Assingment_6_Group_Project_Master_Carsten_Scholle.ipynb

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        "Group Members:\n",
        "* Bradon Barfuss\n",
        "* Balal Glab\n",
        "* Carsten Scholle\n",
        "\n",
        "Dataset: Medical Cost Personal Datasets\n",
        "https://www.kaggle.com/datasets/mirichoi0218/insurance\n",
        "\n",
        "**Research Questions:**\n",
        "* What is the correlation between smoking and medical costs? - Belal\n",
        "* How do medical costs compare between males and females? - Carsten\n",
        "* Based on age and gender, what combination of BMI, number of children, and smoking status results in the lowest medical costs?  - Bradon"
      ],
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        "id": "3l3iyoOQXoik"
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      "source": [
        "# Import needed libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "Bv9T_lqP0npH"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "# Load the dataset from my (Carsten) goolge drive\n",
        "file_path = 'https://drive.google.com/uc?export=download&id=1F6uuraiYTH-ck32kO2IrOv3iOC8DSe0I'\n",
        "df_insurance = pd.read_csv(file_path)"
      ],
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        "id": "6_uOcZWt1kGf"
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      "source": [
        "## Data Exploration\n",
        "\n",
        "First lets look at the data and its features to better understand it."
      ],
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        "id": "TnMFBLje3K2w"
      }
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      "source": [
        "df_insurance.head()"
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              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
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              "summary": "{\n  \"name\": \"df_insurance\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679017,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236693994,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "# What columns and datatypes are in the dataset\n",
        "df_insurance.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
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      "source": [
        "# Get some statistics about the columns\n",
        "df_insurance.describe()"
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            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
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              "      <td>1338.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
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              "      <td>13270.422265</td>\n",
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              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_insurance\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 460.6106090399993,\n        \"min\": 14.049960379216172,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.20702541106129,\n          39.0,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 463.29524977918294,\n        \"min\": 6.098186911679017,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.66339686098655,\n          30.4,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.5368318870757,\n        \"min\": 0.0,\n        \"max\": 1338.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1338.0,\n          1.0949177877429,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20381.922846226596,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13270.422265141257,\n          9382.033,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning and Transformation\n",
        "Check if there are missing values or incorrect labels."
      ],
      "metadata": {
        "id": "EAmD8xb53y1h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for na values\n",
        "df_insurance.isna().sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "SY9jECbx3-EE",
        "outputId": "63324771-6a23-451e-902a-93879d866c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "bmi         0\n",
              "children    0\n",
              "smoker      0\n",
              "region      0\n",
              "charges     0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoker</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charges</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# So there are no na-values that needs to be fixed.\n",
        "# Next lets look at the different values from the categorical columns\n",
        "cat_columns = ['sex', 'smoker', 'region']\n",
        "for col in cat_columns:\n",
        "  print(f'Unique values for column \"{col}\": {df_insurance[col].value_counts()}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftVZyk-T4D5Y",
        "outputId": "3f92bcb2-6482-4c8c-f834-cdd5e418bef5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values for column \"sex\": sex\n",
            "male      676\n",
            "female    662\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Unique values for column \"smoker\": smoker\n",
            "no     1064\n",
            "yes     274\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Unique values for column \"region\": region\n",
            "southeast    364\n",
            "southwest    325\n",
            "northwest    325\n",
            "northeast    324\n",
            "Name: count, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Also take a look at the distribution of the data\n",
        "df_insurance.hist(bins=50, figsize=(12, 8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "4AsllDDu4plY",
        "outputId": "d662e3df-ceec-48af-81ea-d88f326785bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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gQ2jRqk43u+GGG/T666+rY8eOOnToUI2nm73wwgtKSUnRnDlzlJ6ergMHDig6OjqA0SOc1HVLLG6HBaAxTC+03W63hgwZoieeeEKSNGjQIO3bt08rVqxQZmZmo9aZk5Oj7Oxsz/OysjIlJycrLS1Ndrvdr3j7zt/c6GU/+L/RKigo0NixY2W1Wv2KoyVyuVz0nx/oP//Qf/4Jl/6rOkIKgcHpZgCAcGV6od25c2f16dPHa1rv3r315z//WZKUmJgoSSopKVHnzp09bUpKSjRw4MAa12mz2WSz2apNt1qtfg/wnJWWRi9b9dpmxNGS0X/+of/8Q//5J9T7L5RjDwdNcbqZ1LhTzsLhdIhwyEFqmjzMOFXQl/XaIgyvf31db13rbmxMDX3di9vxeQq8cMhBCo88fInd9EJ7xIgROnjwoNe0f/7zn+rWrZukr7ZUJyYmauvWrZ7CuqysTLt27dL06dPNDgcAAASppjjdTPLvlLNQPx1CCo8cJHPz8OdUwY0bNzZ6vY8OcTdqvfWt25+Y6nvdi/F5Ch7hkIMU2nn4csqZ6YX27Nmzdc011+iJJ57Q7bffrvfee0/PPfecnnvuOUmSxWLRrFmz9Nhjj6lHjx6e862SkpJ06623mh0OAAAIUk1xupnUuFPOwuF0iHDIQWqaPPw5VXDf/HSf12uLMPToELfm7ImQ013z0ZN1rbeudTc2poa+bhU+T8EjHHKQwiMPX045M73Qvvrqq7V27Vrl5ORo4cKFSklJ0ZIlSzR58mRPmwceeEDnz5/XtGnTVFpaqpEjR2rTpk1c1AQAgBakKU43k/w75SzUT4eQwiMHydw8zDhVsDHrdbottbapL7e61u1PTL72KZ+n4BEOOUihnYcvcZteaEvSzTffrJtvvrnW+RaLRQsXLtTChQub4uUBAEAI4HQzAEC4apJCGwAAoD6cbgYACFcU2gAAICA43QwAEK4otAEAQMBwuhkAIBxFBDoAAAAAAADCCYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIi6EBAAAAzaz7QxsCHQKAJsQebQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNxH20AAACgFtzvGkBjsEcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJuL2XgAAAEALUdftyj5ZNL4ZIwHCG3u0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEpp+jPX/+fC1YsMBrWs+ePfWPf/xDklRRUaH77rtPL730kpxOp9LT07Vs2TIlJCSYHQoAAADCCOcXAwgVTbJH+8orr9SJEyc8j3feecczb/bs2Xrttde0Zs0abdu2TcePH9fEiRObIgwAAAAAAJpdkxTarVq1UmJioudxySWXSJLOnDmjlStX6qmnntLo0aM1ePBg5efna8eOHdq5c2dThAIAAILY/PnzZbFYvB69evXyzK+oqFBWVpY6dOigtm3bKiMjQyUlJQGMGACA+jXJ7b0OHTqkpKQkRUdHy+FwKDc3V127dlVRUZFcLpdSU1M9bXv16qWuXbuqsLBQw4cPr3F9TqdTTqfT87ysrEyS5HK55HK5/IrVFmk0etmq1/Y3hpaK/vMP/ecf+s8/4dJ/oR5/uLjyyiv1xhtveJ63avX18GT27NnasGGD1qxZo9jYWM2YMUMTJ07Uu+++G4hQEcTqOqwcAJqb6YX2sGHDtGrVKvXs2VMnTpzQggULdO2112rfvn0qLi5WVFSU4uLivJZJSEhQcXFxrevMzc2tdt63JG3ZskUxMTF+xbt4aOOXLSgo8PoXjUP/+Yf+8w/9559Q77/y8vJAhwB9fSTcxaqOhFu9erVGjx4tScrPz1fv3r21c+fOWjfQAwAQaKYX2uPGjfP8v3///ho2bJi6deumP/3pT2rdunWj1pmTk6Ps7GzP87KyMiUnJystLU12u92vePvO39zoZT/4v9EqKCjQ2LFjZbVa/YqjJXK5XPSfH+g//9B//gmX/qs6QgqBZfaRcAAABFqTHDr+TXFxcbriiit0+PBhjR07VhcuXFBpaanXXu2SkpIat2RXsdlsstls1aZbrVa/B3jOSkujl616bTPiaMnoP//Qf/6h//wT6v0XyrGHi6Y4Eq4xp5yFw+kQ4ZCDVHce/pzy15xsEYbXv2ar6z0247TIb/4/nD9PoSIccpDCIw9fYm/yQvvcuXP617/+pTvvvFODBw+W1WrV1q1blZGRIUk6ePCgjh49KofD0dShAACAINMUR8L5c8pZqJ8OIYVHDlLNefhzyl8gPDrE3STr3bhxY63z/OmjmtYbzp+nUBMOOUihnYcvp5yZXmj/9Kc/1YQJE9StWzcdP35c8+bNU2RkpL73ve8pNjZWU6dOVXZ2tuLj42W32zVz5kw5HA4O/wIAAKYcCdeYU87C4XSIcMhBqjsPf075a062CEOPDnFrzp4IOd2NP3qyue2bn+75f0v4PIWKcMhBCo88fDnlzPRC+9///re+973v6dSpU+rYsaNGjhypnTt3qmPHjpKkp59+WhEREcrIyJDT6VR6erqWLVtmdhgAACAEmXEknD+nnIX66RBSeOQg1ZyHP6f8BYLTbQmpmGv63ITz5ynUhEMOUmjn4UvcphfaL730Up3zo6OjlZeXp7y8PLNfGgAAhBiOhAMAhKMmP0cbAACgNhwJBwAIRxTaAAAgYDgSDgAQjiICHQAAAAAAAOGEPdoAAAAA6tT9oQ11zv9k0fhmigQIDezRBgAAAADARBTaAAAAAACYiEIbAAAAAAATcY42AAAAgkL3hzbIFmlo8VCp7/zNclZaAh0SADQKe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIm7vBQAAAKDJdH9oQ63zPlk0vhkjAZoPe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIm7vBQAAgGZT162eACBcsEcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgImavNBetGiRLBaLZs2a5ZlWUVGhrKwsdejQQW3btlVGRoZKSkqaOhQAAAAAAJpckxbau3fv1rPPPqv+/ft7TZ89e7Zee+01rVmzRtu2bdPx48c1ceLEpgwFAAAAAIBm0WSF9rlz5zR58mQ9//zzat++vWf6mTNntHLlSj311FMaPXq0Bg8erPz8fO3YsUM7d+5sqnAAAECQ4yg4AEC4aLL7aGdlZWn8+PFKTU3VY4895pleVFQkl8ul1NRUz7RevXqpa9euKiws1PDhw6uty+l0yul0ep6XlZVJklwul1wul19x2iKNRi9b9dr+xtBS0X/+of/8Q//5J1z6L9TjDyd1HQW3YcMGrVmzRrGxsZoxY4YmTpyod999N0CRoiG4VzaAlq5JCu2XXnpJ77//vnbv3l1tXnFxsaKiohQXF+c1PSEhQcXFxTWuLzc3VwsWLKg2fcuWLYqJifEr1sVDG79sQUGB179oHPrPP/Sff+g//4R6/5WXlwc6BMj7KLhvbpyvOgpu9erVGj16tCQpPz9fvXv31s6dO2vcOA8AQDAwvdD+7LPPdO+996qgoEDR0dGmrDMnJ0fZ2dme52VlZUpOTlZaWprsdrtf6+47f3Ojl/3g/0aroKBAY8eOldVq9SuOlsjlctF/fqD//EP/+Sdc+q/qCCkElplHwUmNOxIuHI7SCKYc/Dli0BZheP0bikI1h29+di7+PNX3ntb1uatr2ab+vAbT96KxwiEHKTzy8CV20wvtoqIinTx5UldddZVnWmVlpbZv365f/epX2rx5sy5cuKDS0lKvvdolJSVKTEyscZ02m002m63adKvV6vcAz1lpafSyVa9tRhwtGf3nH/rPP/Sff0K9/0I59nBh9lFwkn9HwoX6URpScOTgzxGDVR4d4vZ/JQEWajls3Lix2rSqz1N972lNy1apa9m6ljNTMHwv/BUOOUihnYcvR8KZXmiPGTNGH374ode0KVOmqFevXnrwwQeVnJwsq9WqrVu3KiMjQ5J08OBBHT16VA6Hw+xwAABAkGqKo+Ckxh0JFw5HaQRTDv4cMWiLMPToELfm7ImQ0934HSKBFKo57Juf7vn/xZ+n+t7Tby57sbqWrWs5MwTT96KxwiEHKTzy8OVIONML7Xbt2qlv375e09q0aaMOHTp4pk+dOlXZ2dmKj4+X3W7XzJkz5XA4ONcKAIAWpCmOgpP8OxIu1I/SkIIjB3+OGPSsw20xZT2BFGo51PS5qfo81ZdHXZ+5upZtrs9qMHwv/BUOOUihnYcvcTfZVcfr8vTTTysiIkIZGRlyOp1KT0/XsmXLAhEKAAAIEI6CAwCEq2YptN9++22v59HR0crLy1NeXl5zvDwAAAhCHAUHhA9u6QZ4C8gebQAAgIbgKDgAQCii0AYAAEGDo+AAAOEgItABAAAAAAAQTii0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATNQq0AEAAAAACLzuD23w/N8WaWjxUKnv/M1yVloCGBUQmtijDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAm4hxtAAAA+OSb5/ICAKpjjzYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmMj0Qnv58uXq37+/7Ha77Ha7HA6HXn/9dc/8iooKZWVlqUOHDmrbtq0yMjJUUlJidhgAAAAAAASE6YV2ly5dtGjRIhUVFWnPnj0aPXq0brnlFu3fv1+SNHv2bL322mtas2aNtm3bpuPHj2vixIlmhwEAAEIAG+gBAOHI9EJ7woQJuummm9SjRw9dccUVevzxx9W2bVvt3LlTZ86c0cqVK/XUU09p9OjRGjx4sPLz87Vjxw7t3LnT7FAAAECQYwM9ACActWrKlVdWVmrNmjU6f/68HA6HioqK5HK5lJqa6mnTq1cvde3aVYWFhRo+fHiN63E6nXI6nZ7nZWVlkiSXyyWXy+VXjLZIo9HLVr22vzG0VPSff+g//9B//gmX/gv1+MPBhAkTvJ4//vjjWr58uXbu3KkuXbpo5cqVWr16tUaPHi1Jys/PV+/evbVz585axw0AAARakxTaH374oRwOhyoqKtS2bVutXbtWffr00d69exUVFaW4uDiv9gkJCSouLq51fbm5uVqwYEG16Vu2bFFMTIxfsS4e2vhlCwoKvP5F49B//qH//EP/+SfU+6+8vDzQIeAbzNpADwBAoDVJod2zZ0/t3btXZ86c0csvv6zMzExt27at0evLyclRdna253lZWZmSk5OVlpYmu93uV6x9529u9LIf/N9oFRQUaOzYsbJarX7F0RK5XC76zw/0n3/oP/+ES/9VHSGFwDJ7A31jjoQLh6M0mjMHf44IrHfdEYbXv6GIHBquqT+vfLeDRzjk4UvsTVJoR0VF6fLLL5ckDR48WLt379Yvf/lLffe739WFCxdUWlrq9aNZUlKixMTEWtdns9lks9mqTbdarX4P8JyVlkYvW/XaZsTRktF//qH//EP/+SfU+y+UYw8nZm+g9+dIuFA/SkNqnhz8OSKwoR4d4m76F2li5FC/jRs3Nun6q/DdDh6hnIcvR8I16TnaVdxut5xOpwYPHiyr1aqtW7cqIyNDknTw4EEdPXpUDoejOUIBAABBxuwN9I05Ei4cjtJozhz8OSKwPrYIQ48OcWvOngg53Y3fIRJI5NBw++anN9m6Jb7bwSQc8vDlSDjTC+2cnByNGzdOXbt21dmzZ7V69Wq9/fbb2rx5s2JjYzV16lRlZ2crPj5edrtdM2fOlMPhCMnzrPrO36zFQ7/6t6Y9458sGh+AqIDA6/7QhmZ/zbq+bzXFY4s06vz++vuaABrP3w30/hwJF+pHaUjNk4M/RwQ2+DXclmZ5naZEDvVrru8b3+3gEcp5+BK36YX2yZMn9YMf/EAnTpxQbGys+vfvr82bN2vs2LGSpKeffloRERHKyMiQ0+lUenq6li1bZnYYAAAgBLSkDfQAgJbD9EJ75cqVdc6Pjo5WXl6e8vLyzH5pAAAQYthADwAIR81yjjYAAEBN2EAPAAhHFNpNKBDnqQYC56kiGATj982fmPheAQAAhC4KbQAAgBaqrg2CbPADgMaLCHQAAAAAAACEE/Zow2/1HR5b3xbxxt5eqSVtaa/r9lS+LgcAAACgabFHGwAAAAAAE7FHGwAAANVwVBSag79HRgLBij3aAAAAAACYiD3aCFlNtaWdLacIBny+AQAAQheFNgAAAICQw+3pEMw4dBwAAAAAABNRaAMAAAAAYCIOHQcuwlVWEc4a+/nmEDwAAICGY482AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNxMTQAAAAAYaXq4p+2SEOLh0p952+Ws9Limc9FPtHU2KMNAAAAAICJ2KMNAKjXxbcFq20PgdnY4wAAAEIRe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEScow0ACFoXnxv+TZy/DQAAghWFNgAAQAir6zZGbJACgMDg0HEAAAAAAExkeqGdm5urq6++Wu3atVOnTp1066236uDBg15tKioqlJWVpQ4dOqht27bKyMhQSUmJ2aEAAAAAANDsTC+0t23bpqysLO3cuVMFBQVyuVxKS0vT+fPnPW1mz56t1157TWvWrNG2bdt0/PhxTZw40exQAABAkGMDPQAgHJleaG/atEl33XWXrrzySg0YMECrVq3S0aNHVVRUJEk6c+aMVq5cqaeeekqjR4/W4MGDlZ+frx07dmjnzp1mhwMAAIIYG+gBAOGoyS+GdubMGUlSfHy8JKmoqEgul0upqameNr169VLXrl1VWFio4cOHV1uH0+mU0+n0PC8rK5MkuVwuuVwuv+KzRRqNXzbC8PoXNavtPaqaTv81TlW/1fUd8OfzHe7M+P7W9/cnnPs/GP7++fv336x1wD+bNm3yer5q1Sp16tRJRUVFuu666zwb6FevXq3Ro0dLkvLz89W7d2/t3LmzxnEDAACB1qSFttvt1qxZszRixAj17dtXklRcXKyoqCjFxcV5tU1ISFBxcXGN68nNzdWCBQuqTd+yZYtiYmL8inHxUL8WlyQ9OsTt/0rC2MaNG+ucT//5p6CgoNZ5Zny+w50/n7/6Ptstof8D+f2tr/8bory83IRIYKZAbaCvmh6KG1+qNurVtAEsFDcIBsOGPH+Rg3n83aFQWx6h9F0P5b9P3xQOefgSu8UwjCb79kyfPl2vv/663nnnHXXp0kWStHr1ak2ZMsXrB1CShg4dqhtuuEFPPvlktfXU9IOZnJys//73v7Lb7X7F2Hf+5kYva4sw9OgQt+bsiZDTbfErjnC2b356jdNdLpcKCgrov0aq+vyNHTtWVqu1xjb+fL7DnRnf39o+21XCuf+D4e9fff3fEGVlZbrkkkt05swZv39P4D+3261vf/vbKi0t1TvvvCOpceOG+fPn17iBfvXq1X5voAcAtFzl5eX6/ve/36BxQ5Pt0Z4xY4bWr1+v7du3e4psSUpMTNSFCxdUWlrqtVe7pKREiYmJNa7LZrPJZrNVm261WmstMBqq6j6Tfq3DbTFlPeGqvveI/vNPXd8D+rV+/nz+6v1st4D+D+T319+//2atA+bJysrSvn37PEV2Y+Xk5Cg7O9vzvGoDfVpaWq0Do6qNv3VtvAxWVRv1atoAFoobBINhQ56/yME8dX2GG/L5rS0PMzbWNpdQ/vv0TeGQR9URUg1heqFtGIZmzpyptWvX6u2331ZKSorX/MGDB8tqtWrr1q3KyMiQJB08eFBHjx6Vw+EwOxwAABACgmUDvRkb8ZvbxRu7vrkBLJQ3CIbDhnhy8F9dn2Ff4ro4j1D7nkuh+fepJqGchy9xm15oZ2VlafXq1XrllVfUrl07z3nXsbGxat26tWJjYzV16lRlZ2crPj5edrtdM2fOlMPh4IImAAC0MGygBwCEI9ML7eXLl0uSRo0a5TU9Pz9fd911lyTp6aefVkREhDIyMuR0OpWenq5ly5aZHQoAAAhybKAHUJfuD20IdAhAozTJoeP1iY6OVl5envLy8sx+eQAAEELYQA8ACEdNfh9tAACA2rCBHgAQjiICHQAAAAAAAOGEQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYKJWgQ4AAAAATaP7QxsCHQIAtEjs0QYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE3GONgAAAAD8f3Vd2+CTReObMRKEMvZoAwAAAABgIgptAAAAAABMRKENAAAAAICJOEcbAAAAQIvCPebR1NijDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJjI9EJ7+/btmjBhgpKSkmSxWLRu3Tqv+YZhaO7cuercubNat26t1NRUHTp0yOwwAABACGDcAAAIR6bf3uv8+fMaMGCAfvjDH2rixInV5i9evFhLly7VCy+8oJSUFM2ZM0fp6ek6cOCAoqOjzQ4HAAAEMcYNAEJJfbcF+2TR+GaKBMHO9EJ73LhxGjduXI3zDMPQkiVL9Mgjj+iWW26RJP32t79VQkKC1q1bp0mTJpkdDgAACGKMGwAA4cj0QrsuR44cUXFxsVJTUz3TYmNjNWzYMBUWFtb6g+l0OuV0Oj3Py8rKJEkul0sul8uvmGyRRuOXjTC8/kXNanuPqqbTf41T1W91fQf8+XyHOzO+v/X9/Qnn/g+Gv3/+/v03ax1oOo0dNwAAEGjNWmgXFxdLkhISErymJyQkeObVJDc3VwsWLKg2fcuWLYqJifErpsVD/VpckvToELf/KwljGzdurHM+/eefgoKCWueZ8fkOd/58/ur7bLeE/g/k97e+/m+I8vJyEyJBU2nsuKExG+irpofixpeqjXrBsAHMDOGQBzkEj+bOoyn+hoTy36dvCoc8fIm9WQvtxsrJyVF2drbneVlZmZKTk5WWlia73e7XuvvO39zoZW0Rhh4d4tacPRFyui1+xRHO9s1Pr3G6y+VSQUEB/ddIVZ+/sWPHymq11tjGn893uDPj+1vbZ7tKOPd/MPz9q6//G6KqAEN48WcDfV0bL4PVxRv1wmUDdjjkQQ7Bo7nyMGMjcG1C8e9TTUI5D1820DdroZ2YmChJKikpUefOnT3TS0pKNHDgwFqXs9lsstls1aZbrdZaC4yGclb6P0B0ui2mrCdc1fce0X/+qet7QL/Wz5/PX72f7RbQ/4H8/vr799+sdaDpNHbc0JgN9FUbf+vaeBmsqjbqBcMGMDOEQx7kEDyaOw8zNgJfLJT/Pn1TOOThywb6Zi20U1JSlJiYqK1bt3p+IMvKyrRr1y5Nnz69OUMBAABBrrHjBn820JuxEb+5XbyxK1w2YIdDHuQQPJorj6b8+xGKf59qEsp5+BK36YX2uXPndPjwYc/zI0eOaO/evYqPj1fXrl01a9YsPfbYY+rRo4fnNh1JSUm69dZbzQ4FAAAEOcYNAIBwZHqhvWfPHt1www2e51WHbmVmZmrVqlV64IEHdP78eU2bNk2lpaUaOXKkNm3axL0wAQBogRg3AADCkemF9qhRo2QYtV/Vz2KxaOHChVq4cKHZLw0AAEIM4wYAQDiKCHQAAAAAAACEk5C4vRcAAECw6/7Qhjrnf7JofJMsCyB41PVd5nvcsrBHGwAAAAAAE1FoAwAAAABgIgptAAAAAABMxDnaAAAAzaC+87ABAOGDPdoAAAAAAJiIQhsAAAAAABNx6DgAAECQ47BzILzV9R23RRpaPLQZg4Ep2KMNAAAAAICJKLQBAAAAADARhTYAAAAAACbiHG0AAAAACFN1nf/9yaLxzRhJy8IebQAAAAAATEShDQAAAACAiSi0AQAAAAAwEedoAwAAAEATq+tc6UDh/O2mwx5tAAAAAABMRKENAAAAAICJOHQcAAAAAIJc3/mb5ay0BDoMNBB7tAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARJyjDQAAAAAIafXdPq25b1fGHm0AAAAAAExEoQ0AAAAAgIkCeuh4Xl6efvazn6m4uFgDBgzQM888o6FDhwYyJAAAEISCZcxQ36GJABAu/Pl7V9dh2vXdpqy5D/FuKgHbo/3HP/5R2dnZmjdvnt5//30NGDBA6enpOnnyZKBCAgAAQYgxAwAg1ASs0H7qqad09913a8qUKerTp49WrFihmJgY/eY3vwlUSAAAIAgxZgAAhJqAHDp+4cIFFRUVKScnxzMtIiJCqampKiwsrNbe6XTK6XR6np85c0aSdPr0ablcLr9iafXl+cYv6zZUXu5WK1eEKt21H/7Q0p06darG6S6XS+Xl5fRfI1V9/k6dOiWr1VpzGz8+3+HOjO9vbZ9tz2uEcf8Hw9+/+vq/Ic6ePStJMgzD73Whafg6ZpAaN26o+k2q62+qFNzf62D4XpohHPIgh+ARDnmEYg41/UY3dOzf2N/3+v4+N/u4wQiAY8eOGZKMHTt2eE2///77jaFDh1ZrP2/ePEMSDx48ePDg0SSPzz77rLl+AuEjX8cMhsG4gQcPHjx4NO2jIeOGkLiPdk5OjrKzsz3P3W63Tp8+rQ4dOshiCdxWnbKyMiUnJ+uzzz6T3W4PWByhiv7zD/3nH/rPP+HSf4Zh6OzZs0pKSgp0KDBRY8YN4fCZDoccpPDIgxyCRzjkEQ45SOGRhy/jhoAU2pdccokiIyNVUlLiNb2kpESJiYnV2ttsNtlsNq9pcXFxTRmiT+x2e8h+WIIB/ecf+s8/9J9/wqH/YmNjAx0C6uDrmEHyb9wQDp/pcMhBCo88yCF4hEMe4ZCDFPp5NHTcEJCLoUVFRWnw4MHaunWrZ5rb7dbWrVvlcDgCERIAAAhCjBkAAKEoYIeOZ2dnKzMzU0OGDNHQoUO1ZMkSnT9/XlOmTAlUSAAAIAgxZgAAhJqAFdrf/e539Z///Edz585VcXGxBg4cqE2bNikhISFQIfnMZrNp3rx51Q5PQ8PQf/6h//xD//mH/kNzao4xQzh8psMhByk88iCH4BEOeYRDDlL45NFQFsPgniYAAAAAAJglIOdoAwAAAAAQrii0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoNkJubq6uvvlrt2rVTp06ddOutt+rgwYNebSoqKpSVlaUOHTqobdu2ysjIUElJSYAiDi7Lly9X//79PTendzgcev311z3z6buGW7RokSwWi2bNmuWZRv/Vbf78+bJYLF6PXr16eebTf3U7duyY7rjjDnXo0EGtW7dWv379tGfPHs98wzA0d+5cde7cWa1bt1ZqaqoOHToUwIiB2m3fvl0TJkxQUlKSLBaL1q1b5zX/rrvuqvb34sYbbwxMsLUIlzFJQ/IYNWpUtffjxz/+cYAiri5cxjf15RHs70NNwmG8VFMOofBeMO76GoV2A2zbtk1ZWVnauXOnCgoK5HK5lJaWpvPnz3vazJ49W6+99prWrFmjbdu26fjx45o4cWIAow4eXbp00aJFi1RUVKQ9e/Zo9OjRuuWWW7R//35J9F1D7d69W88++6z69+/vNZ3+q9+VV16pEydOeB7vvPOOZx79V7vPP/9cI0aMkNVq1euvv64DBw7oF7/4hdq3b+9ps3jxYi1dulQrVqzQrl271KZNG6Wnp6uioiKAkQM1O3/+vAYMGKC8vLxa29x4441efy/+8Ic/NGOE9QuXMUlD8pCku+++2+v9WLx4cYAiri5cxjf15SEF9/twsXAYL9WWgxQa7wXjrv/PgM9OnjxpSDK2bdtmGIZhlJaWGlar1VizZo2nzUcffWRIMgoLCwMVZlBr37698etf/5q+a6CzZ88aPXr0MAoKCozrr7/euPfeew3D4LPXEPPmzTMGDBhQ4zz6r24PPvigMXLkyFrnu91uIzEx0fjZz37mmVZaWmrYbDbjD3/4Q3OECDSaJGPt2rVe0zIzM41bbrklIPE0VriMSS7OwzAMr9+7UBEu45uqPAwjtN6HcBgv1ZaDYYTGe8G462vs0W6EM2fOSJLi4+MlSUVFRXK5XEpNTfW06dWrl7p27arCwsKAxBisKisr9dJLL+n8+fNyOBz0XQNlZWVp/PjxXv0k8dlrqEOHDikpKUnf+ta3NHnyZB09elQS/VefV199VUOGDNF3vvMdderUSYMGDdLzzz/vmX/kyBEVFxd79V9sbKyGDRtG/yFkvf322+rUqZN69uyp6dOn69SpU4EOqU7hMia5OI8qL774oi655BL17dtXOTk5Ki8vD0R49QqX8c3FeVQJlfchHMZLteVQJRTeC8ZdX2kV6ABCjdvt1qxZszRixAj17dtXklRcXKyoqCjFxcV5tU1ISFBxcXEAogw+H374oRwOhyoqKtS2bVutXbtWffr00d69e+m7erz00kt6//33tXv37mrz+OzVb9iwYVq1apV69uypEydOaMGCBbr22mu1b98++q8eH3/8sZYvX67s7Gw9/PDD2r17t+655x5FRUUpMzPT00cJCQley9F/CFU33nijJk6cqJSUFP3rX//Sww8/rHHjxqmwsFCRkZGBDq+acBmT1JSHJH3/+99Xt27dlJSUpL///e968MEHdfDgQf3lL38JYLTewmV8U1seUmi8D1J4jJfqykEKjfeCcdfXKLR9lJWVpX379nmda4D69ezZU3v37tWZM2f08ssvKzMzU9u2bQt0WEHvs88+07333quCggJFR0cHOpyQNG7cOM//+/fvr2HDhqlbt27605/+pNatWwcwsuDndrs1ZMgQPfHEE5KkQYMGad++fVqxYoUyMzMDHB1gvkmTJnn+369fP/Xv31+XXXaZ3n77bY0ZMyaAkdUsXMYkteUxbdo0z//79eunzp07a8yYMfrXv/6lyy67rLnDrFG4jG9qy6NPnz4h8T6Ew3ipITmEwnvBuOtrHDrugxkzZmj9+vV666231KVLF8/0xMREXbhwQaWlpV7tS0pKlJiY2MxRBqeoqChdfvnlGjx4sHJzczVgwAD98pe/pO/qUVRUpJMnT+qqq65Sq1at1KpVK23btk1Lly5Vq1atlJCQQP/5KC4uTldccYUOHz7M568enTt39uzRqNK7d2/PIWBVfXTx1ULpP4SLb33rW7rkkkt0+PDhQIdSTbiMSWrLoybDhg2TpKB6P8JlfFNbHjUJxvchHMZL9eVQWVlZbZlgfC8u1pLHXRTaDWAYhmbMmKG1a9fqzTffVEpKitf8wYMHy2q1auvWrZ5pBw8e1NGjR73Ob8HX3G63nE4nfVePMWPG6MMPP9TevXs9jyFDhmjy5Mme/9N/vjl37pz+9a9/qXPnznz+6jFixIhqt9v55z//qW7dukmSUlJSlJiY6NV/ZWVl2rVrF/2HsPDvf/9bp06dUufOnQMdike4jEnqy6Mme/fulaSgej8uFi7jm6o8ahKM70M4jJfqy6Gm01eC8b24WIsedwX4YmwhYfr06UZsbKzx9ttvGydOnPA8ysvLPW1+/OMfG127djXefPNNY8+ePYbD4TAcDkcAow4eDz30kLFt2zbjyJEjxt///nfjoYceMiwWi7FlyxbDMOg7X118xUn6r2733Xef8fbbbxtHjhwx3n33XSM1NdW45JJLjJMnTxqGQf/V5b333jNatWplPP7448ahQ4eMF1980YiJiTF+//vfe9osWrTIiIuLM1555RXj73//u3HLLbcYKSkpxhdffBHAyIGanT171vjggw+MDz74wJBkPPXUU8YHH3xgfPrpp8bZs2eNn/70p0ZhYaFx5MgR44033jCuuuoqo0ePHkZFRUWgQ/cIlzFJfXkcPnzYWLhwobFnzx7jyJEjxiuvvGJ861vfMq677roAR/61cBnf1JVHKLwPtQmH8dI3cwiV94Jx19cotBtAUo2P/Px8T5svvvjC+MlPfmK0b9/eiImJMW677TbjxIkTgQs6iPzwhz80unXrZkRFRRkdO3Y0xowZ4/kRMgz6zlcX/3DQf3X77ne/a3Tu3NmIiooyLr30UuO73/2ucfjwYc98+q9ur732mtG3b1/DZrMZvXr1Mp577jmv+W6325gzZ46RkJBg2Gw2Y8yYMcbBgwcDFC1Qt7feeqvG3/PMzEyjvLzcSEtLMzp27GhYrVajW7duxt13320UFxcHOmwv4TImqS+Po0ePGtddd50RHx9v2Gw24/LLLzfuv/9+48yZM4EN/BvCZXxTVx6h8D7UJhzGS9/MIVTeC8ZdX7MYhmE03/5zAAAAAADCG+doAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYQYKtWrZLFYtGePXvqbTtq1CiNGjXK8/yTTz6RxWLRqlWr6l32rrvuUvfu3RsfKAAACDhfxg0AAodCGwAAAAAAE7UKdAAAGm7Lli2BDgEAAABAPdijDYSQqKgoRUVFNdn6v/zyS124cKHJ1g8AAEJDeXl5oEMAQhqFNtAMjh07pqlTpyopKUk2m00pKSmaPn26V1HrdDqVnZ2tjh07qk2bNrrtttv0n//8x2s9F5+jXZt169apb9++io6OVt++fbV27dpqbarO7/75z3+uJUuW6LLLLpPNZtOBAwckSf/4xz/0P//zP4qPj1d0dLSGDBmiV1991WsdVeeJvfvuu/XGDgAAGsasccMrr7yi8ePHe9Zz2WWX6dFHH1VlZaVXu1GjRqlv374qKirSddddp5iYGD388MOSpFOnTunOO++U3W5XXFycMjMz9be//a3Ga8Q0ZOzgcrm0YMEC9ejRQ9HR0erQoYNGjhypgoICE3sQCDwOHQea2PHjxzV06FCVlpZq2rRp6tWrl44dO6aXX37Za2vxzJkz1b59e82bN0+ffPKJlixZohkzZuiPf/yjT6+3ZcsWZWRkqE+fPsrNzdWpU6c0ZcoUdenSpcb2+fn5qqio0LRp02Sz2RQfH6/9+/drxIgRuvTSS/XQQw+pTZs2+tOf/qRbb71Vf/7zn3Xbbbd5rcOs2AEAaOnMHDesWrVKbdu2VXZ2ttq2bas333xTc+fOVVlZmX72s595ve6pU6c0btw4TZo0SXfccYcSEhLkdrs1YcIEvffee5o+fbp69eqlV155RZmZmdXibujYYf78+crNzdX//u//aujQoSorK9OePXv0/vvva+zYsU3Uq0AAGACa1A9+8AMjIiLC2L17d7V5brfbyM/PNyQZqamphtvt9sybPXu2ERkZaZSWlnqmXX/99cb111/veX7kyBFDkpGfn++ZNnDgQKNz585ey23ZssWQZHTr1q3asna73Th58qRXXGPGjDH69etnVFRUeMV6zTXXGD169PBM8yV2AABQPzPHDeXl5dXW8aMf/ciIiYnx+o2//vrrDUnGihUrvNr++c9/NiQZS5Ys8UyrrKw0Ro8eXW380dCxw4ABA4zx48c3sDeA0MWh40ATcrvdWrdunSZMmKAhQ4ZUm2+xWDz/nzZtmtfza6+9VpWVlfr0008b/HonTpzQ3r17lZmZqdjYWM/0sWPHqk+fPjUuk5GRoY4dO3qenz59Wm+++aZuv/12nT17Vv/973/13//+V6dOnVJ6eroOHTqkY8eOea3DjNgBAGjpzB43tG7d2vP/qt/0a6+9VuXl5frHP/7htW6bzaYpU6Z4Tdu0aZOsVqvuvvtuz7SIiAhlZWV5tfNl7BAXF6f9+/fr0KFDvnQNEHIotIEm9J///EdlZWXq27dvvW27du3q9bx9+/aSpM8//7zBr1f149qjR49q83r27FnjMikpKV7PDx8+LMMwNGfOHHXs2NHrMW/ePEnSyZMnTY8dAICWzuxxw/79+3XbbbcpNjZWdrtdHTt21B133CFJOnPmjNfyl156abULrn766afq3LmzYmJivKZffvnlXs99GTssXLhQpaWluuKKK9SvXz/df//9+vvf/15vvkCo4RxtIEhERkbWON0wjCZ93W9u7Za+2pouST/96U+Vnp5e4zIX/8AGKnYAAFqq+n57S0tLdf3118tut2vhwoW67LLLFB0drffff18PPvig5/e+ysXjAV/4Mna47rrr9K9//UuvvPKKtmzZol//+td6+umntWLFCv3v//5vo2MAgg2FNtCEOnbsKLvdrn379jXL63Xr1k2Sajwc6+DBgw1ax7e+9S1JktVqVWpqqnnBAQCAOpk5bnj77bd16tQp/eUvf9F1113nmX7kyJEGr6Nbt2566623VF5e7rVX+/Dhw17tfB07xMfHa8qUKZoyZYrOnTun6667TvPnz6fQRljh0HGgCUVEROjWW2/Va6+9pj179lSbb/Ye386dO2vgwIF64YUXvA4JKygo8Ny2qz6dOnXSqFGj9Oyzz+rEiRPV5nPbLgAAmoaZ44aqPd7fXObChQtatmxZg9eRnp4ul8ul559/3jPN7XYrLy/Pq50vY4dTp055zWvbtq0uv/xyOZ3OBscFhAL2aANN7IknntCWLVt0/fXXa9q0aerdu7dOnDihNWvW6J133jH99XJzczV+/HiNHDlSP/zhD3X69Gk988wzuvLKK3Xu3LkGrSMvL08jR45Uv379dPfdd+tb3/qWSkpKVFhYqH//+9/629/+ZnrcAADAvHHDNddco/bt2yszM1P33HOPLBaLfve73/lUrN96660aOnSo7rvvPh0+fFi9evXSq6++qtOnT0vyvjhbQ8cOffr00ahRozR48GDFx8drz549evnllzVjxowGxwWEAgptoIldeuml2rVrl+bMmaMXX3xRZWVluvTSSzVu3LhqFxcxw4033qg1a9bokUceUU5Oji677DLl5+frlVde0dtvv92gdfTp00d79uzRggULtGrVKp06dUqdOnXSoEGDNHfuXNNjBgAAXzFr3NChQwetX79e9913nx555BG1b99ed9xxh8aMGVPredQXi4yM1IYNG3TvvffqhRdeUEREhG677TbNmzdPI0aMUHR0tKdtQ8cO99xzj1599VVt2bJFTqdT3bp102OPPab777+/4Z0EhACLwdWKAAAAADTQunXrdNttt+mdd97RiBEjAh0OEJQotAEAAADU6IsvvvC6InllZaXS0tK0Z88eFRcX+3W1ciCcceg4AAAAgBrNnDlTX3zxhRwOh5xOp/7yl79ox44deuKJJyiygTqwRxsAAABAjVavXq1f/OIXOnz4sCoqKnT55Zdr+vTpXLwMqAeFNgAAAAAAJvL5PtrHjh3THXfcoQ4dOqh169bq16+f133+DMPQ3Llz1blzZ7Vu3Vqpqak6dOiQ1zpOnz6tyZMny263Ky4uTlOnTm3wbYcAAAAAAAhmPhXan3/+uUaMGCGr1arXX39dBw4c0C9+8Qu1b9/e02bx4sVaunSpVqxYoV27dqlNmzZKT09XRUWFp83kyZO1f/9+FRQUaP369dq+fbumTZtmXlYAAAAAAASIT4eOP/TQQ3r33Xf117/+tcb5hmEoKSlJ9913n376059Kks6cOaOEhAStWrVKkyZN0kcffaQ+ffpo9+7dGjJkiCRp06ZNuummm/Tvf/9bSUlJ9cbhdrt1/PhxtWvXThaLpaHhAwDgxTAMnT17VklJSYqI8PkgL4QIxg0AADP4Mm7w6arjr776qtLT0/Wd73xH27Zt06WXXqqf/OQnuvvuuyVJR44cUXFxsVJTUz3LxMbGatiwYSosLNSkSZNUWFiouLg4T5EtSampqYqIiNCuXbt022231RvH8ePHlZyc7EvoAADU6rPPPlOXLl0CHQaaCOMGAICZGjJu8KnQ/vjjj7V8+XJlZ2fr4Ycf1u7du3XPPfcoKipKmZmZKi4uliQlJCR4LZeQkOCZV1xcrE6dOnkH0aqV4uPjPW0u5nQ65XQ6Pc+rdsIfOXJE7dq18yUFLy6XS2+99ZZuuOEGWa3WRq8n3NFPDUM/1Y8+ahj6qX5m9dHZs2eVkpLi128Jard9+3b97Gc/U1FRkU6cOKG1a9fq1ltvrbHtj3/8Yz377LN6+umnNWvWLM/006dPa+bMmXrttdcUERGhjIwM/fKXv1Tbtm0bHEfV+/vZZ5/JbrfX297lcmnLli1KS0sL6+9gS8lTItdw1FLylMg1mJSVlSk5OblB4wafCm23260hQ4boiSeekCQNGjRI+/bt04oVK5SZmdm4aBsgNzdXCxYsqDa9sLBQMTExfq07JiZGu3bt8msdLQH91DD0U/3oo4ahn+pnRh+Vl5dLEocTN5Hz589rwIAB+uEPf6iJEyfW2m7t2rXauXNnjaePTZ48WSdOnFBBQYFcLpemTJmiadOmafXq1Q2Oo+r9tdvtDS60Y2JiZLfbg3KgZ5aWkqdEruGopeQpkWswasi4wadCu3PnzurTp4/XtN69e+vPf/6zJCkxMVGSVFJSos6dO3valJSUaODAgZ42J0+e9FrHl19+qdOnT3uWv1hOTo6ys7M9z6u2JKSlpTXoB7M2LpdLBQUFGjt2bFC/kYFGPzUM/VQ/+qhh6Kf6mdVHZWVlJkaFi40bN07jxo2rs82xY8c0c+ZMbd68WePHj/ea99FHH2nTpk1e13V55plndNNNN+nnP/95g67rAgBAIPhUaI8YMUIHDx70mvbPf/5T3bp1kySlpKQoMTFRW7du9RTWZWVl2rVrl6ZPny5JcjgcKi0tVVFRkQYPHixJevPNN+V2uzVs2LAaX9dms8lms1WbbrVaTRmEmrWecEc/NQz9VD/6qGHop/r520f0b2C53W7deeeduv/++3XllVdWm2/GdV0AAAgEnwrt2bNn65prrtETTzyh22+/Xe+9956ee+45Pffcc5K+2oU+a9YsPfbYY+rRo4dSUlI0Z84cJSUlec7J6t27t2688UbdfffdWrFihVwul2bMmKFJkyaxZRoAgBbkySefVKtWrXTPPffUOL8x13WRql/bperIBZfLJZfLVW9cVW0a0jaUtZQ8JXINRy0lT4lcg4kvcflUaF999dVau3atcnJytHDhQqWkpGjJkiWaPHmyp80DDzyg8+fPa9q0aSotLdXIkSO1adMmRUdHe9q8+OKLmjFjhsaMGeO5sMnSpUt9CQUAAISwoqIi/fKXv9T7779v+jnytV3bZcuWLT5d26WgoMDMsIJWS8lTItdw1FLylMg1GFRd26UhfCq0Jenmm2/WzTffXOt8i8WihQsXauHChbW2iY+P9+kiJgAAILz89a9/1cmTJ9W1a1fPtMrKSt13331asmSJPvnkk0Zd10Xy/9ouLeU6CS0lT4lcw1FLyVMi12Diy7VdfC60AQAA/HXnnXcqNTXVa1p6erruvPNOTZkyRVLjrusimXdtl5ZynYSWkqdEruGopeQpkWsw8CUmCm0AANAkzp07p8OHD3ueHzlyRHv37lV8fLy6du2qDh06eLW3Wq1KTExUz549JXFdFwBA6IoIdAAAACA87dmzR4MGDdKgQYMkSdnZ2Ro0aJDmzp3b4HW8+OKL6tWrl8aMGaObbrpJI0eO9FyEFQCAYMUebQAA0CRGjRolwzAa3P6TTz6pNo3rugAAQhF7tAEAAAAAMBF7tCX1nb9Zzsqaby3yyaLxzRwNAAAIVt0f2lDrPMYMAIAq7NEGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAKBJbN++XRMmTFBSUpIsFovWrVvnmedyufTggw+qX79+atOmjZKSkvSDH/xAx48f91rH6dOnNXnyZNntdsXFxWnq1Kk6d+5cM2cCAIBvKLQBAECTOH/+vAYMGKC8vLxq88rLy/X+++9rzpw5ev/99/WXv/xFBw8e1Le//W2vdpMnT9b+/ftVUFCg9evXa/v27Zo2bVpzpQAAQKO0CnQAAAAgPI0bN07jxo2rcV5sbKwKCgq8pv3qV7/S0KFDdfToUXXt2lUfffSRNm3apN27d2vIkCGSpGeeeUY33XSTfv7znyspKanJcwAAoDF8KrTnz5+vBQsWeE3r2bOn/vGPf0iSKioqdN999+mll16S0+lUenq6li1bpoSEBE/7o0ePavr06XrrrbfUtm1bZWZmKjc3V61aUfMDANCSnTlzRhaLRXFxcZKkwsJCxcXFeYpsSUpNTVVERIR27dql2267rcb1OJ1OOZ1Oz/OysjJJXx2u7nK56o2jqk1NbW2RRr3LhYq68gw35Bp+WkqeErkGE1/i8rm6vfLKK/XGG298vYJvFMizZ8/Whg0btGbNGsXGxmrGjBmaOHGi3n33XUlSZWWlxo8fr8TERO3YsUMnTpzQD37wA1mtVj3xxBO+hgIAAMJERUWFHnzwQX3ve9+T3W6XJBUXF6tTp05e7Vq1aqX4+HgVFxfXuq7c3NxqOwYkacuWLYqJiWlwTBfvcZekxUNrb79x48YGrzuY1JRnuCLX8NNS8pTINRiUl5c3uK3PhXarVq2UmJhYbfqZM2e0cuVKrV69WqNHj5Yk5efnq3fv3tq5c6eGDx+uLVu26MCBA3rjjTeUkJCggQMH6tFHH9WDDz6o+fPnKyoqytdwAABAiHO5XLr99ttlGIaWL1/u9/pycnKUnZ3teV5WVqbk5GSlpaV5ivj64ikoKNDYsWNltVq95vWdv7nW5fbNT2980AFQV57hhlzDT0vJUyLXYFJ1hFRD+FxoHzp0SElJSYqOjpbD4VBubq66du2qoqIiuVwupaametr26tVLXbt2VWFhoYYPH67CwkL169fP61Dy9PR0TZ8+Xfv379egQYNqfE1/DwGrTdWytojwOQysKQT7IRzBgn6qH33UMPRT/czqI/o48KqK7E8//VRvvvmmVyGcmJiokydPerX/8ssvdfr06Ro3+lex2Wyy2WzVplutVp8GbjW1d1Za6mwfinztl1BGruGnpeQpkWsw8CUmnwrtYcOGadWqVerZs6dOnDihBQsW6Nprr9W+fftUXFysqKgoz3lVVRISEjyHdxUXF3sV2VXzq+bVxqxDwGrz6BB3rfNC9TCwphCsh3AEG/qpfvRRw9BP9fO3j3w5BAzmqyqyDx06pLfeeksdOnTwmu9wOFRaWqqioiINHjxYkvTmm2/K7XZr2LBhgQgZAIAG8anQ/uaVQ/v3769hw4apW7du+tOf/qTWrVubHlwVfw8Bq03VoQlz9kTI6a55C3WoHQbWFIL9EI5gQT/Vjz5qGPqpfmb1kS+HgMF3586d0+HDhz3Pjxw5or179yo+Pl6dO3fW//zP/+j999/X+vXrVVlZ6dnoHh8fr6ioKPXu3Vs33nij7r77bq1YsUIul0szZszQpEmTuOI4ACCo+XWp77i4OF1xxRU6fPiwxo4dqwsXLqi0tNRrr3ZJSYnn8K7ExES99957XusoKSnxzKuNWYeA1cbpttR6KBiD3K8F6yEcwYZ+qh991DD0U/387SP6t2nt2bNHN9xwg+d51UbzzMxMzZ8/X6+++qokaeDAgV7LvfXWWxo1apQk6cUXX9SMGTM0ZswYRUREKCMjQ0uXLm2W+AEAaCy/Cu1z587pX//6l+68804NHjxYVqtVW7duVUZGhiTp4MGDOnr0qBwOh6SvDgF7/PHHdfLkSc9VRAsKCmS329WnTx8/UwEAAMFk1KhRMozar4NS17wq8fHxWr16tZlhAQDQ5HwqtH/6059qwoQJ6tatm44fP6558+YpMjJS3/ve9xQbG6upU6cqOztb8fHxstvtmjlzphwOh4YPHy5JSktLU58+fXTnnXdq8eLFKi4u1iOPPKKsrKwa91gDAAAAABBqfCq0//3vf+t73/ueTp06pY4dO2rkyJHauXOnOnbsKEl6+umnPYd1OZ1Opaena9myZZ7lIyMjtX79ek2fPl0Oh0Nt2rRRZmamFi5caG5WAAAAAAAEiE+F9ksvvVTn/OjoaOXl5SkvL6/WNt26deNK3gAAAACAsBUR6AAAAAAAAAgnFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmahXoAAAAAMJB94c2NHrZTxaNNzESAECgsUcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAATWL79u2aMGGCkpKSZLFYtG7dOq/5hmFo7ty56ty5s1q3bq3U1FQdOnTIq83p06c1efJk2e12xcXFaerUqTp37lwzZgEAgO8otAEAQJM4f/68BgwYoLy8vBrnL168WEuXLtWKFSu0a9cutWnTRunp6aqoqPC0mTx5svbv36+CggKtX79e27dv17Rp05orBQAAGqVVoAMAAADhady4cRo3blyN8wzD0JIlS/TII4/olltukST99re/VUJCgtatW6dJkybpo48+0qZNm7R7924NGTJEkvTMM8/opptu0s9//nMlJSU1Wy4AAPiCPdoAAKDZHTlyRMXFxUpNTfVMi42N1bBhw1RYWChJKiwsVFxcnKfIlqTU1FRFRERo165dzR4zAAANxR5tAADQ7IqLiyVJCQkJXtMTEhI884qLi9WpUyev+a1atVJ8fLynTU2cTqecTqfneVlZmSTJ5XLJ5XLVG1tVm5ra2iKNepdvjIbE1VSvGYjXbm7kGn5aSp4SuQYTX+Ki0AYAAGElNzdXCxYsqDZ9y5YtiomJafB6CgoKqk1bPNSv0Gq1cePGpllxA9SUZ7gi1/DTUvKUyDUYlJeXN7gthTYAAGh2iYmJkqSSkhJ17tzZM72kpEQDBw70tDl58qTXcl9++aVOnz7tWb4mOTk5ys7O9jwvKytTcnKy0tLSZLfb643N5XKpoKBAY8eOldVq9ZrXd/7mepdvjH3z05tkvXWpK89wQ67hp6XkKZFrMKk6QqohKLQBAECzS0lJUWJiorZu3eoprMvKyrRr1y5Nnz5dkuRwOFRaWqqioiINHjxYkvTmm2/K7XZr2LBhta7bZrPJZrNVm261Wn0auNXU3llpafDyvgjkgNLXfgll5Bp+WkqeErkGA19iotAGAABN4ty5czp8+LDn+ZEjR7R3717Fx8era9eumjVrlh577DH16NFDKSkpmjNnjpKSknTrrbdKknr37q0bb7xRd999t1asWCGXy6UZM2Zo0qRJLe6K490f2lDrvE8WjW/GSAAADUGhDQAAmsSePXt0ww03eJ5XHc6dmZmpVatW6YEHHtD58+c1bdo0lZaWauTIkdq0aZOio6M9y7z44ouaMWOGxowZo4iICGVkZGjp0qXNngsAAL7w6/ZeixYtksVi0axZszzTKioqlJWVpQ4dOqht27bKyMhQSUmJ13JHjx7V+PHjFRMTo06dOun+++/Xl19+6U8oAAAgyIwaNUqGYVR7rFq1SpJksVi0cOFCFRcXq6KiQm+88YauuOIKr3XEx8dr9erVOnv2rM6cOaPf/OY3atu2bQCyAQCg4RpdaO/evVvPPvus+vfv7zV99uzZeu2117RmzRpt27ZNx48f18SJEz3zKysrNX78eF24cEE7duzQCy+8oFWrVmnu3LmNzwIAAAAAgCDRqEL73Llzmjx5sp5//nm1b9/eM/3MmTNauXKlnnrqKY0ePVqDBw9Wfn6+duzYoZ07d0r66tYaBw4c0O9//3sNHDhQ48aN06OPPqq8vDxduHDBnKwAAAAAAAiQRhXaWVlZGj9+vFJTU72mFxUVyeVyeU3v1auXunbtqsLCQklSYWGh+vXrp4SEBE+b9PR0lZWVaf/+/Y0JBwAAAACAoOHzxdBeeuklvf/++9q9e3e1ecXFxYqKilJcXJzX9ISEBBUXF3vafLPIrppfNa8mTqdTTqfT87zq/mUul0sul8vXFDyqlrVFGPW2acmq+oC+qBv9VD/6qGHop/qZ1Uf0MQAAaAo+FdqfffaZ7r33XhUUFHhdEbSp5ebmasGCBdWmb9myRTExMX6v/9Eh7lrnbdy40e/1h4uCgoJAhxAS6Kf60UcNQz/Vz98+Ki8vNykSAACAr/lUaBcVFenkyZO66qqrPNMqKyu1fft2/epXv9LmzZt14cIFlZaWeu3VLikpUWJioiQpMTFR7733ntd6q65KXtXmYjk5OZ5bgkhf7dFOTk5WWlqa7Ha7Lyl4cblcKigo0Jw9EXK6LTW22Tc/vdHrDxdV/TR27NigvHF8sKCf6kcfNQz9VD+z+qjqCCkAAAAz+VRojxkzRh9++KHXtClTpqhXr1568MEHlZycLKvVqq1btyojI0OSdPDgQR09elQOh0OS5HA49Pjjj+vkyZPq1KmTpK/2SNjtdvXp06fG17XZbLLZbNWmW61WUwahTrdFzsqaC20GuV8zq7/DHf1UP/qoYein+vnbR/QvAABoCj4V2u3atVPfvn29prVp00YdOnTwTJ86daqys7MVHx8vu92umTNnyuFwaPjw4ZKktLQ09enTR3feeacWL16s4uJiPfLII8rKyqqxmAYAAAAAIJT4fDG0+jz99NOKiIhQRkaGnE6n0tPTtWzZMs/8yMhIrV+/XtOnT5fD4VCbNm2UmZmphQsXmh0KAAAAAADNzu9C++233/Z6Hh0drby8POXl5dW6TLdu3bjIGAAAAAAgLDXqPtoAAAAAAKBmFNoAAAAAAJjI9HO0AQAA4JvuD21okmU/WTS+0esFADQee7QBAAAAADARhTYAAAAAACai0AYAAAAAwEScow0AAIBqOPcbABqPPdoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABO1CnQAAACgZaqsrNT8+fP1+9//XsXFxUpKStJdd92lRx55RBaLRZJkGIbmzZun559/XqWlpRoxYoSWL1+uHj16BDj60ND9oQ21zrNFGlo8tBmDAYAWhD3aAAAgIJ588kktX75cv/rVr/TRRx/pySef1OLFi/XMM8942ixevFhLly7VihUrtGvXLrVp00bp6emqqKgIYOQAANSNPdoAACAgduzYoVtuuUXjx4+XJHXv3l1/+MMf9N5770n6am/2kiVL9Mgjj+iWW26RJP32t79VQkKC1q1bp0mTJgUsdgAA6kKhDQAAAuKaa67Rc889p3/+85+64oor9Le//U3vvPOOnnrqKUnSkSNHVFxcrNTUVM8ysbGxGjZsmAoLC2sttJ1Op5xOp+d5WVmZJMnlcsnlctUbV1WbmtraIo2GJxjkbBFf5VJbn9SVa0P6MZjU9Z6Gm5aSa0vJUyLXYOJLXBTaAAAgIB566CGVlZWpV69eioyMVGVlpR5//HFNnjxZklRcXCxJSkhI8FouISHBM68mubm5WrBgQbXpW7ZsUUxMTIPjKygoqDYtHM9prilPqe5cN27c2ETRNK3acg1HLSXXlpKnRK7BoLy8vMFtKbQBAEBA/OlPf9KLL76o1atX68orr9TevXs1a9YsJSUlKTMzs9HrzcnJUXZ2tud5WVmZkpOTlZaWJrvdXu/yLpdLBQUFGjt2rKxWq9e8vvM3NzquYGOLMPToEHeNeUp157pvfnpThma6ut7TcNNScm0peUrkGkyqjpBqCAptAAAQEPfff78eeughzyHg/fr106effqrc3FxlZmYqMTFRklRSUqLOnTt7lispKdHAgQNrXa/NZpPNZqs23Wq1+jRwq6m9s9LS4OVDRW39UleuwTgAbghfPwOhrKXk2lLylMg1GPgSE1cdBwAAAVFeXq6ICO+hSGRkpNxutyQpJSVFiYmJ2rp1q2d+WVmZdu3aJYfD0ayxAgDgC/ZoAwCAgJgwYYIef/xxde3aVVdeeaU++OADPfXUU/rhD38oSbJYLJo1a5Yee+wx9ejRQykpKZozZ46SkpJ06623BjZ4AADqQKENAAAC4plnntGcOXP0k5/8RCdPnlRSUpJ+9KMfae7cuZ42DzzwgM6fP69p06aptLRUI0eO1KZNmxQdHR3AyAEAqBuFNhqs7/zNNZ6v9cmi8QGIBgAQ6tq1a6clS5ZoyZIltbaxWCxauHChFi5c2HyBAQDgJ87RBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIl8KrSXL1+u/v37y263y263y+Fw6PXXX/fMr6ioUFZWljp06KC2bdsqIyNDJSUlXus4evSoxo8fr5iYGHXq1En333+/vvzyS3OyAQAAgE/6zt+s7g9tqPYAADSeT4V2ly5dtGjRIhUVFWnPnj0aPXq0brnlFu3fv1+SNHv2bL322mtas2aNtm3bpuPHj2vixIme5SsrKzV+/HhduHBBO3bs0AsvvKBVq1Zp7ty55mYFAAAAAECAtPKl8YQJE7yeP/7441q+fLl27typLl26aOXKlVq9erVGjx4tScrPz1fv3r21c+dODR8+XFu2bNGBAwf0xhtvKCEhQQMHDtSjjz6qBx98UPPnz1dUVJR5mQEAAAAAEAA+FdrfVFlZqTVr1uj8+fNyOBwqKiqSy+VSamqqp02vXr3UtWtXFRYWavjw4SosLFS/fv2UkJDgaZOenq7p06dr//79GjRoUI2v5XQ65XQ6Pc/LysokSS6XSy6Xq7EpeJa1RRj1tmnJ6usn+ugrVf1Af9SOPmoY+ql+ZvURfQwAAJqCz4X2hx9+KIfDoYqKCrVt21Zr165Vnz59tHfvXkVFRSkuLs6rfUJCgoqLiyVJxcXFXkV21fyqebXJzc3VggULqk3fsmWLYmJifE2hmkeHuGudt3HjRr/XHy5q6yf6yFtBQUGgQwh69FHD0E/187ePysvLTYoEAADgaz4X2j179tTevXt15swZvfzyy8rMzNS2bduaIjaPnJwcZWdne56XlZUpOTlZaWlpstvtjV6vy+VSQUGB5uyJkNNtqbHNvvnpjV5/uKivn+ijr1T109ixY2W1WgMdTlCijxqGfqqfWX1UdYQUAACAmXwutKOionT55ZdLkgYPHqzdu3frl7/8pb773e/qwoULKi0t9dqrXVJSosTERElSYmKi3nvvPa/1VV2VvKpNTWw2m2w2W7XpVqvVlEGo022Rs7LmQptB7tdq6yf6yJtZn8twRh81DP1UP3/7iP4FAABNwe/7aLvdbjmdTg0ePFhWq1Vbt271zDt48KCOHj0qh8MhSXI4HPrwww918uRJT5uCggLZ7Xb16dPH31AAAAAAAAg4n/Zo5+TkaNy4ceratavOnj2r1atX6+2339bmzZsVGxurqVOnKjs7W/Hx8bLb7Zo5c6YcDoeGDx8uSUpLS1OfPn105513avHixSouLtYjjzyirKysGvdYAwAAAAAQanwqtE+ePKkf/OAHOnHihGJjY9W/f39t3rxZY8eOlSQ9/fTTioiIUEZGhpxOp9LT07Vs2TLP8pGRkVq/fr2mT58uh8OhNm3aKDMzUwsXLjQ3KwAAAAAAAsSnQnvlypV1zo+OjlZeXp7y8vJqbdOtWzeuUg0AAAAACFt+n6MNAAAAAAC+RqENAAAAAICJKLQBAAAAADARhTYAAAAAACby6WJoAAAAQPeHNtQ5/5NF45spEgAITuzRBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AABAwx44d0x133KEOHTqodevW6tevn/bs2eOZbxiG5s6dq86dO6t169ZKTU3VoUOHAhgxAAD1o9AGAAAB8fnnn2vEiBGyWq16/fXXdeDAAf3iF79Q+/btPW0WL16spUuXasWKFdq1a5fatGmj9PR0VVRUBDByAADqxu29AABAQDz55JNKTk5Wfn6+Z1pKSorn/4ZhaMmSJXrkkUd0yy23SJJ++9vfKiEhQevWrdOkSZOaPWYAABqCPdoAACAgXn31VQ0ZMkTf+c531KlTJw0aNEjPP/+8Z/6RI0dUXFys1NRUz7TY2FgNGzZMhYWFgQgZAIAGYY82AAAIiI8//ljLly9Xdna2Hn74Ye3evVv33HOPoqKilJmZqeLiYklSQkKC13IJCQmeeTVxOp1yOp2e52VlZZIkl8sll8tVb1xVbWpqa4s06k8sRNgiDK9/zdSQfm5Odb2n4aal5NpS8pTINZj4EheFNgAACAi3260hQ4boiSeekCQNGjRI+/bt04oVK5SZmdno9ebm5mrBggXVpm/ZskUxMTENXk9BQUG1aYuHNjqsoPXoELfp69y4caPp6zRDTe9puGopubaUPCVyDQbl5eUNbkuhDQAAAqJz587q06eP17TevXvrz3/+syQpMTFRklRSUqLOnTt72pSUlGjgwIG1rjcnJ0fZ2dme52VlZUpOTlZaWprsdnu9cblcLhUUFGjs2LGyWq1e8/rO31zv8qHCFmHo0SFuzdkTIafbYuq6981PN3V9/qrrPQ03LSXXlpKnRK7BpOoIqYag0AYAAAExYsQIHTx40GvaP//5T3Xr1k3SVxdGS0xM1NatWz2FdVlZmXbt2qXp06fXul6bzSabzVZtutVq9WngVlN7Z6W5BWkwcLotpucVjANkyffPQChrKbm2lDwlcg0GvsREoQ0AAAJi9uzZuuaaa/TEE0/o9ttv13vvvafnnntOzz33nCTJYrFo1qxZeuyxx9SjRw+lpKRozpw5SkpK0q233hrY4AEAqAOFNgAACIirr75aa9euVU5OjhYuXKiUlBQtWbJEkydP9rR54IEHdP78eU2bNk2lpaUaOXKkNm3apOjo6ABGDgBA3Si0AQBAwNx88826+eaba51vsVi0cOFCLVy4sBmjAgDAPxTagMn6zt9c47lunywaH4BoAAAAADS3iEAHAAAAAABAOKHQBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARBTaAAAAAACYiEIbAAAAAAAT+VRo5+bm6uqrr1a7du3UqVMn3XrrrTp48KBXm4qKCmVlZalDhw5q27atMjIyVFJS4tXm6NGjGj9+vGJiYtSpUyfdf//9+vLLL/3PBgAAAACAAPOp0N62bZuysrK0c+dOFRQUyOVyKS0tTefPn/e0mT17tl577TWtWbNG27Zt0/HjxzVx4kTP/MrKSo0fP14XLlzQjh079MILL2jVqlWaO3eueVkBAAAAABAgrXxpvGnTJq/nq1atUqdOnVRUVKTrrrtOZ86c0cqVK7V69WqNHj1akpSfn6/evXtr586dGj58uLZs2aIDBw7ojTfeUEJCggYOHKhHH31UDz74oObPn6+oqCjzsgMAAAAAoJn5VGhf7MyZM5Kk+Ph4SVJRUZFcLpdSU1M9bXr16qWuXbuqsLBQw4cPV2Fhofr166eEhARPm/T0dE2fPl379+/XoEGDqr2O0+mU0+n0PC8rK5MkuVwuuVyuRsdftawtwqi3TUtWXz/RR1+hn+pX1Qf0Rd3op/qZ1Uf0MQAAaAqNLrTdbrdmzZqlESNGqG/fvpKk4uJiRUVFKS4uzqttQkKCiouLPW2+WWRXza+aV5Pc3FwtWLCg2vQtW7YoJiamsSl4PDrEXeu8jRs3+r3+cFFbP9FH3uin+hUUFAQ6hJBAP9XP3z4qLy83KRIAAICvNbrQzsrK0r59+/TOO++YGU+NcnJylJ2d7XleVlam5ORkpaWlyW63N3q9LpdLBQUFmrMnQk63pcY2++anN3r94aK+fqKPvkI/1a+qj8aOHSur1RrocIIW/VQ/s/qo6ggpAAAAMzWq0J4xY4bWr1+v7du3q0uXLp7piYmJunDhgkpLS732apeUlCgxMdHT5r333vNaX9VVyavaXMxms8lms1WbbrVaTRmEOt0WOStrLrQZ5H6ttn6ij7zRT/Uz67sb7uin+vnbR/QvAABoCj5dddwwDM2YMUNr167Vm2++qZSUFK/5gwcPltVq1datWz3TDh48qKNHj8rhcEiSHA6HPvzwQ508edLTpqCgQHa7XX369PEnFwAAAAAAAs6nPdpZWVlavXq1XnnlFbVr185zTnVsbKxat26t2NhYTZ06VdnZ2YqPj5fdbtfMmTPlcDg0fPhwSVJaWpr69OmjO++8U4sXL1ZxcbEeeeQRZWVl1bjXGgAAAACAUOJTob18+XJJ0qhRo7ym5+fn66677pIkPf3004qIiFBGRoacTqfS09O1bNkyT9vIyEitX79e06dPl8PhUJs2bZSZmamFCxf6lwkAAACCQveHNtQ675NF45sxEgAIDJ8KbcOo/TZYVaKjo5WXl6e8vLxa23Tr1o0rMAMAAAAAwpJP52gDAAAAAIC6UWgDAAAAAGAiCm0AAAAAAExEoQ0AAILCokWLZLFYNGvWLM+0iooKZWVlqUOHDmrbtq0yMjJUUlISuCABAGgACm0AABBwu3fv1rPPPqv+/ft7TZ89e7Zee+01rVmzRtu2bdPx48c1ceLEAEUJAEDDUGgDAICAOnfunCZPnqznn39e7du390w/c+aMVq5cqaeeekqjR4/W4MGDlZ+frx07dmjnzp0BjBgAgLr5dHsvAAAAs2VlZWn8+PFKTU3VY4895pleVFQkl8ul1NRUz7RevXqpa9euKiws1PDhw2tcn9PplNPp9DwvKyuTJLlcLrlcrnrjqWpTU1tbZP23Og0VtgjD69/m0pD3oKleMxCv3dxaSq4tJU+JXIOJL3FRaAMAgIB56aWX9P7772v37t3V5hUXFysqKkpxcXFe0xMSElRcXFzrOnNzc7VgwYJq07ds2aKYmJgGx1ZQUFBt2uKhDV48ZDw6xN2sr7dx48Zmfb1vquk9DVctJdeWkqdErsGgvLy8wW0ptAEAQEB89tlnuvfee1VQUKDo6GjT1puTk6Ps7GzP87KyMiUnJystLU12u73e5V0ulwoKCjR27FhZrVaveX3nbzYtzkCzRRh6dIhbc/ZEyOm2NNvr7puf3myvVaWu9zTctJRcW0qeErkGk6ojpBqCQhsAAAREUVGRTp48qauuusozrbKyUtu3b9evfvUrbd68WRcuXFBpaanXXu2SkhIlJibWul6bzSabzVZtutVq9WngVlN7Z2XzFaTNxem2NGtegRw8+/oZCGUtJdeWkqdErsHAl5gotAEAQECMGTNGH374ode0KVOmqFevXnrwwQeVnJwsq9WqrVu3KiMjQ5J08OBBHT16VA6HIxAhAwDQIBTaAAAgINq1a6e+fft6TWvTpo06dOjgmT516lRlZ2crPj5edrtdM2fOlMPhqPVCaAAABAMKbQAAELSefvppRUREKCMjQ06nU+np6Vq2bFmgwwIAoE4U2gCaXd/5m2s8H/CTReMDEA2AYPL22297PY+OjlZeXp7y8vICExAAAI0QEegAAAAAAAAIJxTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJmoV6AAAAAAAf3V/aEOt8w49mtaMkQAAe7QBAAAAADAVhTYAAAAAACbyudDevn27JkyYoKSkJFksFq1bt85rvmEYmjt3rjp37qzWrVsrNTVVhw4d8mpz+vRpTZ48WXa7XXFxcZo6darOnTvnVyIAAAAAAAQDnwvt8+fPa8CAAcrLy6tx/uLFi7V06VKtWLFCu3btUps2bZSenq6KigpPm8mTJ2v//v0qKCjQ+vXrtX37dk2bNq3xWQAAAAAAECR8vhjauHHjNG7cuBrnGYahJUuW6JFHHtEtt9wiSfrtb3+rhIQErVu3TpMmTdJHH32kTZs2affu3RoyZIgk6ZlnntFNN92kn//850pKSvIjHQAAAAAAAsvUc7SPHDmi4uJipaameqbFxsZq2LBhKiwslCQVFhYqLi7OU2RLUmpqqiIiIrRr1y4zwwEAAAAAoNmZenuv4uJiSVJCQoLX9ISEBM+84uJiderUyTuIVq0UHx/vaXMxp9Mpp9PpeV5WViZJcrlccrlcjY63allbhFFvm5asvn6ij75CP9WPPmqYqn6gP2pnVh/RxwAAoCmExH20c3NztWDBgmrTt2zZopiYGL/X/+gQd63zNm7c6Pf6w0Vt/UQfeaOf6kcfNUxBQUGgQwh6/vZReXm5SZEAAAB8zdRCOzExUZJUUlKizp07e6aXlJRo4MCBnjYnT570Wu7LL7/U6dOnPctfLCcnR9nZ2Z7nZWVlSk5OVlpamux2e6PjdblcKigo0Jw9EXK6LTW22Tc/vdHrDxf19RN99BX6qX70UcNU9dPYsWNltVoDHU5QMquPqo6QAgAAMJOphXZKSooSExO1detWT2FdVlamXbt2afr06ZIkh8Oh0tJSFRUVafDgwZKkN998U263W8OGDatxvTabTTabrdp0q9VqyiDU6bbIWVlzoc0g92u19RN95I1+qh991DBm/Y0LZ/72Ef0beLm5ufrLX/6if/zjH2rdurWuueYaPfnkk+rZs6enTUVFhe677z699NJLcjqdSk9P17Jly6qdqgYAQLDwudA+d+6cDh8+7Hl+5MgR7d27V/Hx8eratatmzZqlxx57TD169FBKSormzJmjpKQk3XrrrZKk3r1768Ybb9Tdd9+tFStWyOVyacaMGZo0aRJXHAcAoIXZtm2bsrKydPXVV+vLL7/Uww8/rLS0NB04cEBt2rSRJM2ePVsbNmzQmjVrFBsbqxkzZmjixIl69913Axw9wkX3hzbUOu+TReObMRIA4cLnQnvPnj264YYbPM+rDunOzMzUqlWr9MADD+j8+fOaNm2aSktLNXLkSG3atEnR0dGeZV588UXNmDFDY8aMUUREhDIyMrR06VIT0gEAAKFk06ZNXs9XrVqlTp06qaioSNddd53OnDmjlStXavXq1Ro9erQkKT8/X71799bOnTs1fPjwQIQNAECdfC60R40aJcOo/SrdFotFCxcu1MKFC2ttEx8fr9WrV/v60gAAIMydOXNG0ldjBUkqKiqSy+XyunVor1691LVrVxUWFlJoAwCCUkhcdRwAAIQ/t9utWbNmacSIEerbt6+kr24LGhUVpbi4OK+237x16MX8vS1oXbePs0XWvrMh1FTdarGu25w2haa6rV5d7019twRsyLKhoqXcIrKl5CmRazDxJS4KbQAAEBSysrK0b98+vfPOO36tx6zbgtZ0+7jFQ/0KLSjVdZvTptBUt3Ks672pei9ruyVgXcuG6q0nW8otIltKnhK5BgNfbgtKoQ0AAAJuxowZWr9+vbZv364uXbp4picmJurChQsqLS312qtdUlLSZLcFrev2cX3nb/Yxs+BlizD06BB3nbc5bQr+3Mqxsf3vT66hduvJlnKLyJaSp0SuwcSX24JSaAMAgIAxDEMzZ87U2rVr9fbbbyslJcVr/uDBg2W1WrV161ZlZGRIkg4ePKijR4/K4XDUuE6zbgtaU/vabgcayuq6zWlTqOs9qOvq31/xL87G5OpPvIG8YnlLuUVkS8lTItdg4EtMFNoAACBgsrKytHr1ar3yyitq166d57zr2NhYtW7dWrGxsZo6daqys7MVHx8vu92umTNnyuFwcCE0AEDQotAGAAABs3z5cklf3dXkm/Lz83XXXXdJkp5++mnP7UCdTqfS09O1bNmyZo4UAICGo9AGAAABU9ctQ6tER0crLy9PeXl5zRARmlr9h4cDQOiLCHQAAAAAAACEEwptAAAAAABMRKENAAAAAICJOEcbAAAAQJ0CdSuz2l7XFmlo8dAmeUnAFOzRBgAAAADAROzRBgAAABAQXIUe4Yo92gAAAAAAmIg92gAAAEAt2OMa3PrO3yxnpaXa9KY6ZxxoKPZoAwAAAABgIvZoAwAAAE2grr3h4bbHNZz2/Lek9w1Nhz3aAAAAAACYiD3aAAAAQDML1H2p0bTYG44q7NEGAAAAAMBE7NEGAAAAEFY4YgCBRqENAAAABBkOQQZCG4eOAwAAAABgIvZoAwAAAEAD+HMbM45SaFnYow0AAAAAgInYow0AAACEkLr2jB56NK0ZIwld/uyZBhqCPdoAAAAAAJiIPdoAEKT6zt8sZ6Wl2nTO4wIA1Kbv/M1aPLRxvyHs5QXMwx5tAAAAAABMRKENAAAAAICJOHQcABC0ajuM0RZpaPHQZg4GAACggSi0AQAAACCA6jo/no3LoYlDxwEAAAAAMBGFNgAAAAAAJgpooZ2Xl6fu3bsrOjpaw4YN03vvvRfIcAAAQJBizAAACCUBO0f7j3/8o7Kzs7VixQoNGzZMS5YsUXp6ug4ePKhOnToFKiwAABBkGDMA5uFe2QhX9X2267qHfFMIWKH91FNP6e6779aUKVMkSStWrNCGDRv0m9/8Rg899FCgwgIAAEGGMQMASH3nb5az0lJten0FZF0FaF3L+lO4NvY1qzQ212ASkEL7woULKioqUk5OjmdaRESEUlNTVVhYWK290+mU0+n0PD9z5owk6fTp03K5XI2Ow+Vyqby8XK1cEap0V38jJenUqVONXn+4qK+f6KOv0E/1o48ahn76Wqsvz9c83W2ovNytU6dOyWq1Nnr9Z8+elSQZhtHodaBp+TpmkPwfN1R9B2v6fNX2mQxFVd+jusZB4YJcw09LyVOqP9f6xgV1/d2qa9n6/t41dtm6lvNnDORPvA3l07jBCIBjx44ZkowdO3Z4Tb///vuNoUOHVms/b948QxIPHjx48ODRJI/PPvusuX4C4SNfxwyGwbiBBw8ePHg07aMh44aQuI92Tk6OsrOzPc/dbrdOnz6tDh06yGJp/BassrIyJScn67PPPpPdbjcj1LBEPzUM/VQ/+qhh6Kf6mdVHhmHo7NmzSkpKMjE6BJq/44aW8h1sKXlK5BqOWkqeErkGE1/GDQEptC+55BJFRkaqpKTEa3pJSYkSExOrtbfZbLLZbF7T4uLiTIvHbrcH5RsZbOinhqGf6kcfNQz9VD8z+ig2NtakaNAUfB0zSOaNG1rKd7Cl5CmRazhqKXlK5BosGjpuCMjtvaKiojR48GBt3brVM83tdmvr1q1yOByBCAkAAAQhxgwAgFAUsEPHs7OzlZmZqSFDhmjo0KFasmSJzp8/77miKAAAgMSYAQAQegJWaH/3u9/Vf/7zH82dO1fFxcUaOHCgNm3apISEhGaLwWazad68edUOL4M3+qlh6Kf60UcNQz/Vjz5qWZp7zNBSPl8tJU+JXMNRS8lTItdQZTEM7mkCAAAAAIBZAnKONgAAAAAA4YpCGwAAAAAAE1FoAwAAAABgIgptAAAAAABM1KIL7by8PHXv3l3R0dEaNmyY3nvvvUCHFFS2b9+uCRMmKCkpSRaLRevWrQt0SEEnNzdXV199tdq1a6dOnTrp1ltv1cGDBwMdVtBZvny5+vfvL7vdLrvdLofDoddffz3QYQW1RYsWyWKxaNasWYEOJajMnz9fFovF69GrV69Ah4UwEuxjg/p+mw3D0Ny5c9W5c2e1bt1aqampOnTokFeb06dPa/LkybLb7YqLi9PUqVN17tw5rzZ///vfde211yo6OlrJyclavHhxtVjWrFmjXr16KTo6Wv369dPGjRtNy7Mhv68VFRXKyspShw4d1LZtW2VkZKikpMSrzdGjRzV+/HjFxMSoU6dOuv/++/Xll196tXn77bd11VVXyWaz6fLLL9eqVauqxdOUn4v6fiPDJc+L1fQ7Fy651vdbFS55Vjl27JjuuOMOdejQQa1bt1a/fv20Z88ez/xw+bvkM6OFeumll4yoqCjjN7/5jbF//37j7rvvNuLi4oySkpJAhxY0Nm7caPzf//2f8Ze//MWQZKxduzbQIQWd9PR0Iz8/39i3b5+xd+9e46abbjK6du1qnDt3LtChBZVXX33V2LBhg/HPf/7TOHjwoPHwww8bVqvV2LdvX6BDC0rvvfee0b17d6N///7GvffeG+hwgsq8efOMK6+80jhx4oTn8Z///CfQYSFMhMLYoL7f5kWLFhmxsbHGunXrjL/97W/Gt7/9bSMlJcX44osvPG1uvPFGY8CAAcbOnTuNv/71r8bll19ufO973/PMP3PmjJGQkGBMnjzZ2Ldvn/GHP/zBaN26tfHss8962rz77rtGZGSksXjxYuPAgQPGI488YlitVuPDDz80Jc+G/L7++Mc/NpKTk42tW7cae/bsMYYPH25cc801nvlffvml0bdvXyM1NdX44IMPjI0bNxqXXHKJkZOT42nz8ccfGzExMUZ2drZx4MAB45lnnjEiIyONTZs2edo09eeivt/IcMnzm2r7nQuXXOv7rQqXPA3DME6fPm1069bNuOuuu4xdu3YZH3/8sbF582bj8OHDnjbh8nfJVy220B46dKiRlZXleV5ZWWkkJSUZubm5AYwqeFFoN8zJkycNSca2bdsCHUrQa9++vfHrX/860GEEnbNnzxo9evQwCgoKjOuvv55C+yLz5s0zBgwYEOgwEKZCbWxw8W+z2+02EhMTjZ/97GeeaaWlpYbNZjP+8Ic/GIZhGAcOHDAkGbt37/a0ef311w2LxWIcO3bMMAzDWLZsmdG+fXvD6XR62jz44INGz549Pc9vv/12Y/z48V7xDBs2zPjRj35kao5VLv59LS0tNaxWq7FmzRpPm48++siQZBQWFhqG8dVGiYiICKO4uNjTZvny5Ybdbvfk9sADDxhXXnml12t997vfNdLT0z3PA/G5qPqNDMc8a/udC6dc6/qtCqc8DeOrvw0jR46sdX44/12qT4s8dPzChQsqKipSamqqZ1pERIRSU1NVWFgYwMgQ6s6cOSNJio+PD3AkwauyslIvvfSSzp8/L4fDEehwgk5WVpbGjx/v9fcJ3g4dOqSkpCR961vf0uTJk3X06NFAh4QwEA5jgyNHjqi4uNgrh9jYWA0bNsyTQ2FhoeLi4jRkyBBPm9TUVEVERGjXrl2eNtddd52ioqI8bdLT03Xw4EF9/vnnnjYX/51KT09vsr66+Pe1qKhILpfLK4ZevXqpa9euXrn269dPCQkJXjGWlZVp//79DcqjuT8XF/9GhmOetf3OhVuutf1WhVuer776qoYMGaLvfOc76tSpkwYNGqTnn3/eMz+c/y7Vp0UW2v/9739VWVnp9eGVpISEBBUXFwcoKoQ6t9utWbNmacSIEerbt2+gwwk6H374odq2bSubzaYf//jHWrt2rfr06RPosILKSy+9pPfff1+5ubmBDiVoDRs2TKtWrdKmTZu0fPlyHTlyRNdee63Onj0b6NAQ4sJhbFAVZ105FBcXq1OnTl7zW7Vqpfj4eK82Na3jm69RW5um6Kuafl+Li4sVFRWluLi4WmPwJ4+ysjJ98cUXzfa5qO03MtzyrOt3Lpxyreu3KpzylKSPP/5Yy5cvV48ePbR582ZNnz5d99xzj1544QWveMPt71JDtArIqwJhKCsrS/v27dM777wT6FCCUs+ePbV3716dOXNGL7/8sjIzM7Vt2zaK7f/vs88+07333quCggJFR0cHOpygNW7cOM//+/fvr2HDhqlbt27605/+pKlTpwYwMgBNpSX8vtb2GxlOWtLvXF2/Va1btw5gZOZzu90aMmSInnjiCUnSoEGDtG/fPq1YsUKZmZkBji6wWuQe7UsuuUSRkZHVru5XUlKixMTEAEWFUDZjxgytX79eb731lrp06RLocIJSVFSULr/8cg0ePFi5ubkaMGCAfvnLXwY6rKBRVFSkkydP6qqrrlKrVq3UqlUrbdu2TUuXLlWrVq1UWVkZ6BCDUlxcnK644godPnw40KEgxIXD2KAqzrpySExM1MmTJ73mf/nllzp9+rRXm5rW8c3XqK2N2X1V2+9rYmKiLly4oNLS0lpj8CcPu92u1q1bN9vnorbfyHDKs77fuYSEhLDJ9WLf/K0Kp/dUkjp37lxtp0nv3r09h8qH49+lhmqRhXZUVJQGDx6srVu3eqa53W5t3bqVc0bhE8MwNGPGDK1du1ZvvvmmUlJSAh1SyHC73XI6nYEOI2iMGTNGH374ofbu3et5DBkyRJMnT9bevXsVGRkZ6BCD0rlz5/Svf/1LnTt3DnQoCHHhMDZISUlRYmKiVw5lZWXatWuXJweHw6HS0lIVFRV52rz55ptyu90aNmyYp8327dvlcrk8bQoKCtSzZ8//1979hMLzx3EcH8VsNvlTq01bu6VIcSBSG7eVcpLT5iBxEK6bcpGT2oNc5OLCgdrcJCXKrqIotf5lWw6Li1JKKQ7K63v4Rd9la+k3fru/8XzUnubT7Ps9O8173k07b6OiouJ9zd/f87bGqmOVrb42NzcbRUVFaTEkk0nj5uYmLdfT09O0G/itrS2jtLT0vTHIlkeuzou3GmmnPLPVuZaWFtvk+tHftcpOv6lhGEZbW9un0XsXFxeGz+czDMNe16Vvy8kr2PJAJBKRw+HQ4uKizs/PNTQ0pPLy8rS3+/12j4+PisfjisfjMgxDMzMzisfjur6+znVoeWNkZERlZWWKxWJpIxyenp5yHVpeGR8f187OjlKplE5OTjQ+Pq6CggJtbm7mOrS8xlvHPwuFQorFYkqlUtrb21NHR4dcLpfu7u5yHRps4P9wb5CtNofDYZWXl2t1dVUnJyfq7u7OOEanqalJBwcH2t3dVU1NTdoYnYeHB7ndbvX19ens7EyRSEROp/PTGJ3CwkJNT08rkUhocnLS0jE6X6mvw8PD8nq92t7e1uHhofx+v/x+//v2txFJnZ2dOjo60sbGhiorKzOOSBobG1MikdDc3FzGEUk/eV5kq5F2yTOTj3XOLrlmq1V2yVP6Z1RbYWGhpqamdHl5qeXlZTmdTi0tLb2vsct16bt+baMtSbOzs/J6vTJNU62trdrf3891SHklGo3KMIxPn/7+/lyHljcyHR/DMLSwsJDr0PLK4OCgfD6fTNNUZWWlAoEATfYX0Gh/FgwGVVVVJdM05fF4FAwG02Z1Av9Wvt8bZKvNr6+vmpiYkNvtlsPhUCAQUDKZTNvH/f29ent7VVJSotLSUg0MDOjx8TFtzfHxsdrb2+VwOOTxeBQOhz/FsrKyotraWpmmqfr6eq2vr1uW51fq6/Pzs0ZHR1VRUSGn06menh7d3t6m7efq6kpdXV0qLi6Wy+VSKBTSy8tL2ppoNKrGxkaZpqnq6uqMNfwnz4tsNdIueWbysc7ZJddstcoueb5ZW1tTQ0ODHA6H6urqND8/n7bdLtel7yqQpP/u+TkAAAAAAPb2K/+jDQAAAADAT6HRBgAAAADAQjTaAAAAAABYiEYbAAAAAAAL0WgDAAAAAGAhGm0AAAAAACxEow0AAAAAgIVotAEAAAAAsBCNNgAAAAAAFqLRBgAAAADAQjTaAAAAAABYiEYbAAAAAAAL/QFOL05pke9GeQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check what features correlate with other features\n",
        "df_insurance.corr(numeric_only=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "xc314zP27wwi",
        "outputId": "a178acb6-5b0f-41f6-daf3-ad625d7a8efb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age       bmi  children   charges\n",
              "age       1.000000  0.109272  0.042469  0.299008\n",
              "bmi       0.109272  1.000000  0.012759  0.198341\n",
              "children  0.042469  0.012759  1.000000  0.067998\n",
              "charges   0.299008  0.198341  0.067998  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-575e3719-1d3e-44fa-b8f0-f9d2d5b8b8d8\" 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>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.109272</td>\n",
              "      <td>0.042469</td>\n",
              "      <td>0.299008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>0.109272</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.012759</td>\n",
              "      <td>0.198341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>0.042469</td>\n",
              "      <td>0.012759</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.067998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charges</th>\n",
              "      <td>0.299008</td>\n",
              "      <td>0.198341</td>\n",
              "      <td>0.067998</td>\n",
              "      <td>1.000000</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-575e3719-1d3e-44fa-b8f0-f9d2d5b8b8d8')\"\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-575e3719-1d3e-44fa-b8f0-f9d2d5b8b8d8 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-575e3719-1d3e-44fa-b8f0-f9d2d5b8b8d8');\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-2f1eaff5-10a8-4e76-9fd5-1372a98837e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2f1eaff5-10a8-4e76-9fd5-1372a98837e8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2f1eaff5-10a8-4e76-9fd5-1372a98837e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_insurance\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43855126689828755,\n        \"min\": 0.04246899855884958,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.1092718815485351,\n          0.299008193330648,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45298891937204494,\n        \"min\": 0.012758900820673994,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.19834096883362912,\n          0.1092718815485351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4799933789762917,\n        \"min\": 0.012758900820673994,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.012758900820673994,\n          0.06799822684790495,\n          0.04246899855884958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.416649607535863,\n        \"min\": 0.06799822684790495,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.19834096883362912,\n          1.0,\n          0.299008193330648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rSITDM6F-TuA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}