main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('ObesityDataSet_raw_and_data_sinthetic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2111 entries, 0 to 2110\n",
      "Data columns (total 17 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Gender                          2111 non-null   object \n",
      " 1   Age                             2111 non-null   float64\n",
      " 2   Height                          2111 non-null   float64\n",
      " 3   Weight                          2111 non-null   float64\n",
      " 4   family_history_with_overweight  2111 non-null   object \n",
      " 5   FAVC                            2111 non-null   object \n",
      " 6   FCVC                            2111 non-null   float64\n",
      " 7   NCP                             2111 non-null   float64\n",
      " 8   CAEC                            2111 non-null   object \n",
      " 9   SMOKE                           2111 non-null   object \n",
      " 10  CH2O                            2111 non-null   float64\n",
      " 11  SCC                             2111 non-null   object \n",
      " 12  FAF                             2111 non-null   float64\n",
      " 13  TUE                             2111 non-null   float64\n",
      " 14  CALC                            2111 non-null   object \n",
      " 15  MTRANS                          2111 non-null   object \n",
      " 16  NObeyesdad                      2111 non-null   object \n",
      "dtypes: float64(8), object(9)\n",
      "memory usage: 280.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2111 entries, 0 to 2110\n",
      "Data columns (total 17 columns):\n",
      " #   Column                          Non-Null Count  Dtype\n",
      "---  ------                          --------------  -----\n",
      " 0   Gender                          2111 non-null   bool \n",
      " 1   Age                             2111 non-null   bool \n",
      " 2   Height                          2111 non-null   bool \n",
      " 3   Weight                          2111 non-null   bool \n",
      " 4   family_history_with_overweight  2111 non-null   bool \n",
      " 5   FAVC                            2111 non-null   bool \n",
      " 6   FCVC                            2111 non-null   bool \n",
      " 7   NCP                             2111 non-null   bool \n",
      " 8   CAEC                            2111 non-null   bool \n",
      " 9   SMOKE                           2111 non-null   bool \n",
      " 10  CH2O                            2111 non-null   bool \n",
      " 11  SCC                             2111 non-null   bool \n",
      " 12  FAF                             2111 non-null   bool \n",
      " 13  TUE                             2111 non-null   bool \n",
      " 14  CALC                            2111 non-null   bool \n",
      " 15  MTRANS                          2111 non-null   bool \n",
      " 16  NObeyesdad                      2111 non-null   bool \n",
      "dtypes: bool(17)\n",
      "memory usage: 35.2 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.isnull().info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of rows :  2111\n",
      "No. of columns :  17\n",
      "Independent variables :  ['Gender', 'Age', 'Height', 'Weight', 'family_history_with_overweight', 'FAVC', 'FCVC', 'NCP', 'CAEC', 'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE', 'CALC', 'MTRANS']\n",
      "Dependent Variable :  NObeyesdad\n"
     ]
    }
   ],
   "source": [
    "print(\"No. of rows : \", dataset.shape[0])\n",
    "print(\"No. of columns : \", dataset.shape[1])\n",
    "print(\"Independent variables : \", list(dataset.columns[:-1]))\n",
    "print(\"Dependent Variable : \", dataset.columns[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Categorical Data Columns :  9\n",
      "Number of Numerical Data Columns :  8\n",
      "Numerical Columns : \n",
      " ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE']\n",
      "Categorical Columns : \n",
      " ['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS', 'NObeyesdad']\n"
     ]
    }
   ],
   "source": [
    "numerical_vals = [x for x in dataset.select_dtypes(include=np.number)]\n",
    "categorical_vals = [x for x in dataset.select_dtypes(exclude=np.number)]\n",
    "\n",
    "print(\"Number of Categorical Data Columns : \", len(categorical_vals))\n",
    "print(\"Number of Numerical Data Columns : \", len(numerical_vals))\n",
    "print(\"Numerical Columns : \\n\", numerical_vals)\n",
    "print(\"Categorical Columns : \\n\", categorical_vals) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='NObeyesdad'>"
      ]
     },
     "execution_count": 84,
     "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": [
    "dataset.groupby('NObeyesdad')['Gender'].value_counts().unstack('Gender').plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='NObeyesdad'>"
      ]
     },
     "execution_count": 85,
     "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": [
    "dataset.groupby('NObeyesdad')['family_history_with_overweight'].value_counts().unstack('family_history_with_overweight').plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='NObeyesdad'>"
      ]
     },
     "execution_count": 86,
     "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": [
    "dataset.groupby('NObeyesdad')['MTRANS'].value_counts().unstack('MTRANS').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='NObeyesdad'>"
      ]
     },
     "execution_count": 87,
     "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": [
    "dataset.groupby('NObeyesdad')['SMOKE'].value_counts().unstack('SMOKE').plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.312600</td>\n",
       "      <td>1.701677</td>\n",
       "      <td>86.586058</td>\n",
       "      <td>2.419043</td>\n",
       "      <td>2.685628</td>\n",
       "      <td>2.008011</td>\n",
       "      <td>1.010298</td>\n",
       "      <td>0.657866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.345968</td>\n",
       "      <td>0.093305</td>\n",
       "      <td>26.191172</td>\n",
       "      <td>0.533927</td>\n",
       "      <td>0.778039</td>\n",
       "      <td>0.612953</td>\n",
       "      <td>0.850592</td>\n",
       "      <td>0.608927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.947192</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>65.473343</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.658738</td>\n",
       "      <td>1.584812</td>\n",
       "      <td>0.124505</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.777890</td>\n",
       "      <td>1.700499</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>2.385502</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.768464</td>\n",
       "      <td>107.430682</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.477420</td>\n",
       "      <td>1.666678</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age       Height       Weight         FCVC          NCP  \\\n",
       "count  2111.000000  2111.000000  2111.000000  2111.000000  2111.000000   \n",
       "mean     24.312600     1.701677    86.586058     2.419043     2.685628   \n",
       "std       6.345968     0.093305    26.191172     0.533927     0.778039   \n",
       "min      14.000000     1.450000    39.000000     1.000000     1.000000   \n",
       "25%      19.947192     1.630000    65.473343     2.000000     2.658738   \n",
       "50%      22.777890     1.700499    83.000000     2.385502     3.000000   \n",
       "75%      26.000000     1.768464   107.430682     3.000000     3.000000   \n",
       "max      61.000000     1.980000   173.000000     3.000000     4.000000   \n",
       "\n",
       "              CH2O          FAF          TUE  \n",
       "count  2111.000000  2111.000000  2111.000000  \n",
       "mean      2.008011     1.010298     0.657866  \n",
       "std       0.612953     0.850592     0.608927  \n",
       "min       1.000000     0.000000     0.000000  \n",
       "25%       1.584812     0.124505     0.000000  \n",
       "50%       2.000000     1.000000     0.625350  \n",
       "75%       2.477420     1.666678     1.000000  \n",
       "max       3.000000     3.000000     2.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop('NObeyesdad', axis=1).copy()\n",
    "y = dataset['NObeyesdad'].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gender        Age    Height      Weight family_history_with_overweight  \\\n",
      "0     Female  21.000000  1.620000   64.000000                            yes   \n",
      "1     Female  21.000000  1.520000   56.000000                            yes   \n",
      "2       Male  23.000000  1.800000   77.000000                            yes   \n",
      "3       Male  27.000000  1.800000   87.000000                             no   \n",
      "4       Male  22.000000  1.780000   89.800000                             no   \n",
      "...      ...        ...       ...         ...                            ...   \n",
      "2106  Female  20.976842  1.710730  131.408528                            yes   \n",
      "2107  Female  21.982942  1.748584  133.742943                            yes   \n",
      "2108  Female  22.524036  1.752206  133.689352                            yes   \n",
      "2109  Female  24.361936  1.739450  133.346641                            yes   \n",
      "2110  Female  23.664709  1.738836  133.472641                            yes   \n",
      "\n",
      "     FAVC  FCVC  NCP       CAEC SMOKE      CH2O  SCC       FAF       TUE  \\\n",
      "0      no   2.0  3.0  Sometimes    no  2.000000   no  0.000000  1.000000   \n",
      "1      no   3.0  3.0  Sometimes   yes  3.000000  yes  3.000000  0.000000   \n",
      "2      no   2.0  3.0  Sometimes    no  2.000000   no  2.000000  1.000000   \n",
      "3      no   3.0  3.0  Sometimes    no  2.000000   no  2.000000  0.000000   \n",
      "4      no   2.0  1.0  Sometimes    no  2.000000   no  0.000000  0.000000   \n",
      "...   ...   ...  ...        ...   ...       ...  ...       ...       ...   \n",
      "2106  yes   3.0  3.0  Sometimes    no  1.728139   no  1.676269  0.906247   \n",
      "2107  yes   3.0  3.0  Sometimes    no  2.005130   no  1.341390  0.599270   \n",
      "2108  yes   3.0  3.0  Sometimes    no  2.054193   no  1.414209  0.646288   \n",
      "2109  yes   3.0  3.0  Sometimes    no  2.852339   no  1.139107  0.586035   \n",
      "2110  yes   3.0  3.0  Sometimes    no  2.863513   no  1.026452  0.714137   \n",
      "\n",
      "            CALC                 MTRANS  \n",
      "0             no  Public_Transportation  \n",
      "1      Sometimes  Public_Transportation  \n",
      "2     Frequently  Public_Transportation  \n",
      "3     Frequently                Walking  \n",
      "4      Sometimes  Public_Transportation  \n",
      "...          ...                    ...  \n",
      "2106   Sometimes  Public_Transportation  \n",
      "2107   Sometimes  Public_Transportation  \n",
      "2108   Sometimes  Public_Transportation  \n",
      "2109   Sometimes  Public_Transportation  \n",
      "2110   Sometimes  Public_Transportation  \n",
      "\n",
      "[2111 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in the categorical columns which need to be encoded : \n",
      "Gender  :  ['Female' 'Male']\n",
      "family_history_with_overweight  :  ['yes' 'no']\n",
      "FAVC  :  ['no' 'yes']\n",
      "CAEC  :  ['Sometimes' 'Frequently' 'Always' 'no']\n",
      "SMOKE  :  ['no' 'yes']\n",
      "SCC  :  ['no' 'yes']\n",
      "CALC  :  ['no' 'Sometimes' 'Frequently' 'Always']\n",
      "MTRANS  :  ['Public_Transportation' 'Walking' 'Automobile' 'Motorbike' 'Bike']\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique Values in the categorical columns which need to be encoded : \")\n",
    "for i in categorical_vals[:-1]: \n",
    "    print(i,\" : \" ,pd.unique(X[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0             Normal_Weight\n",
      "1             Normal_Weight\n",
      "2             Normal_Weight\n",
      "3        Overweight_Level_I\n",
      "4       Overweight_Level_II\n",
      "               ...         \n",
      "2106       Obesity_Type_III\n",
      "2107       Obesity_Type_III\n",
      "2108       Obesity_Type_III\n",
      "2109       Obesity_Type_III\n",
      "2110       Obesity_Type_III\n",
      "Name: NObeyesdad, Length: 2111, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Normal_Weight', 'Overweight_Level_I', 'Overweight_Level_II',\n",
       "       'Obesity_Type_I', 'Insufficient_Weight', 'Obesity_Type_II',\n",
       "       'Obesity_Type_III'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in categorical_vals[:-1]: \n",
    "    X[i] = le.fit_transform(X[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2111 entries, 0 to 2110\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Gender                          2111 non-null   int32  \n",
      " 1   Age                             2111 non-null   float64\n",
      " 2   Height                          2111 non-null   float64\n",
      " 3   Weight                          2111 non-null   float64\n",
      " 4   family_history_with_overweight  2111 non-null   int32  \n",
      " 5   FAVC                            2111 non-null   int32  \n",
      " 6   FCVC                            2111 non-null   float64\n",
      " 7   NCP                             2111 non-null   float64\n",
      " 8   CAEC                            2111 non-null   int32  \n",
      " 9   SMOKE                           2111 non-null   int32  \n",
      " 10  CH2O                            2111 non-null   float64\n",
      " 11  SCC                             2111 non-null   int32  \n",
      " 12  FAF                             2111 non-null   float64\n",
      " 13  TUE                             2111 non-null   float64\n",
      " 14  CALC                            2111 non-null   int32  \n",
      " 15  MTRANS                          2111 non-null   int32  \n",
      "dtypes: float64(8), int32(8)\n",
      "memory usage: 198.0 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
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       "      <td>0.561997</td>\n",
       "      <td>1.419172</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.011914</td>\n",
       "      <td>-0.522124</td>\n",
       "      <td>-1.947599</td>\n",
       "      <td>-1.168077</td>\n",
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       "      <td>0.404153</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>6.853997</td>\n",
       "      <td>1.618759</td>\n",
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       "      <td>2.339750</td>\n",
       "      <td>-1.080625</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.054029</td>\n",
       "      <td>-0.366090</td>\n",
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       "      <td>-0.145900</td>\n",
       "      <td>-0.013073</td>\n",
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       "      <td>1.163820</td>\n",
       "      <td>0.561997</td>\n",
       "      <td>-2.461491</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.988227</td>\n",
       "      <td>0.423582</td>\n",
       "      <td>1.054029</td>\n",
       "      <td>0.015808</td>\n",
       "      <td>-2.117337</td>\n",
       "      <td>-2.759769</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>0.404153</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.145900</td>\n",
       "      <td>-0.013073</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>1.163820</td>\n",
       "      <td>-1.080625</td>\n",
       "      <td>-2.461491</td>\n",
       "      <td>1.296280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.988227</td>\n",
       "      <td>-0.364507</td>\n",
       "      <td>0.839627</td>\n",
       "      <td>0.122740</td>\n",
       "      <td>-2.117337</td>\n",
       "      <td>-2.759769</td>\n",
       "      <td>-0.785019</td>\n",
       "      <td>-2.167023</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.145900</td>\n",
       "      <td>-0.013073</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender       Age    Height    Weight  family_history_with_overweight  \\\n",
       "0 -1.011914 -0.522124 -0.875589 -0.862558                        0.472291   \n",
       "1 -1.011914 -0.522124 -1.947599 -1.168077                        0.472291   \n",
       "2  0.988227 -0.206889  1.054029 -0.366090                        0.472291   \n",
       "3  0.988227  0.423582  1.054029  0.015808                       -2.117337   \n",
       "4  0.988227 -0.364507  0.839627  0.122740                       -2.117337   \n",
       "\n",
       "       FAVC      FCVC       NCP      CAEC     SMOKE      CH2O       SCC  \\\n",
       "0 -2.759769 -0.785019  0.404153  0.300346 -0.145900 -0.013073 -0.218272   \n",
       "1 -2.759769  1.088342  0.404153  0.300346  6.853997  1.618759  4.581439   \n",
       "2 -2.759769 -0.785019  0.404153  0.300346 -0.145900 -0.013073 -0.218272   \n",
       "3 -2.759769  1.088342  0.404153  0.300346 -0.145900 -0.013073 -0.218272   \n",
       "4 -2.759769 -0.785019 -2.167023  0.300346 -0.145900 -0.013073 -0.218272   \n",
       "\n",
       "        FAF       TUE      CALC    MTRANS  \n",
       "0 -1.188039  0.561997  1.419172  0.503337  \n",
       "1  2.339750 -1.080625 -0.521160  0.503337  \n",
       "2  1.163820  0.561997 -2.461491  0.503337  \n",
       "3  1.163820 -1.080625 -2.461491  1.296280  \n",
       "4 -1.188039 -1.080625 -0.521160  0.503337  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "  \n",
    "scaler = StandardScaler() \n",
    "  \n",
    "scaler.fit(X) \n",
    "scaled_features = scaler.transform(X) \n",
    "  \n",
    "data_feat = pd.DataFrame(scaled_features, columns = dataset.columns[:-1]) \n",
    "data_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_feat,y,test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
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       "      <th>2001</th>\n",
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       "      <td>-0.521160</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>-1.011914</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>-1.011914</td>\n",
       "      <td>-0.390041</td>\n",
       "      <td>-1.218139</td>\n",
       "      <td>-1.617340</td>\n",
       "      <td>-2.117337</td>\n",
       "      <td>-2.759769</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>-1.272151</td>\n",
       "      <td>-1.834434</td>\n",
       "      <td>-0.145900</td>\n",
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       "      <td>0.104145</td>\n",
       "      <td>-1.080625</td>\n",
       "      <td>1.419172</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>0.988227</td>\n",
       "      <td>0.265136</td>\n",
       "      <td>1.178403</td>\n",
       "      <td>0.743025</td>\n",
       "      <td>0.472291</td>\n",
       "      <td>0.362349</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>0.404153</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.145900</td>\n",
       "      <td>1.387317</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>0.944295</td>\n",
       "      <td>0.036711</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender       Age    Height    Weight  family_history_with_overweight  \\\n",
       "162  -1.011914 -0.522124 -0.768388 -1.015318                        0.472291   \n",
       "2001 -1.011914 -0.533953  0.545156  1.796170                        0.472291   \n",
       "1435 -1.011914 -0.222692 -0.428400 -0.152388                        0.472291   \n",
       "649  -1.011914 -0.390041 -1.218139 -1.617340                       -2.117337   \n",
       "1280  0.988227  0.265136  1.178403  0.743025                        0.472291   \n",
       "\n",
       "          FAVC      FCVC       NCP      CAEC     SMOKE      CH2O       SCC  \\\n",
       "162   0.362349  1.088342  0.404153 -3.969213  6.853997 -0.013073 -0.218272   \n",
       "2001  0.362349  1.088342  0.404153  0.300346 -0.145900  1.435437 -0.218272   \n",
       "1435  0.362349 -2.276674 -1.710184  0.300346 -0.145900  1.236246 -0.218272   \n",
       "649  -2.759769  1.088342 -1.272151 -1.834434 -0.145900  0.884935 -0.218272   \n",
       "1280  0.362349  1.088342  0.404153  0.300346 -0.145900  1.387317 -0.218272   \n",
       "\n",
       "           FAF       TUE      CALC    MTRANS  \n",
       "162   1.163820 -1.080625 -0.521160  0.503337  \n",
       "2001  0.553418  0.200030 -0.521160  0.503337  \n",
       "1435 -1.037118  1.645266 -0.521160  0.503337  \n",
       "649   0.104145 -1.080625  1.419172  0.503337  \n",
       "1280  0.944295  0.036711 -0.521160  0.503337  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>-1.011914</td>\n",
       "      <td>-0.615612</td>\n",
       "      <td>0.582108</td>\n",
       "      <td>-1.255930</td>\n",
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       "      <td>1.550889</td>\n",
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       "      <td>-0.1459</td>\n",
       "      <td>-0.235117</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>2.187029</td>\n",
       "      <td>2.204618</td>\n",
       "      <td>1.419172</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>-1.011914</td>\n",
       "      <td>0.265964</td>\n",
       "      <td>-0.822525</td>\n",
       "      <td>0.932553</td>\n",
       "      <td>0.472291</td>\n",
       "      <td>0.362349</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>0.404153</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.1459</td>\n",
       "      <td>1.136250</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>-1.188039</td>\n",
       "      <td>-0.550607</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>0.988227</td>\n",
       "      <td>-0.994977</td>\n",
       "      <td>1.590034</td>\n",
       "      <td>-1.015318</td>\n",
       "      <td>0.472291</td>\n",
       "      <td>0.362349</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>1.689740</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.1459</td>\n",
       "      <td>-0.013073</td>\n",
       "      <td>4.581439</td>\n",
       "      <td>1.163820</td>\n",
       "      <td>-1.080625</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>-1.875493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>-1.011914</td>\n",
       "      <td>-0.522124</td>\n",
       "      <td>-1.947599</td>\n",
       "      <td>-1.702735</td>\n",
       "      <td>-2.117337</td>\n",
       "      <td>0.362349</td>\n",
       "      <td>1.088342</td>\n",
       "      <td>-2.167023</td>\n",
       "      <td>-1.834434</td>\n",
       "      <td>-0.1459</td>\n",
       "      <td>-1.644905</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>-1.188039</td>\n",
       "      <td>-1.080625</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>0.503337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.988227</td>\n",
       "      <td>-0.364507</td>\n",
       "      <td>0.518024</td>\n",
       "      <td>-0.480660</td>\n",
       "      <td>0.472291</td>\n",
       "      <td>-2.759769</td>\n",
       "      <td>-0.785019</td>\n",
       "      <td>0.404153</td>\n",
       "      <td>0.300346</td>\n",
       "      <td>-0.1459</td>\n",
       "      <td>-0.013073</td>\n",
       "      <td>-0.218272</td>\n",
       "      <td>-0.012109</td>\n",
       "      <td>2.204618</td>\n",
       "      <td>-0.521160</td>\n",
       "      <td>-1.082550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender       Age    Height    Weight  family_history_with_overweight  \\\n",
       "544  -1.011914 -0.615612  0.582108 -1.255930                        0.472291   \n",
       "1987 -1.011914  0.265964 -0.822525  0.932553                        0.472291   \n",
       "420   0.988227 -0.994977  1.590034 -1.015318                        0.472291   \n",
       "527  -1.011914 -0.522124 -1.947599 -1.702735                       -2.117337   \n",
       "196   0.988227 -0.364507  0.518024 -0.480660                        0.472291   \n",
       "\n",
       "          FAVC      FCVC       NCP      CAEC   SMOKE      CH2O       SCC  \\\n",
       "544   0.362349 -0.785019  1.550889 -1.834434 -0.1459 -0.235117 -0.218272   \n",
       "1987  0.362349  1.088342  0.404153  0.300346 -0.1459  1.136250 -0.218272   \n",
       "420   0.362349  1.088342  1.689740  0.300346 -0.1459 -0.013073  4.581439   \n",
       "527   0.362349  1.088342 -2.167023 -1.834434 -0.1459 -1.644905 -0.218272   \n",
       "196  -2.759769 -0.785019  0.404153  0.300346 -0.1459 -0.013073 -0.218272   \n",
       "\n",
       "           FAF       TUE      CALC    MTRANS  \n",
       "544   2.187029  2.204618  1.419172  0.503337  \n",
       "1987 -1.188039 -0.550607 -0.521160  0.503337  \n",
       "420   1.163820 -1.080625 -0.521160 -1.875493  \n",
       "527  -1.188039 -1.080625 -0.521160  0.503337  \n",
       "196  -0.012109  2.204618 -0.521160 -1.082550  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 0, 0, 1, 0, 3, 5, 2, 0, 2, 4, 3, 3, 4, 6, 4, 3, 2, 6, 2, 5,\n",
       "       4, 2, 1, 6, 6, 4, 4, 0, 4, 5, 6, 1, 0, 4, 2, 1, 1, 1, 1, 3, 1, 1,\n",
       "       5, 0, 3, 1, 2, 3, 1, 1, 1, 5, 0, 6, 3, 3, 5, 3, 1, 4, 6, 0, 3, 2,\n",
       "       1, 4, 0, 1, 6, 6, 0, 0, 5, 3, 3, 1, 4, 3, 1, 2, 3, 3, 2, 2, 1, 6,\n",
       "       1, 6, 5, 3, 0, 5, 2, 0, 5, 2, 1, 3, 2, 2, 4, 2, 6, 2, 1, 4, 4, 3,\n",
       "       2, 1, 2, 1, 0, 2, 2, 3, 6, 1, 4, 3, 2, 6, 1, 3, 2, 2, 4, 3, 4, 3,\n",
       "       1, 2, 6, 4, 0, 4, 5, 2, 5, 6, 0, 0, 1, 3, 2, 4, 0, 2, 0, 0, 1, 3,\n",
       "       2, 5, 6, 5, 0, 0, 1, 1, 2, 0, 3, 0, 2, 1, 5, 6, 1, 6, 3, 4, 2, 6,\n",
       "       2, 6, 2, 5, 0, 3, 0, 5, 3, 2, 5, 4, 3, 3, 0, 4, 0, 1, 4, 5, 3, 2,\n",
       "       4, 0, 3, 3, 4, 4, 1, 4, 5, 5, 2, 4, 5, 2, 0, 2, 3, 0, 5, 3, 3, 3,\n",
       "       4, 6, 3, 5, 3, 6, 0, 4, 5, 5, 5, 4, 1, 5, 1, 2, 2, 2, 2, 0, 6, 6,\n",
       "       5, 2, 2, 2, 1, 2, 2, 0, 5, 2, 0, 1, 1, 6, 2, 3, 2, 2, 4, 4, 5, 2,\n",
       "       2, 4, 4, 1, 1, 4, 2, 4, 6, 2, 3, 2, 3, 4, 3, 1, 0, 5, 6, 4, 4, 5,\n",
       "       2, 6, 0, 3, 2, 5, 1, 2, 0, 5, 3, 3, 6, 6, 1, 5, 0, 0, 5, 1, 6, 5,\n",
       "       6, 4, 5, 3, 1, 4, 5, 6, 2, 4, 6, 1, 0, 1, 4, 0, 1, 4, 2, 2, 4, 0,\n",
       "       1, 1, 3, 4, 2, 5, 2, 0, 0, 0, 2, 4, 6, 2, 4, 3, 5, 2, 5, 6, 6, 4,\n",
       "       6, 1, 0, 6, 6, 6, 5, 6, 6, 3, 2, 2, 3, 4, 2, 6, 5, 4, 0, 2, 1, 3,\n",
       "       2, 5, 0, 1, 2, 2, 6, 6, 1, 0, 4, 5, 4, 4, 1, 2, 3, 5, 4, 6, 3, 5,\n",
       "       0, 4, 5, 4, 1, 0, 1, 5, 2, 1, 0, 4, 1, 4, 6, 5, 5, 5, 4, 0, 5, 2,\n",
       "       5, 3, 1, 3, 6])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 2, ..., 6, 2, 5])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 0, 0, 1, 0, 3, 5, 2, 0, 2, 4, 3, 3, 4, 6, 4, 3, 2, 6, 2, 5,\n",
       "       4, 2, 1, 6, 6, 4, 4, 0, 4, 6, 6, 1, 0, 4, 2, 1, 1, 1, 1, 3, 1, 1,\n",
       "       5, 0, 3, 1, 2, 3, 5, 0, 1, 5, 0, 6, 3, 3, 5, 3, 1, 4, 6, 0, 3, 2,\n",
       "       1, 4, 0, 1, 6, 6, 0, 0, 5, 3, 3, 1, 4, 3, 1, 2, 3, 3, 2, 2, 1, 5,\n",
       "       1, 6, 5, 3, 0, 5, 2, 0, 5, 2, 5, 3, 2, 2, 4, 2, 6, 2, 1, 4, 4, 3,\n",
       "       2, 1, 2, 1, 0, 2, 2, 3, 6, 5, 4, 3, 2, 6, 1, 3, 2, 2, 4, 3, 4, 3,\n",
       "       1, 2, 6, 4, 0, 4, 5, 2, 5, 5, 0, 0, 0, 3, 2, 4, 0, 2, 0, 0, 1, 3,\n",
       "       2, 5, 6, 5, 0, 0, 1, 0, 2, 0, 3, 0, 3, 5, 5, 6, 1, 6, 3, 4, 2, 6,\n",
       "       2, 6, 2, 5, 0, 3, 0, 5, 3, 2, 5, 4, 3, 3, 0, 4, 0, 1, 4, 1, 3, 2,\n",
       "       4, 0, 3, 3, 4, 4, 1, 4, 5, 5, 2, 4, 5, 2, 0, 2, 3, 0, 5, 3, 3, 2,\n",
       "       4, 6, 3, 5, 3, 6, 0, 4, 1, 5, 5, 4, 1, 5, 1, 2, 2, 2, 2, 0, 6, 6,\n",
       "       5, 2, 2, 2, 1, 2, 2, 0, 5, 2, 0, 1, 1, 6, 2, 3, 2, 2, 4, 4, 5, 2,\n",
       "       2, 4, 4, 1, 1, 4, 3, 4, 6, 2, 3, 2, 3, 4, 3, 0, 0, 5, 6, 4, 4, 5,\n",
       "       2, 6, 0, 3, 2, 5, 1, 2, 0, 5, 3, 3, 6, 6, 1, 5, 0, 0, 5, 1, 6, 5,\n",
       "       6, 4, 5, 3, 1, 4, 5, 6, 2, 4, 6, 1, 0, 1, 4, 0, 1, 4, 2, 2, 4, 0,\n",
       "       1, 1, 3, 4, 2, 5, 2, 0, 0, 0, 2, 4, 6, 2, 4, 3, 5, 2, 5, 6, 6, 4,\n",
       "       6, 1, 0, 6, 6, 6, 5, 6, 6, 3, 2, 2, 3, 4, 2, 6, 5, 4, 0, 2, 1, 3,\n",
       "       2, 5, 0, 1, 2, 2, 6, 6, 1, 0, 4, 5, 4, 4, 1, 6, 3, 5, 4, 6, 3, 5,\n",
       "       0, 4, 5, 4, 1, 0, 1, 5, 2, 1, 0, 4, 1, 4, 6, 6, 5, 5, 4, 0, 5, 2,\n",
       "       5, 3, 0, 3, 6])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[56  0  0  0  0  0  0]\n",
      " [ 5 53  0  0  0  4  0]\n",
      " [ 0  0 75  2  0  0  1]\n",
      " [ 0  0  1 57  0  0  0]\n",
      " [ 0  0  0  0 63  0  0]\n",
      " [ 0  2  0  0  0 52  2]\n",
      " [ 0  0  0  0  0  2 48]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9550827423167849"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96        56\n",
      "           1       0.96      0.85      0.91        62\n",
      "           2       0.99      0.96      0.97        78\n",
      "           3       0.97      0.98      0.97        58\n",
      "           4       1.00      1.00      1.00        63\n",
      "           5       0.90      0.93      0.91        56\n",
      "           6       0.94      0.96      0.95        50\n",
      "\n",
      "    accuracy                           0.96       423\n",
      "   macro avg       0.95      0.96      0.95       423\n",
      "weighted avg       0.96      0.96      0.95       423\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.20 %\n",
      "Standard Deviation: 1.34 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 94.20 %\n",
      "Best Parameters: {'C': 1, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = [{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['linear']},\n",
    "              {'C': [0.25, 0.5, 0.75, 1], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n",
    "grid_search = GridSearchCV(estimator = classifier,\n",
    "                           param_grid = parameters,\n",
    "                           scoring = 'accuracy',\n",
    "                           cv = 10,\n",
    "                           n_jobs = -1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_accuracy = grid_search.best_score_\n",
    "best_parameters = grid_search.best_params_\n",
    "print(\"Best Accuracy: {:.2f} %\".format(best_accuracy*100))\n",
    "print(\"Best Parameters:\", best_parameters)"
   ]
  }
 ],
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