Calories_Burnt_Prediction_ML_Project_16.ipynb

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  "metadata": {
    "colab": {
      "name": "Calories Burnt Prediction-ML Project 16",
      "provenance": [],
      "authorship_tag": "ABX9TyMyNR+EJt116gjXAVwY8M+3",
      "include_colab_link": true
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joykareko/Data-Science-Projects/blob/main/Calories_Burnt_Prediction_ML_Project_16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfNU_mcn3TUO"
      },
      "source": [
        "#importing dependencies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8SpmX2g57aN"
      },
      "source": [
        "#loading the data\n",
        "exercise_data = pd.read_csv('/content/exercise.csv')\n",
        "calories_data = pd.read_csv('/content/calories.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ACbYmJE46wc2",
        "outputId": "5a450be7-0296-4661-ddcb-70f59a33a4d1"
      },
      "source": [
        "calories_data.head(3)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14733363</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14861698</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11179863</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "    User_ID  Calories\n",
              "0  14733363     231.0\n",
              "1  14861698      66.0\n",
              "2  11179863      26.0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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    {
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        "outputId": "30ce22b2-6b19-4cf6-d904-acb0a68365b7"
      },
      "source": [
        "exercise_data.head(3)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14733363</td>\n",
              "      <td>male</td>\n",
              "      <td>68</td>\n",
              "      <td>190.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>40.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14861698</td>\n",
              "      <td>female</td>\n",
              "      <td>20</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>40.3</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11179863</td>\n",
              "      <td>male</td>\n",
              "      <td>69</td>\n",
              "      <td>179.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>38.7</td>\n",
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            "text/plain": [
              "    User_ID  Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp\n",
              "0  14733363    male   68   190.0    94.0      29.0       105.0       40.8\n",
              "1  14861698  female   20   166.0    60.0      14.0        94.0       40.3\n",
              "2  11179863    male   69   179.0    79.0       5.0        88.0       38.7"
            ]
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          "metadata": {},
          "execution_count": 5
        }
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        "id": "UQrdu-9_7FUU",
        "outputId": "201cb5a2-4ffb-4ae5-887c-2f1f5b8ba97f"
      },
      "source": [
        "#combining the two dataframes\n",
        "calories_data = pd.concat([exercise_data,calories_data['Calories']],axis = 1)\n",
        "calories_data.head(3)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14733363</td>\n",
              "      <td>male</td>\n",
              "      <td>68</td>\n",
              "      <td>190.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>40.8</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14861698</td>\n",
              "      <td>female</td>\n",
              "      <td>20</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>40.3</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11179863</td>\n",
              "      <td>male</td>\n",
              "      <td>69</td>\n",
              "      <td>179.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "    User_ID  Gender  Age  Height  ...  Duration  Heart_Rate  Body_Temp  Calories\n",
              "0  14733363    male   68   190.0  ...      29.0       105.0       40.8     231.0\n",
              "1  14861698  female   20   166.0  ...      14.0        94.0       40.3      66.0\n",
              "2  11179863    male   69   179.0  ...       5.0        88.0       38.7      26.0\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "SI_C0RJ78V6s",
        "outputId": "6ecface1-c728-4df7-b568-f29dcb2fa46b"
      },
      "source": [
        "#missing values?\n",
        "calories_data.isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID       0\n",
              "Gender        0\n",
              "Age           0\n",
              "Height        0\n",
              "Weight        0\n",
              "Duration      0\n",
              "Heart_Rate    0\n",
              "Body_Temp     0\n",
              "Calories      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QI7OfKMh8dTa",
        "outputId": "4d9a5132-edd3-4326-fe07-2ce06fe2d7a4"
      },
      "source": [
        "#type of variables\n",
        "calories_data.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15000 entries, 0 to 14999\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   User_ID     15000 non-null  int64  \n",
            " 1   Gender      15000 non-null  object \n",
            " 2   Age         15000 non-null  int64  \n",
            " 3   Height      15000 non-null  float64\n",
            " 4   Weight      15000 non-null  float64\n",
            " 5   Duration    15000 non-null  float64\n",
            " 6   Heart_Rate  15000 non-null  float64\n",
            " 7   Body_Temp   15000 non-null  float64\n",
            " 8   Calories    15000 non-null  float64\n",
            "dtypes: float64(6), int64(2), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "KxXufJPh8rLz",
        "outputId": "6886a725-7c21-4a31-aad3-7efd7c2ea3e2"
      },
      "source": [
        "#statistics\n",
        "calories_data.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.500000e+04</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.497736e+07</td>\n",
              "      <td>42.789800</td>\n",
              "      <td>174.465133</td>\n",
              "      <td>74.966867</td>\n",
              "      <td>15.530600</td>\n",
              "      <td>95.518533</td>\n",
              "      <td>40.025453</td>\n",
              "      <td>89.539533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.872851e+06</td>\n",
              "      <td>16.980264</td>\n",
              "      <td>14.258114</td>\n",
              "      <td>15.035657</td>\n",
              "      <td>8.319203</td>\n",
              "      <td>9.583328</td>\n",
              "      <td>0.779230</td>\n",
              "      <td>62.456978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000116e+07</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>37.100000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.247419e+07</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>39.600000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.499728e+07</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>40.200000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.744928e+07</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>40.600000</td>\n",
              "      <td>138.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.999965e+07</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>314.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            User_ID           Age  ...     Body_Temp      Calories\n",
              "count  1.500000e+04  15000.000000  ...  15000.000000  15000.000000\n",
              "mean   1.497736e+07     42.789800  ...     40.025453     89.539533\n",
              "std    2.872851e+06     16.980264  ...      0.779230     62.456978\n",
              "min    1.000116e+07     20.000000  ...     37.100000      1.000000\n",
              "25%    1.247419e+07     28.000000  ...     39.600000     35.000000\n",
              "50%    1.499728e+07     39.000000  ...     40.200000     79.000000\n",
              "75%    1.744928e+07     56.000000  ...     40.600000    138.000000\n",
              "max    1.999965e+07     79.000000  ...     41.500000    314.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-gK8Nt9865t",
        "outputId": "c683459b-9100-43fe-fc5b-c8b10c527b3c"
      },
      "source": [
        "#rows and columns\n",
        "calories_data.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCZXWw4R9R6D"
      },
      "source": [
        "#data visualization\n",
        "sns.set()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
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        "id": "KWAbRogC-0yR",
        "outputId": "781c7494-36eb-4a18-dece-2f191a1328d0"
      },
      "source": [
        "#plotting\n",
        "sns.countplot(calories_data['Gender'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f96048ff750>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "G7-1UK4u_Fpp",
        "outputId": "84d38fe6-bdb7-4c5d-bbca-15c27169b966"
      },
      "source": [
        "#creating a distribution plot on 'age' column\n",
        "sns.distplot(calories_data['Age'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f960471a050>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "UZ_8wbUOCXqe",
        "outputId": "a983c871-03f9-4d55-c2fe-cf54bbfea2c5"
      },
      "source": [
        "#creating a distribution plot on 'weight' column\n",
        "sns.distplot(calories_data['Weight'])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f86ee190>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uxmg9u/ZXc7Zu+L6pieElZPeQPPjgg7z00ksUFhby0ksvsX79egBWrVrFxx9/DEBRURHZ2dksXryYW2+9lbvvvpucnBwA1q9fT2dnJ2vXrqWoqIiioiJOnDgBwDe/+U1aWlq44YYb+Na3vsVDDz2EyRR5zVwjgaIoNDR1kpc5vJsVAskUa2DJ3FxSEqPZ/VEtpRXn1A5JiEGF7KPj2LFj+4xbOe+5557r/V6n0/Umnc/605/+dNFzG41Gnn766aEHKYKupa0Ld7eX0dYEtUOJKNFROm6YncOeYhv7S+tJMEbx1evHo9WGzwBUIS4kd7+LkKpv6ukTyJPkctnOD/CcmJvEmwerefpPR1Qf5CnExUhyESHVcK6DKIOWtM8syiX8o9VomHtVOrdeN46jZ5z89H8OUt/UoXZYQvQjyUWEVH1TB2lJsWE1n1gkWjA9k/tWTKfJ5eaR/z7AiUrphxHhRZKLCBl3l5eWti4sSXLVMlQarYZcawL3fXUmcbF6fv6Hj9h7xKZ2WEL0kuQiQsbR0tPfkpp0eXNmif7c3V72l9o5a2vhizN7xr+88uZJDp5oQFEiZ7yWGL4ia6CBiGiNzT3JJeUyJ2QUlxZl0HHd1VmcsbXw3sd1aLUaZo5P7d2v0Wpo+0zHv0xmKYJNkosIGUdzJwlGQ+9cWiJwtJ+spNlwroOPTzswRuuYmJsM9FzlFJc19Ckvk1mKYJPPLiJkGps7SUmUq5Zg0Wg0zJ2cTrYljn2l9dQ52tUOSYxgklxESLR3euhwe0hNlM78YNJqNMyfbiUhLordxbUyDkaoRpKLCInG5p6xGKly5RJ0UXodC6dn0uXx8d7ROungF6qQ5CJCwtHciUYDyQnRaocyIiTFR1MwwUJNQxv7SvovICZEsElyESHR2NxJkikavU6ecqEyaVQSacmxbHv3LJ1d0jwmQkte6SLoFEXB0dIpTWIhptFo+NxV6XR2eTl4omHwA4QIIEkuIuha27vp6vZJclFBUnw0C2dmcrqmpXcQqxChIMlFBJ3j/OBJSS6quH5WDtEGHYdk9L4IIUkuIugamzvRaTUkmaQzXw2x0XqmjU3B5mjH9snYl/Oj9i/88vhUDlQMKzJEVwSdo6UTc0K0LGylogm5iRw766T4lANrilFG7YugkysXEVRen4KzpVMGT6pMp9UyZbSZhqYO7E5Z/0UEnyQXEVT159rxeBXMMr5FdeOyE4mN1nHktEPtUMQIIMlFBFVNQxsAZpkJWXV6nZb8PDN1znZqGlxqhyOGOUkuIqiq611otRoS46LUDkUA47MT0es07CmuVTsUMcxJchFBVd3gItkknfnhItqgY0xmAoeON8ikliKoJLmIoFEUhep6l8wnFmYmjUrG61M4Vd2sdihiGJPkIoLmXKubtk4P5nhJLuEkyRTNmMwETtU0y6BKETSSXETQVNb3dBpLZ374mTs5g9b2brktWQSNJBcRNJX2VjRAsly5hJ1p41Iw6LWcrG5SOxQxTElyEUFTZXdhSY7FoJenWbiJMugYbU2g0u6iW+Z9EUEgr3oRNJX1rWRZTGqHIS5iTGY8Xp9CVb2MeRGBJ8lFBEV7p4eGpk6yLXFqhyIuwpIUizFGz1lbi9qhiGFIkosIiqr6VgCy0+TKJVxpNBryMuKpbWzD3e1VOxwxzPidXHbt2oXHI4OuhH8q7T1NLdnSLBbW8qzx+JRP/15CBIrfyeXpp59m/vz5PPTQQxQXFwczJhGhPD561wY5Y2sh3mjAZJRpX8JZSkIMpliDNI2JgPM7ubz22mu88MILREdHc88991BYWMimTZuorq726/jy8nJWrFhBYWEhK1as4OzZs/3KeL1e1q9fz6JFi7jhhhvYvHlz7769e/fy5S9/mSlTprBhw4Y+xz3zzDPMmzePoqIiioqKWL9+vb/VEgHk7vawv9TO/lI7ZVVNxBuj8PjkTqRwptFoyLPGU+dsp7W9S+1wxDByWSsDTZo0iUmTJvGjH/2I999/n8cee4xnnnmGq6++mhUrVrB06VK02oHz1bp161i5ciVFRUVs3bqVtWvX8uKLL/Yp8/rrr1NZWcnOnTtpampi2bJlzJs3j+zsbHJycnj00UfZsWMHXV39XwTLli3j/vvvv5zqiCDx+hSaXW6yUqUzPxKMtsZz9IyTj042smROrtrhiGHisjv0Kysr2bhxIw8++CBut5vVq1ezfPlyXn75ZVavXj3gMQ6Hg5KSEpYuXQrA0qVLKSkpwel09im3fft2li9fjlarxWw2s2jRInbs2AHAqFGjyM/PR6+XlfLCXbPLjU9B5hSLEEmmaBLjojj0mZUphRgKv9+pX375ZbZu3UpFRQU33ngjjz/+ODNmzOjdX1hYyOc///kBj7XZbKSnp6PT6QDQ6XSkpaVhs9kwm819ymVmZvb+bLVaqaur8yu+N954g71792KxWLjnnnuYOXOmv1UTAeZscQM97fki/J1vGjtyykGzy02iST4UiKHzO7ns3r2bb3zjG1x//fVERfXvpI2NjeWZZ54JaHD++spXvsK3v/1tDAYD7777Lt/97nfZvn07ycnJfp8jJSXy7mqyWOLVDqEPxdlOvCkGV6cDvU6LNS0eg0FPvKl/khlou9EYjcVs7P3ZYonvPac/x/u7LVyP/2z9gSHX39+y+aNTKD7l4GSdixvnpfY7x+UKt+dmoEn9Bud3cpkzZw433nhjv+3PP/883/jGNwCYP3/+gMdarVbsdjterxedTofX66W+vh6r1dqvXG1tLdOmTQP6X8lcjMVi6f3+mmuuwWq1cvLkSebMmeNv9XA4XPh8kTNDrMUST0NDq9ph9NHu9tDq6sTuaCM5Poq2Njfd3T3bPmug7e3tbhq8PeMtztfv/Dn9Od7fbWofH2+KGbT+vduGWH9/yxq0kJoYw+6DVcwal9LvHJcjHJ+bgST166HVai75odzvPpeNGzcOuP3ZZ58d9NiUlBTy8/PZtm0bANu2bSM/P79PkxjAkiVL2Lx5Mz6fD6fTya5duygsLBz0/Ha7vff70tJSampqGD169KDHicBTFAVnq1tmQo4wGo2G6eNSKa04R3tnt9rhiGFg0CuX999/H+i5TfiDDz7os/5DdXU1cXH+3RH04IMPsmbNGjZt2kRCQkLv7cSrVq1i9erVTJ06laKiIoqLi1m8eDEAd999Nzk5OQAcOHCA73//+7hcLhRF4Y033uDRRx9lwYIFPPHEExw7dgytVovBYODxxx/vczUjQsfV0U23xyczIUeg6eNSefNgNcWnHcybnKF2OCLCDZpcfvzjHwPQ1dXFAw880Ltdo9FgsVj4yU9+4tcDjR07ts+4lfOee+653u91Ot1Fx6jMmjWL3bt3D7jvs+NehHrOd+ab5U6xiDPKGk+iqeeuMUkuYqgGTS5vvfUWAD/60Y94/PHHgx6QiGzOVjcaTc/trSKyaDUaZo638N5RG13dXqIMOrVDEhHM7z4XSSzCH+daOkmMi0KvkzlRI9HVE1Lp6vZxrNw5eGEhLuGSVy433ngjf/nLXwBYuHAhGo1mwHJ///vfAx6YiEzOFjcZKcbBC4qwNCk3GWO0nkNlDcycIP2W4spdMrk8/PDDvd///Oc/D3owIrK1tnfR7vZIZ34E0+u0TB+XwkenGvH6fOguMp2TEIO5ZHKZNWtW7/eXM2ZEjEw1DW2AdOZHuqsnWHj/mJ2yyiby88yDHyDEAPz+WPL8889TWloKwEcffcQXvvAFrrvuOg4fPhy04ERkqW7oWRMkOV7GuEQijVZDm9vD6KxEDHotH5TW0+b24JGJrcUV8Du5vPDCC2RnZwPwH//xH3z961/nO9/5Dj/96U+DFpyILDUNLowxemKi5C6jSOTu9rK/1M6RU41kmI0cPF7PvpI63N2ySKC4fH4nl9bWVuLj43G5XJw4cYLbb7+d5cuXU15eHsz4RASprm/DLP0tw0Juuol2twdHc//pZITwh99zi1mtVg4dOsSpU6eYNWsWOp0Ol8vVO9OxGNnc3V7s59qZOmZo81KJ8JBtMaHRQIUsfyyukN/J5Uc/+hGrV68mKiqKp59+GoC3336bqVOnBi04ETmqG1woinTmDxfRUToyzEaq7K19pnwSwl9+J5eFCxeyd+/ePtuWLFnCkiVLAh6UiDyVdT2zqMqElcNHTrqJfSX11DnbGZeZqHY4IsJc1rKOra2tlJeX09bW1mf7vHnzAhqUiDwV9laMMXriYmSl0OEiN60nuRSfapTkIi6b3+8Er776Kg899BBGo5GYmE8/nWo0Gt58882gBCciR0Wdi9w000VncRCRxxhjIDUxhiOnHPzjtWPVDkdEGL+Ty5NPPslTTz3FwoULgxmPiEAer4/qBhdfvDpb7VBEgOWmmzhU1khjcwepibFqhyMiiN+3Inu93ouuNClGtpqGNrw+hZy0yFsqWlxabnrPcrcflvQMqDz/JQMrxWD8Ti6rVq3i2WefxeeTZ5Xoq8Le05mfky7JZbhJiIsiI8XI3iO17C+1937JwEoxGL+bxV544QUaGxv5r//6L5KSkvrsk1mRR7YKeyux0TpSEmM4a2u54vOcn34EQHG20+724JO7YFU3dWwKu/ZV0dnlISZKbtgQ/vH7mSKzIouLqaxrJTctHu0QO/Pd3V6KyxoAiDfF0OrqZLpM+666KWNS+Nu+KqrqXYzPThr8ACG4jOQisyKLgXh9PqrqXXxhZpbaoYggybLEERejp9IuyUX4z+8+l66uLp588kmuv/56CgoKANi7dy8vvfRS0IIT4c/maKfL42PUJx2/YvjRaDTkpsdja2yny+NVOxwRIfxOLj/96U8pKyvjF7/4Re9YhvHjx/P73/8+aMGJ8Ffxycj83AxJLsNZbroJn6L0rtkjxGD8bhbbtWsXO3fuxGg0ov1kdbr09HTsdnvQghPhr8LeSpRBi9VspKNbPtUOV5bkWGKidFTaXYy2JqgdjogAfl+5GAwGvN6+bx5Op7PfnWNiZOntzNfKyPzhTKvRkJNmoqbBhdcrwxHE4PxOLkuWLOH++++nqqoKgPr6eh566CFuuummoAUnwptPUaiod0l/ywiRm27C41WwOdrVDkVEAL+Ty7333ktOTg633HILLS0tFBYWYrFYuPvuu4MZnwhj9ec6cHd5yc2QwZMjQUZKHAa9trefTYhL8bvPpbKyktGjR/Otb30Lr9fLokWLmDhxYjBjE2GuvLZnwGRehrTBjwQ6bU/TWFW9C480jYlBDJpcFEXhgQceYMuWLWRkZJCWlobdbmfjxo0UFRXx05/+VGbCHWE8PnB3eyirbiLKoCUpPpo2GU0/ZBfOUHBeuP1OR2XEc6a2hZNVTcyamKZ2OCKMDZpcXnnlFfbt28crr7zCtGnTercfOXKE++67jz/84Q989atfDWqQIry4uz3sL7VzrNxJsimagyfqAWQ0/RBdOEPBeeH2O81MMaLXaTh8slGSi7ikQftctm7dyk9+8pM+iQVg2rRpPPDAA2zdujVowYnw5fX5cLa4SU2SlSdHEp1OS3aaiSOnGvHKJLbiEgZNLqdPn2b27NkD7ps9ezanT58OeFAi/J1rceNTFFnjYwQalR5PW6eHssomtUMRYWzQ5OL1ejGZBr4byGQyyRT8I1RDcycAqYly5TLSZFniiNJreb/ELmu8iIsatM/F4/HwwQcfoCgD9yx+dmClGBkczZ3ERuswxsgU7CONXqclP8/MgeP15Fk/nQ17dn46+mh5Pogegz4TUlJSeOCBBy6632w2+/VA5eXlrFmzhqamJpKSktiwYQN5eXl9yni9Xh555BH27NmDRqPhrrvuYvny5UDPJJlPPPEEZWVl3H777dx///1+HSeCo6GpZ9lbuVNwZJo6LoXiU400nOsg3WxUOxwRhgZNLm+99VZAHmjdunWsXLmSoqIitm7dytq1a3nxxRf7lHn99deprKxk586dNDU1sWzZMubNm0d2djY5OTk8+uij7Nixg66uLr+PE4HX1tlNa3s347IS1Q5FqCQ/LxmdVkOFvVWSixiQ3yP0h8LhcFBSUsLSpUsBWLp0KSUlJTidzj7ltm/fzvLly9FqtZjNZhYtWsSOHTsAGDVqFPn5+ej1/fPhpZvoPswAABtpSURBVI4TgVf5yQhtuVNs5IqJ0pOZGkdlneuiTeZiZAtJA6nNZiM9PR2dTgeATqcjLS0Nm83Wp1nNZrORmZnZ+7PVaqWurs6v81/JcRdKSYm8KUwsFnXm9Kp9rwKAvMwkogy63u0Gg554U9+EM9A2f8vGm2KGdPxQH384HB/Mx5qYZ2bXvkrau3xkpMRhNEZj+eQqRq3nZqhI/QYnvW+fcDhc+MJtOPQlWCzxNDSoM8fTyapzJJqicLu7cbu7e7d3d3todXX2KTvQNn/Knl/m+EqPH+rjB/P4eFNMyB5/qLFeqmxqfBRaDZSWO4iL1tHe7qbB61X1uRkKUr8eWq3mkh/KQ9IsZrVasdvtvXeWeb1e6uvrsVqt/crV1tb2/myz2cjIyPDr/FdynLh8iqJQUdcqtyALogw6rKlxVNS1StOY6CckySUlJYX8/Hy2bdsGwLZt28jPz+93p9mSJUvYvHkzPp8Pp9PJrl27KCwsHPT8V3qcuHyNzZ24Orpl8KQAPh1Q6Whxqx2KCDMhaxZ78MEHWbNmDZs2bSIhIYENGzYAsGrVKlavXs3UqVMpKiqiuLiYxYsXA3D33XeTk5MDwIEDB/j+97+Py9XTgfjGG2/w6KOPsmDBgkseJwLrdE0zIJ35okdOmgmNBpmGX/QTsuQyduxYNm/e3G/7c8891/u9Tqdj/fr1Ax4/a9Ysdu/ePeC+Sx0nAutkTTMxUTqSTdFqhyLCQHSUDmuKkUq7NI2JvkLSLCaGj5NVzeRZE2RZY9ErNz2e1vZuahra1A5FhBFJLsJv7Z3d1DS4GJspi4OJT+Wm9zSNfXSqUe1QRBiR5CL8dqqmBQUYIyPzxQViovSkm40cLmuQpjHRS5KL8NvJ6iZ0Wg2jMob3ADJx+Ualx1N/roOaRmkaEz0kuQi/naxuJjc9nugLRuULAZ80jQEHjterHYoIE5JchF+6PT7KbS2Mz5YmMdFfbLSesdmJHDzRMHhhMSJIchF+qbC30u3xMT47Se1QRJiaOd5CTWMbVXYZ8yIkuQg/nazuWdJWrlzExUwfl4IGePdI7aBlxfAnyUX45WRVM+nJsSTERakdighTiaZoxmUn8m6xJBchyUX4waconKppliYxMahZE9M4a2uhztmudihCZZJcxCV5fFBua8XV0U1uRjxtbg8RtDKBCLGCiRZA7hoTklzEINzdHt48UAX0jNDfX2rH4/OpHJUIV+aEGCaNSubACUkuI50kFzGoOmc7sdF64o0GtUMREeCa6ZlU2l3Un5OmsZFMkou4JEVRqHO2Y00xotHIZJVicJ+f2rPk+AEZ8zKiSXIRl1TnaKezy0vGJ2ujC3ExGq2GNrcH6Jkp+cMSO21uDx5pRR2RJLmISyqr6hnfIslFDMbd7WV/qZ1DJ+pJS4qhqt7Frv1VuLs9aocmVCDJRVxSWXUTplgDJulvEZchz5qABjhT26x2KEIlklzERfkUhVNVzXLVIi6bMUZPRoqRM7Ut+GQa/hFJkou4qCq7i3a3h4wUSS7i8o3NSqCt08OZmha1QxEqkOQiLqq04hwg/S3iyuSkxaPXadhfalc7FKECSS7ioo5XniMtORZjjF7tUEQEMui15KbHc/hkA90er9rhiBCT5CIG5PH6OFHVxIQcmU9MXLkxmQl0uL0Un3KoHYoIMUkuYkAVda24u7ySXMSQZKQYSYiL4v1jdWqHIkJMkosYUMkn/S3jZP0WMQRajYbZk9I4ctpBs8utdjgihCS5iAEdPeNgVEY88UZZv0UMzbwpGXh9Cns/tqkdigghSS6in/ZOD6drWpgy2qx2KGIYSDcbmZSbxDsf1cqYlxFEkovop7TCiU9RmDomRe1QxDDxhZlZNDZ3cqzcqXYoIkQkuYheHh+0uT0cPtlITJSOjBSjLAwmhkyj1TBxVDKmWANvHqyWySxHCEkuope728O+kjqKTzWSlhzLobIGWRhMDJm728vhsgZGZcTz8RkH7xyulsksRwBJLqKP5rYu2jo9ZKbEqR2KGGbGZyeiKHCyWiazHAkkuYg+ahvbAMhMleQiAishLgpripGT1c14pb112JPkIvqobWwjIS5KptgXQTExN4n2Tg9HTjWqHYoIspAll/LyclasWEFhYSErVqzg7Nmz/cp4vV7Wr1/PokWLuOGGG9i8ebNf+5555hnmzZtHUVERRUVFrF+/PhRVGna6PF7szg6y5KpFBEl2mol4o4G3DlWrHYoIspDNSLhu3TpWrlxJUVERW7duZe3atbz44ot9yrz++utUVlayc+dOmpqaWLZsGfPmzSM7O/uS+wCWLVvG/fffH6rqDEunP2muyEyVWZBFcGg1GvJHJbOvtJ5TNc2My5IZIIarkFy5OBwOSkpKWLp0KQBLly6lpKQEp7PvPe/bt29n+fLlaLVazGYzixYtYseOHYPuE4FxrNyJTqshXabYF0E0NisRY7Senfsq1Q5FBFFIrlxsNhvp6enodDoAdDodaWlp2Gw2zGZzn3KZmZm9P1utVurq6gbdB/DGG2+wd+9eLBYL99xzDzNnzrysGFNSTFdUNzVZLPEBO5eiKHx8xklOejzJiZ8mF4NBT7wppk/ZgbYFo2y8KWbIjzXSj1cr1gv3DVT2CwXZ7Hj/LF6tlowIvDMxkK+9cBSI+g2LhTq+8pWv8O1vfxuDwcC7777Ld7/7XbZv305ycrLf53A4XPgi6A4WiyWehobWgJ2voq4VZ0snE3OTaHV19m7v7vb0+fli2wJdNt4UQ6urc8iPFY7Hx5tiQvb4Q431So4//7e7VNl5k9P56wcV/HHnCb66aHy/xwxngX7thRt/66fVai75oTwkzWJWqxW73Y7X27NgkNfrpb6+HqvV2q9cbW1t7882m42MjIxB91ksFgyGnrubrrnmGqxWKydPngxqnYabwycb0GggJy3yPkWKyJNkimZOfhq7j9TS1tmtdjgiCEKSXFJSUsjPz2fbtm0AbNu2jfz8/D5NYgBLlixh8+bN+Hw+nE4nu3btorCwcNB9dvuny6iWlpZSU1PD6NGjQ1G1YeNQWSNjMhOIiRoWF7MiAiyZOwp3l5e/7a9SOxQRBCF7J3nwwQdZs2YNmzZtIiEhgQ0bNgCwatUqVq9ezdSpUykqKqK4uJjFixcDcPfdd5OTkwNwyX1PPPEEx44dQ6vVYjAYePzxx7FYLKGqWsRraOqgusHFsmvHqB2KGEFy0kxcPcHC3w5Us3h2riynPcyE7K85duzYPmNTznvuued6v9fpdBcdo3KpfecTlbgyh0/2DGibNjaFs7YWlaMRI8nNn8/jUFkDbx6s4uZrpLVhOJER+oKPTjaQZYnDkhSrdihihNBoNbS5PaQmxzJljJm/7q/C1SGTWQ4nklxGOFdHNyeqmpg5PlXtUMQI4u72sr/Uzv5SO7npJto7Pew6KH0vw4kklxGu+FQjigIzx0sflVBHamIsWalxvHWwmg63XL0MF5JcRrj9x+sxJ0STlzG8B4WJ8DZ9XAqujm52fCij9ocLSS4jmKujm2PlTubkp6PRaNQOR4xgqUmxXD3Rwl/3VeJs6T+QU0QeSS4j2IET9Xh9CnPz09UORQhuviYPn6Lw5z1n1A5FBIAklxFsX4mdDLOR3PTIm1dNDD+pibEsmpXDex/XUWkfvtOrjBSSXEYoZ0snJyqbmHuVNImJ8LF03ijiYg288tYpFCVy5voT/UlyGaHePVqHAsybkqF2KEL0MsYYuOWaPEorznHwRIPa4YghkOQyAimKwrtHbEzKTSJNBk6KMPPFq7PITTPx8q4y2jvl1uRIJcllBCqraqK+qYNrploHLyxEiJwftd/Z7ePW68fT0tbF7988icendmTiSshMcSPQO8U2og068kebabtg0FoELWcjhiF3t5fisk+bwvJHJfPuxzamjk1hzqQ0FSMTV0KSywjT0tbFgeN2xmYlcuRUY5990yfIKH0RPmaOT6W2sY3/3VnGpNwkEoxRaockLoM0i40wu4tr8XgVJuUmqR2KEJek02mZP81Ku7ub/3ztWEStFCskuYwoXp+Ptw/XMDE3iURTtNrhCDEoc0IMy784jpKz52RwZYSR5DKCfFhi51yrm4UzstQORQi/fX6qlWunZ/LG+xXsKa4d/AARFiS5jBA+ReGN9yvItsQxeYx58AOECCO3LZ7A5NFm/nvHCQ6flPEvkUCSywhxuKwBm6Odm+bloZUR+SLC6HVavrtsCqMy4tn056N8dLJx8IOEqiS5jABen48te8pJNxuZLbd0iggVG63nvhXTyU038atXP+btQ9VqhyQuQZLLCPDux3XUNLbxj9eOQauVqxYRuYwxBn7wlZlMHWPmf3aW8b+7yuQusjAlyWWY6+zy8Oc9ZxiXlUjBRBnHIiJfbLSee/5xGotn57DrQDW//L9iWtq61A5LfIYkl2HuT++cocXVxS0LRtPe5aXN7ZGR+CLi+dBw8/zRfOX68RyvOMe///ZDik871A5LXECSyzB2uraZtw5WMyE3icamDvaX2tlfasfjk8maRGRzd3vYX2onyqDlxs+NQqfV8NTmYl786wma2rpoc3t6v2RuMnXI9C/DVIfbw39tKyXRFMXMCalqhyNE0CTHR/OleaOosLv4++Eaik81Mn+aleT4noHCs/PT0UfLW12oyZXLMKQoCv+94zj159r55xsnEaXXqR2SEEGl12n58hfGcl1BFh1uD2+8V8HRMw58suCYaiS5DENvvF/BvtJ6vnztGMZlyxxiYuTItpi4ZX4e2WlxHCprZMcHldid7WqHNSJJchlmdhfX8uruM3xucjo3fm6U2uEIMWTn13m58OtSN6XEROlZOCOTBdOstLR3seGlQ+zcXyVXMSEmDZHDyK4DVfzvrpNMHm3mji/ly0h8MSx8dp0XGHx5CI1Gw+jMBNLNRo5XnuMPb57kwPF6Vi6egCUplmiDHr18tA4q+fUOAx6vj5f/Vsb/7jrJzPGprP7Hqeh18qcVwhij5+s35fP5KRlU2Ft55IUD/OfWo7S0udUObdiTK5cIV2lv5YW/HOdsXSs3zMrh1uvGotNKYhHiPI1Gw7jsRDJT4zhU1sDHZ5w88t/7WbZgDPMmZ2CQS5igkOQSoRzNHfzPX0/wzke1mGL13P0PU5g+Po3Obg/w6Y39MmBSiB7GGD3zp1mZkJNEyVknL/zlOH/efYZFs7JZOCMLU6xB7RCHFUkuEcTnUzhR1cS7H9vYV1qPoigsnJHJP1w7BlOsgTZ3z8CyC8nSxUL0lZYcy5fmzaTC1sqOfZX86Z0z/Hl3ORNyEpk5wcKU0WbSzUbpsxyikCWX8vJy1qxZQ1NTE0lJSWzYsIG8vLw+ZbxeL4888gh79uxBo9Fw1113sXz58iHti3Qt7V2cqm7maLmTQyfqaWnvJtqg49qZWSyYmkFqUiyATOsixGXQaDRMHm1m8mgz1fUuPiy1c/hkI7/fdRKA6CgduWkmslLjMCfEkJIQgzkhGnNCDPGJsSpHHxlCllzWrVvHypUrKSoqYuvWraxdu5YXX3yxT5nXX3+dyspKdu7cSVNTE8uWLWPevHlkZ2df8b5IoCgKro5uzrW6aWzupKaxjdrGNirqWqn75B79KL2WaeNSmT0pjbHZiZTXuSi3tVBua+k9j1ylCOGf87c3AyQnxrDkc6O4ef5YahpaOVPbTFW9iyq7i/3H62nr9PQ7PsqgJT42inijgXjj+f8/+T7WgDFGT0y0npgoHbFRPf/HfPL/SJmZPCTJxeFwUFJSwvPPPw/A0qVLefjhh3E6nZjNn66KuH37dpYvX45Wq8VsNrNo0SJ27NjBnXfeecX7/HUlf/AOt4eDJxro9vpQfAoKCorSs+qjTwEUUJSe5OFTFLxeBbfHg7vLh7vbi7vbS0tbFy3tXXi9fS87kk3RTMxNYvGcXLJS47Ba4jB8cgeYT+mZGdbr6dtGrNdpMcZc2bZQHu9P2fP1UzvWYBwfG60Pq991oI//7HMzHP9WXp9Cabmzz7b80WYamjqIN0ZxVZ6Zq/J63puuGm2mqdVNS1sXzW1ddHl8tLR10dreRXtnN20dHhqaOzlrb8Xjx0RmBoMWg06HXqdBr9Ng0OvQaTUY9Fr0Wg06vRaDTotep0Wn1aDVAJqe/zUazSdfoEUDn2z7dB89zXk9/3p+1mrQ8Ol5enb3vN8Z9FqunmAhJqrvLB7+vB8OViYkycVms5Geno5O11MBnU5HWloaNputT3Kx2WxkZmb2/my1WqmrqxvSPn8lJ8ddfsWA7Ex1RsDnZiYOuH1MdvIVbwvl8aF8rJF+fCgfa7geP9KkpJiGfA65B08IIUTAhSS5WK1W7HY7Xq8X6OmAr6+vx2q19itXW1vb+7PNZiMjI2NI+4QQQoReSJJLSkoK+fn5bNu2DYBt27aRn5/fp0kMYMmSJWzevBmfz4fT6WTXrl0UFhYOaZ8QQojQ0yhKaGZzO336NGvWrKGlpYWEhAQ2bNjAmDFjWLVqFatXr2bq1Kl4vV4eeugh3n33XQBWrVrFihUrAK54nxBCiNALWXIRQggxckiHvhBCiICT5CKEECLgJLkIIYQIOEkuQgghAk6SS4T41a9+xcSJEykrKwPgo48+4pZbbqGwsJA77rgDh8OhcoRXxu12s27dOhYvXszNN9/Mv//7vwM9E52uWLGCwsJCVqxYwdmzZ9UN9Aq9/fbbLFu2jKKiIm655RZ27twJRG79NmzYwHXXXdfnuQiXrk8k1XWg+p07d45Vq1ZRWFjIzTffzL/8y7/gdH46dUykvBYv9rc777PvMTDEuiki7B09elT55je/qXzxi19UTpw4oXi9XmXRokXK/v37FUVRlI0bNypr1qxROcor8/DDDyuPPvqo4vP5FEVRlIaGBkVRFOX2229XtmzZoiiKomzZskW5/fbbVYvxSvl8PmXWrFnKiRMnFEVRlNLSUmXGjBmK1+uN2Prt379fqa2t7X0unnep+kRSXQeq37lz55QPPvigt8xjjz2m/Nu//ZuiKEpEvRYv9rdTlP7vMYoy9LpJcglzbrdbufXWW5WqqqreP3xxcbFy00039ZZxOBzKjBkzVIzyyrhcLqWgoEBxuVx9tjc2NioFBQWKx+NRFEVRPB6PUlBQoDgcDjXCvGI+n0+ZM2eOcuDAAUVRFGXfvn3K4sWLh0X9LnwTulR9IrWuA70Bn7djxw7ln//5nxVFUSLytfjZug30HqMoQ6+bLBYW5p566iluueWWPssHfHaiTrPZjM/n610rJ1JUVVWRlJTEr371Kz788EPi4uL413/9V2JiYvya6DTcaTQafvnLX/Ld734Xo9FIW1sb//mf/+n3RK6R4lL1URRlWNXV5/Px+9//nuuuuw4YHq/Fgd5jYOh1kz6XMHb48GGOHj3KypUr1Q4lKLxeL1VVVVx11VW8+uqr/OAHP+Cee+6hvb1d7dACwuPx8Jvf/IZNmzbx9ttv8+yzz/K9731v2NRvJHr44YcxGo3cdtttaocSEMF8j5ErlzC2f/9+Tp8+zfXXXw9AXV0d3/zmN7n99tv7TNTpdDrRarUR80npPKvVil6vZ+nSpQBMnz6d5ORkYmJieic61el0F53oNNyVlpZSX19PQUEBAAUFBcTGxhIdHT0s6nfehRPTfrY+iqIMm7pu2LCBiooKfv3rX6PV9nwu/+ykuZH2WrzYe8zPfvazIddNrlzC2F133cXevXt56623eOutt8jIyOC3v/0td955J52dnRw4cACAP/zhDyxZskTlaC+f2Wxm7ty5vXPClZeX43A4yMvL82ui03CXkZFBXV0dZ86cAXrm13M4HIwaNWpY1O+8S01M6++kteHuiSee4OjRo2zcuJGoqKje7VOmTIno1+LF3mPmz58/5LrJ3GIR5LrrruPXv/41EyZM4NChQ6xbtw63201WVhY///nPSU1NVTvEy1ZVVcUDDzxAU1MTer2e733veyxcuPCiE51Gmtdee43nnnsOjaZn1b7Vq1ezaNGiiK3fI488ws6dO2lsbCQ5OZmkpCTeeOONS9Ynkuo6UP1++ctfsnTpUvLy8oiJiQEgOzubjRs3AkTMa/Fif7sLXfgeA0OrmyQXIYQQASfNYkIIIQJOkosQQoiAk+QihBAi4CS5CCGECDhJLkIIIQJOkosQYei1117jjjvu8Kvsq6++yle/+tUgRyTE5ZHkIkQA/eY3v+HOO+/ss23x4sUDbvvsGIML3XLLLfzud78LSEy33347mzdvDsi5hPCXJBchAmjWrFkcPnwYr9cLQH19PR6Ph9LS0j7bKioqmDVrlpqhChFUklyECKCpU6f2JhOAAwcOMHfuXEaPHt1nW25uLkajkQceeID58+ezYMECnnzyyd4E9Nmmrr1791JYWEhBQQEPPvggt912W7+rkQ0bNjB79myuu+463nnnHQCefPJJDhw4wEMPPcTMmTN56KGHQvFrEEKSixCBFBUVxbRp03rnYzpw4AAFBQUUFBT02TZr1izWrFmDXq9n586dbNmyhXfffXfA5iun08nq1au57777+PDDDxk9ejSHDx/uU+bIkSOMHj2aDz74gDvvvJMf//jHKIrCvffey6xZs1i7di2HDx9m7dq1wf8lCIEkFyECbs6cOezfvx/4NJEUFBT02TZnzhzeeecdHnjgAYxGIykpKXz9618fsB9m9+7djB8/nsWLF6PX6/na177Wb36nzMxMbr31VnQ6Hf/wD/9AQ0MDjY2Nwa+sEBchU+4LEWCzZs3i5ZdfpqmpCafTSV5eHqmpqaxZs4ampiZOnjzJmDFj8Hg8zJ8/v/c4n8834FT09fX1ZGRk9P6s0Wj6/Az0STaxsbEAsm6MUJUkFyECbObMmbhcLv74xz9y9dVXA2AymUhLS+OPf/wjaWlpWK1WoqKi+OCDD9DrL/0ytFgs2O323p8VRaGuri6odRBiqKRZTIgAi4mJYcqUKbzwwgt97ggrKCjo3ZaWlsY111zDY489hsvlwufzUVlZyb59+/qdb+HChZw4cYJdu3bh8Xh4+eWXL6vJKzU1laqqqoDUTQh/SXIRIghmz56Nw+HoXYUSepKLw+Fg9uzZADz++ON0d3fzpS99idmzZ7N69WoaGhr6nctsNvPUU0/x85//nLlz53Lq1CmmTJmCwWDwK5avfe1r/PWvf2X27Nk88sgjgamgEIOQ9VyEiDA+n49rr72WX/ziF3zuc59TOxwhBiRXLkJEgD179tDS0kJXVxe//vWvAZgxY4bKUQlxcdKhL0QE+Oijj/jBD35AV1cX48aNY+PGjb1L7goRjqRZTAghRMBJs5gQQoiAk+QihBAi4CS5CCGECDhJLkIIIQJOkosQQoiAk+QihBAi4P4/Gnd1izJVrAgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "yx2tTix0ChGW",
        "outputId": "84a86167-7c17-48ad-c4fd-1c97b5d8e999"
      },
      "source": [
        "#creating a distribution plot on 'height' column\n",
        "sns.distplot(calories_data['Height'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f8598990>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "7rxYOKcqCnNd",
        "outputId": "8116c8af-2214-4eae-d53d-73da6ef9cb95"
      },
      "source": [
        "#creating a distribution plot on 'calories' column\n",
        "sns.distplot(calories_data['Calories'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f8495450>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA75GWKuC23R"
      },
      "source": [
        "#finding correlation\n",
        "correlation = calories_data.corr()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "8v0PAXo8C-wx",
        "outputId": "21848f0f-d6cc-480b-c324-4967e7943e34"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(correlation, cbar=True, square = True, fmt = '.1f', annot = True, annot_kws = {'size':8} , cmap = 'Blues')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f80fe210>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "pxWIAjbVE2Gg",
        "outputId": "a9314c6d-8cb8-49b8-91cf-1c39b5a50e56"
      },
      "source": [
        "#converting the 'Gender' column to numerical\n",
        "#categorical data encoding\n",
        "calories_data.replace({'Gender':{'female':1,'male':0}},inplace=True)\n",
        "calories_data.head(3)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>User_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14733363</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>190.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>40.8</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14861698</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>40.3</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11179863</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>179.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User_ID  Gender  Age  Height  ...  Duration  Heart_Rate  Body_Temp  Calories\n",
              "0  14733363       0   68   190.0  ...      29.0       105.0       40.8     231.0\n",
              "1  14861698       1   20   166.0  ...      14.0        94.0       40.3      66.0\n",
              "2  11179863       0   69   179.0  ...       5.0        88.0       38.7      26.0\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skNW_iShFdH1",
        "outputId": "7eb97c87-003f-4ac3-c63e-9eb39d65ed85"
      },
      "source": [
        "#separating features and target\n",
        "X = calories_data.drop(columns = ['User_ID','Calories'], axis = 1)\n",
        "Y = calories_data['Calories']\n",
        "print(X.shape,Y.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15000, 7) (15000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jq_r999RGHAp",
        "outputId": "b295d5c9-c95b-4bb5-bb1a-657961f305aa"
      },
      "source": [
        "#splitting the data into train and test\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size = 0.2 , random_state = 2)\n",
        "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(12000, 7) (3000, 7) (12000,) (3000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxlM8y15Gn5B"
      },
      "source": [
        "#model training\n",
        "model = XGBRegressor()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwpoMfqsG7Je",
        "outputId": "95c1d780-ee47-499c-9ea1-984feaff8481"
      },
      "source": [
        "#training\n",
        "model.fit(X_train,Y_train)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[11:40:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
              "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "             silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cytZBQKRHAxA"
      },
      "source": [
        "test_prediction = model.predict(X_test)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f216IutRHBXN",
        "outputId": "a5b549ba-96d7-4be0-a72e-5f29f2967aab"
      },
      "source": [
        "print(test_prediction)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[129.06204  223.79721   39.181965 ... 145.59767   22.53474   92.29064 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xztlrbW9HBpn",
        "outputId": "53bf68c7-c68c-4828-aacc-1a077949dbb5"
      },
      "source": [
        "#model comparison\n",
        "mae = metrics.mean_absolute_error(Y_test,test_prediction)\n",
        "print('Mean Absolute Error', mae)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error 2.7159012502233186\n"
          ]
        }
      ]
    }
  ]
}