b908ef-2542-4816-9581-9e64b2542316')\"\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-b6b908ef-2542-4816-9581-9e64b2542316 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-b6b908ef-2542-4816-9581-9e64b2542316');\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": 417
        }
      ],
      "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": 418,
      "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": 419,
      "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": 420,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WrPeKkScP7t4",
        "outputId": "78fa8351-d769-4c93-87a5-f38974b85ca0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Model  R2 Score  Adjusted R2 Score  Cross Validated R2 Score      RMSE\n",
              "0  XGBoost  0.847903           0.844142                  0.828151  0.041929"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-304d1cd9-f77c-4491-959b-29a02cac4ae3\" 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>XGBoost</td>\n",
              "      <td>0.847903</td>\n",
              "      <td>0.844142</td>\n",
              "      <td>0.828151</td>\n",
              "      <td>0.041929</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-304d1cd9-f77c-4491-959b-29a02cac4ae3')\"\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-304d1cd9-f77c-4491-959b-29a02cac4ae3 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-304d1cd9-f77c-4491-959b-29a02cac4ae3');\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": 420
        }
      ],
      "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": "markdown",
      "metadata": {
        "id": "pxjZIA2rQDv2"
      },
      "source": [
        "## Recursive Feature Elimination (RFE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 421,
      "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": 422,
      "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": 423,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vnNPJHegQIHc",
        "outputId": "920cd7b4-2396-4ada-e309-fa5d9bdce837"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  R2 Score  Adjusted R2 Score  \\\n",
              "0  Random Forest with RFE  0.833024           0.828895   \n",
              "\n",
              "   Cross Validated R2 Score      RMSE  \n",
              "0                  0.799644  0.043932  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e06a0ef3-e63f-43be-9152-e65a171f9eeb\" 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 with RFE</td>\n",
              "      <td>0.833024</td>\n",
              "      <td>0.828895</td>\n",
              "      <td>0.799644</td>\n",
              "      <td>0.043932</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-e06a0ef3-e63f-43be-9152-e65a171f9eeb')\"\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-e06a0ef3-e63f-43be-9152-e65a171f9eeb 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-e06a0ef3-e63f-43be-9152-e65a171f9eeb');\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": 423
        }
      ],
      "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\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",
        "# 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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stp3Us99u7cn",
        "outputId": "85103e27-7aa4-4b55-d349-4ac7620920cd"
      },
      "execution_count": 452,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Model       MSE      RMSE  R-squared\n",
            "0    Ensemble  0.001424  0.037733   0.882221\n",
            "1       Ridge  0.003414  0.058431   0.717576\n",
            "2  Complex RF  0.001305  0.036127   0.892037\n",
            "3     XGBoost  0.001370  0.037014   0.886666\n",
            "4     Stacked  0.001756  0.041910   0.854703\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "McsrODoZ75-C",
        "outputId": "8a849c1e-8db5-40df-ecb5-f26d35a53fdf"
      },
      "execution_count": 453,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Model       MSE      RMSE  R-squared\n",
              "0    Ensemble  0.001424  0.037733   0.882221\n",
              "1       Ridge  0.003414  0.058431   0.717576\n",
              "2  Complex RF  0.001305  0.036127   0.892037\n",
              "3     XGBoost  0.001370  0.037014   0.886666\n",
              "4     Stacked  0.001756  0.041910   0.854703"
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R-squared</th>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "rrtXeQ9076g-",
        "outputId": "cedaaa58-77a9-4a0a-b144-df426526dfc6"
      },
      "execution_count": 456,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
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            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JiFC6HaW9f6p"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1pHrKXM90oE3UXgdrt3FTcQNPTZC-XIRB",
      "authorship_tag": "ABX9TyNX6qgipgDnd7Gl+j/9NT0Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}