Phase2-Projects/Part4/Calories Burnt Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = pd.read_csv('calories.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>14995</th>\n",
       "      <td>15644082</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>17212577</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>17271188</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>18643037</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14999</th>\n",
       "      <td>11751526</td>\n",
       "      <td>98.0</td>\n",
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      ],
      "text/plain": [
       "        User_ID  Calories\n",
       "14995  15644082      45.0\n",
       "14996  17212577      23.0\n",
       "14997  17271188      75.0\n",
       "14998  18643037      11.0\n",
       "14999  11751526      98.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "exercise_data = pd.read_csv('exercise.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
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       "      <th>Heart_Rate</th>\n",
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       "  <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",
       "    </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",
       "    </tr>\n",
       "    <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",
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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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exercise_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "calories_data = pd.concat([exercise_data, cal['Calories']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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",
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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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calories_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15000, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "calories_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": [
    "calories_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calories_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calories_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(calories_data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(calories_data['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Height', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(calories_data['Height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(calories_data['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = calories_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Blues')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "calories_data.replace({\"Gender\":{'male':0,'female':1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calories_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = calories_data.drop(columns=['User_ID','Calories'], axis=1)\n",
    "y = calories_data['Calories']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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": 28,
   "metadata": {},
   "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": "code",
   "execution_count": 29,
   "metadata": {},
   "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": 30,
   "metadata": {},
   "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": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[127.823784 226.00154   38.66253  ... 144.3636    22.767195  89.87375 ]\n"
     ]
    }
   ],
   "source": [
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4807048829992613"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae = metrics.mean_absolute_error(y_test,pred)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9988455491362879"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_test = metrics.r2_score(y_test,pred)\n",
    "r2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.53426242569831"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "mse = mean_squared_error(y_test,pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.12938076108955"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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