Calories_Burnt_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Xi0CMM2clAj"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "6PbZXKDQZEWJ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcfuz4bXdg1I"
      },
      "source": [
        "Data Collection & Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "455T_ehLc9LM"
      },
      "outputs": [],
      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "calories = pd.read_csv('calories.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "afZUOcwbemJc",
        "outputId": "c7ed1e29-240e-4972-cc9c-80fae5d751d0"
      },
      "outputs": [
        {
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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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16180408</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17771927</td>\n",
              "      <td>35.0</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\n",
              "3  16180408      71.0\n",
              "4  17771927      35.0"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# print the first 5 rows of the dataframe\n",
        "calories.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "alQtMyM6etdl"
      },
      "outputs": [],
      "source": [
        "exercise_data = pd.read_csv('exercise.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "5lA2LmJUe3kA",
        "outputId": "5e5b99b8-55e0-436a-93bf-bd74f5a49a46"
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      "outputs": [
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              "      <th></th>\n",
              "      <th>User_ID</th>\n",
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              "      <th>Age</th>\n",
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              "      <td>14733363</td>\n",
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              "      <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",
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              "    <tr>\n",
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              "      <td>14861698</td>\n",
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              "      <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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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16180408</td>\n",
              "      <td>female</td>\n",
              "      <td>34</td>\n",
              "      <td>179.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>40.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17771927</td>\n",
              "      <td>female</td>\n",
              "      <td>27</td>\n",
              "      <td>154.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>39.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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\n",
              "3  16180408  female   34   179.0    71.0      13.0       100.0       40.5\n",
              "4  17771927  female   27   154.0    58.0      10.0        81.0       39.8"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "exercise_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iid8PMjmfqvX"
      },
      "source": [
        "Combining the two Dataframes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "kAHQrky8e6VP"
      },
      "outputs": [],
      "source": [
        "calories_data = pd.concat([exercise_data, calories['Calories']], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "RO-DGhIdgPrG",
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        {
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              "      <th>User_ID</th>\n",
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              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
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              "      <th>Calories</th>\n",
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              "      <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",
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              "    <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",
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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",
              "      <td>26.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16180408</td>\n",
              "      <td>female</td>\n",
              "      <td>34</td>\n",
              "      <td>179.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17771927</td>\n",
              "      <td>female</td>\n",
              "      <td>27</td>\n",
              "      <td>154.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>35.0</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   \n",
              "3  16180408  female   34   179.0    71.0      13.0       100.0       40.5   \n",
              "4  17771927  female   27   154.0    58.0      10.0        81.0       39.8   \n",
              "\n",
              "   Calories  \n",
              "0     231.0  \n",
              "1      66.0  \n",
              "2      26.0  \n",
              "3      71.0  \n",
              "4      35.0  "
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "calories_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kB0hwwMHgS7x",
        "outputId": "d6a38169-6d98-4710-d161-5584b000f4a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15000, 9)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking the number of rows and columns\n",
        "calories_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siYE6fU7gbi3",
        "outputId": "2612cdc7-84a8-4e3f-ab9a-94e7dc5b8f73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "# getting some informations about the data\n",
        "calories_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhKisO0pgwXd",
        "outputId": "0a9e3328-081b-40d3-b92c-4488ecfc4b5d"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking for missing values\n",
        "calories_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oY-QcmsVhSBV"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "95cmeAWZhFa4",
        "outputId": "e2f0f104-4183-4ec1-9285-79ec85461692"
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        {
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              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
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              "  </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        Height        Weight      Duration  \\\n",
              "count  1.500000e+04  15000.000000  15000.000000  15000.000000  15000.000000   \n",
              "mean   1.497736e+07     42.789800    174.465133     74.966867     15.530600   \n",
              "std    2.872851e+06     16.980264     14.258114     15.035657      8.319203   \n",
              "min    1.000116e+07     20.000000    123.000000     36.000000      1.000000   \n",
              "25%    1.247419e+07     28.000000    164.000000     63.000000      8.000000   \n",
              "50%    1.499728e+07     39.000000    175.000000     74.000000     16.000000   \n",
              "75%    1.744928e+07     56.000000    185.000000     87.000000     23.000000   \n",
              "max    1.999965e+07     79.000000    222.000000    132.000000     30.000000   \n",
              "\n",
              "         Heart_Rate     Body_Temp      Calories  \n",
              "count  15000.000000  15000.000000  15000.000000  \n",
              "mean      95.518533     40.025453     89.539533  \n",
              "std        9.583328      0.779230     62.456978  \n",
              "min       67.000000     37.100000      1.000000  \n",
              "25%       88.000000     39.600000     35.000000  \n",
              "50%       96.000000     40.200000     79.000000  \n",
              "75%      103.000000     40.600000    138.000000  \n",
              "max      128.000000     41.500000    314.000000  "
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# get some statistical measures about the data\n",
        "calories_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0t8EoEUaiHeZ"
      },
      "source": [
        "Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "xU4y4ZsIhiKE"
      },
      "outputs": [],
      "source": [
        "sns.set()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "MuzLivXSiMwi",
        "outputId": "d6ce6c29-320b-4e5c-89eb-af77aec2aef5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='count'>"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plotting the gender column in count plot\n",
        "sns.countplot(x='Gender', data=calories_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "UhcoOmdRihmk",
        "outputId": "2c80ced0-94be-439e-aed5-f73d630576b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x17d8e7250>"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# finding the distribution of \"Age\" column\n",
        "sns.displot(calories_data['Age'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "FZDTaUmfjBzk",
        "outputId": "165d6d55-6d08-4469-b502-01e288986ecf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1799f4290>"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# finding the distribution of \"Height\" column\n",
        "sns.displot(calories_data['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "JhP8EIrqjV_e",
        "outputId": "225e6daf-8ea3-4051-964d-204184eb55d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x17d9ef390>"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# finding the distribution of \"Weight\" column\n",
        "sns.displot(calories_data['Weight'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9TJtsGnjrZs"
      },
      "source": [
        "Finding the Correlation in the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6cFMyI3jzPb"
      },
      "source": [
        "1. Positive Correlation\n",
        "2. Negative Correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "2UFA5ZOujfbh"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/ly/s47_9vtj3db6s01357kxt__00000gn/T/ipykernel_24813/669282712.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation = calories_data.corr()\n"
          ]
        }
      ],
      "source": [
        "correlation = calories_data.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "dtp36oCBjyQI",
        "outputId": "38976e6d-201b-4f52-8c7a-1fc7ea5c01be"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# constructing a heatmap to understand the correlation\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Purples')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNxKhdn_lxOC"
      },
      "source": [
        "Converting the text data to numerical values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "lvlWPvd_k2hw"
      },
      "outputs": [],
      "source": [
        "calories_data.replace({\"Gender\":{'male':0,'female':1}}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "Co_h1CUZmMB0",
        "outputId": "82aa7a91-30b0-4b6e-82a4-4161199bbf4e"
      },
      "outputs": [
        {
          "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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16180408</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>179.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17771927</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>154.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User_ID  Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp  \\\n",
              "0  14733363       0   68   190.0    94.0      29.0       105.0       40.8   \n",
              "1  14861698       1   20   166.0    60.0      14.0        94.0       40.3   \n",
              "2  11179863       0   69   179.0    79.0       5.0        88.0       38.7   \n",
              "3  16180408       1   34   179.0    71.0      13.0       100.0       40.5   \n",
              "4  17771927       1   27   154.0    58.0      10.0        81.0       39.8   \n",
              "\n",
              "   Calories  \n",
              "0     231.0  \n",
              "1      66.0  \n",
              "2      26.0  \n",
              "3      71.0  \n",
              "4      35.0  "
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "calories_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDrUQynrm5OZ"
      },
      "source": [
        "Separating features and Target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "yNX7qV5dmh-o"
      },
      "outputs": [],
      "source": [
        "X = calories_data.drop(columns=['User_ID','Calories'], axis=1)\n",
        "Y = calories_data['Calories']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9_t_RaknWMS",
        "outputId": "7a5ccbbd-0302-428f-b124-1c55de8c09ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp\n",
            "0           0   68   190.0    94.0      29.0       105.0       40.8\n",
            "1           1   20   166.0    60.0      14.0        94.0       40.3\n",
            "2           0   69   179.0    79.0       5.0        88.0       38.7\n",
            "3           1   34   179.0    71.0      13.0       100.0       40.5\n",
            "4           1   27   154.0    58.0      10.0        81.0       39.8\n",
            "...       ...  ...     ...     ...       ...         ...        ...\n",
            "14995       1   20   193.0    86.0      11.0        92.0       40.4\n",
            "14996       1   27   165.0    65.0       6.0        85.0       39.2\n",
            "14997       1   43   159.0    58.0      16.0        90.0       40.1\n",
            "14998       0   78   193.0    97.0       2.0        84.0       38.3\n",
            "14999       0   63   173.0    79.0      18.0        92.0       40.5\n",
            "\n",
            "[15000 rows x 7 columns]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Up5-uH0qnXdD",
        "outputId": "47e4c08f-afea-4288-f184-4ac401781c68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0        231.0\n",
            "1         66.0\n",
            "2         26.0\n",
            "3         71.0\n",
            "4         35.0\n",
            "         ...  \n",
            "14995     45.0\n",
            "14996     23.0\n",
            "14997     75.0\n",
            "14998     11.0\n",
            "14999     98.0\n",
            "Name: Calories, Length: 15000, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzj0j0m-nfS7"
      },
      "source": [
        "Splitting the data into training data and Test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "lVpEUea_naiH"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5bBE7wRoPw9",
        "outputId": "99855aa4-40ef-484b-e264-53bf21082f03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(15000, 7) (12000, 7) (3000, 7)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TV84xw6Goeh6"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnB7kvETogu1"
      },
      "source": [
        "XGBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "9C4PUD9noX10"
      },
      "outputs": [],
      "source": [
        "# loading the model\n",
        "model = XGBRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nSNxHXUBowJ4",
        "outputId": "78067fba-1487-4df5-b2d6-80f8a0991296"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# training the model with X_train\n",
        "model.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmzboOlDpOBV"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWyeJ2B0pTjl"
      },
      "source": [
        "Prediction on Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "6fwoa12gpD1A"
      },
      "outputs": [],
      "source": [
        "test_data_prediction = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elvZfEW_pnwa",
        "outputId": "b102fdab-05be-4f66-c126-ab282fa07c5b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[127.823784 226.00154   38.66253  ... 144.3636    22.767195  89.87375 ]\n"
          ]
        }
      ],
      "source": [
        "print(test_data_prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvAOAtW3p3KU"
      },
      "source": [
        "Mean Absolute Error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "e32ffB4Opshq"
      },
      "outputs": [],
      "source": [
        "mae = metrics.mean_absolute_error(Y_test, test_data_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWJQ0yJtqell",
        "outputId": "f5539a41-2d6b-4809-8a28-dbe9d703fc08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error =  1.4807048829992613\n"
          ]
        }
      ],
      "source": [
        "print(\"Mean Absolute Error = \", mae)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yVo9E_U_qkpK"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}