pandemic_impact_on_students_data_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('logistic_regression_model_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Educational_level</th>\n",
       "      <th>Screening_time</th>\n",
       "      <th>health_problems</th>\n",
       "      <th>Irregular_eating_habits</th>\n",
       "      <th>hobby_hours</th>\n",
       "      <th>increased_sleep_hours</th>\n",
       "      <th>social_media_hours</th>\n",
       "      <th>Exercise</th>\n",
       "      <th>depressiveness</th>\n",
       "      <th>anxiety_interference</th>\n",
       "      <th>overthinking</th>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <th>focus_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16 to 18 years</td>\n",
       "      <td>Higher Secondary School</td>\n",
       "      <td>3 to 4 hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Never</td>\n",
       "      <td>Yes</td>\n",
       "      <td>May be</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16 to 18 years</td>\n",
       "      <td>Higher Secondary School</td>\n",
       "      <td>2 to 3 hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Occasionally</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16 to 18 years</td>\n",
       "      <td>Higher Secondary School</td>\n",
       "      <td>4 to 5 hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Occasionally</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16 to 18 years</td>\n",
       "      <td>Higher Secondary School</td>\n",
       "      <td>4 to 5 hours</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Never</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16 to 18 years</td>\n",
       "      <td>Higher Secondary School</td>\n",
       "      <td>4 to 5 hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Occasionally</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Educational_level Screening_time health_problems  \\\n",
       "0  16 to 18 years  Higher Secondary School   3 to 4 hours             Yes   \n",
       "1  16 to 18 years  Higher Secondary School   2 to 3 hours             Yes   \n",
       "2  16 to 18 years  Higher Secondary School   4 to 5 hours             Yes   \n",
       "3  16 to 18 years  Higher Secondary School   4 to 5 hours              No   \n",
       "4  16 to 18 years  Higher Secondary School   4 to 5 hours             Yes   \n",
       "\n",
       "  Irregular_eating_habits  hobby_hours  \\\n",
       "0                     Yes            1   \n",
       "1                     Yes            1   \n",
       "2                     Yes            0   \n",
       "3                      No            1   \n",
       "4                     Yes            2   \n",
       "\n",
       "   increased_sleep_hours                                                                                                                            \\\n",
       "0                                                  4                                                                                                 \n",
       "1                                                  2                                                                                                 \n",
       "2                                                  2                                                                                                 \n",
       "3                                                  2                                                                                                 \n",
       "4                                                  2                                                                                                 \n",
       "\n",
       "   social_media_hours   Exercise depressiveness anxiety_interference  \\\n",
       "0                   4  Sometimes          Never                  Yes   \n",
       "1                   3     Rarely   Occasionally                Maybe   \n",
       "2                   2  Sometimes   Occasionally                  Yes   \n",
       "3                   2     Rarely          Never                   No   \n",
       "4                   1     Rarely   Occasionally                   No   \n",
       "\n",
       "  overthinking  concentration_difficulty  online_difficulty_level  focus_level  \n",
       "0       May be                         1                        1            5  \n",
       "1          Yes                         5                        4            5  \n",
       "2          Yes                         4                        5            3  \n",
       "3           No                         2                        3            3  \n",
       "4          Yes                         3                        3            3  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 15)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1100 entries, 0 to 1099\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                                                                                                           Non-Null Count  Dtype \n",
      "---  ------                                                                                                                                           --------------  ----- \n",
      " 0   Age                                                                                                                                              1100 non-null   object\n",
      " 1   Educational_level                                                                                                                                1100 non-null   object\n",
      " 2   Screening_time                                                                                                                                   1100 non-null   object\n",
      " 3   health_problems                                                                                                                                  1075 non-null   object\n",
      " 4   Irregular_eating_habits                                                                                                                          1100 non-null   object\n",
      " 5   hobby_hours                                                                                                                                      1100 non-null   int64 \n",
      " 6   increased_sleep_hours                                                                                                                            1100 non-null   int64 \n",
      " 7   social_media_hours                                                                                                                               1100 non-null   int64 \n",
      " 8   Exercise                                                                                                                                         1100 non-null   object\n",
      " 9   depressiveness                                                                                                                                   1100 non-null   object\n",
      " 10  anxiety_interference                                                                                                                             1100 non-null   object\n",
      " 11  overthinking                                                                                                                                     1100 non-null   object\n",
      " 12  concentration_difficulty                                                                                                                         1100 non-null   int64 \n",
      " 13  online_difficulty_level                                                                                                                          1100 non-null   int64 \n",
      " 14  focus_level                                                                                                                                      1100 non-null   int64 \n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 129.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#check the information\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby_hours</th>\n",
       "      <th>increased_sleep_hours</th>\n",
       "      <th>social_media_hours</th>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <th>focus_level</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.958182</td>\n",
       "      <td>1.891818</td>\n",
       "      <td>2.385455</td>\n",
       "      <td>3.590909</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.620909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.776167</td>\n",
       "      <td>1.266969</td>\n",
       "      <td>1.112657</td>\n",
       "      <td>1.077484</td>\n",
       "      <td>1.090116</td>\n",
       "      <td>1.081282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       hobby_hours  \\\n",
       "count  1100.000000   \n",
       "mean      0.958182   \n",
       "std       0.776167   \n",
       "min       0.000000   \n",
       "25%       0.000000   \n",
       "50%       1.000000   \n",
       "75%       2.000000   \n",
       "max       2.000000   \n",
       "\n",
       "       increased_sleep_hours                                                                                                                            \\\n",
       "count                                        1100.000000                                                                                                 \n",
       "mean                                            1.891818                                                                                                 \n",
       "std                                             1.266969                                                                                                 \n",
       "min                                             0.000000                                                                                                 \n",
       "25%                                             1.000000                                                                                                 \n",
       "50%                                             2.000000                                                                                                 \n",
       "75%                                             3.000000                                                                                                 \n",
       "max                                             4.000000                                                                                                 \n",
       "\n",
       "       social_media_hours  concentration_difficulty  online_difficulty_level  \\\n",
       "count         1100.000000               1100.000000              1100.000000   \n",
       "mean             2.385455                  3.590909                 3.200000   \n",
       "std              1.112657                  1.077484                 1.090116   \n",
       "min              0.000000                  1.000000                 1.000000   \n",
       "25%              2.000000                  3.000000                 2.000000   \n",
       "50%              2.000000                  4.000000                 3.000000   \n",
       "75%              3.000000                  4.000000                 4.000000   \n",
       "max              4.000000                  5.000000                 5.000000   \n",
       "\n",
       "       focus_level  \n",
       "count  1100.000000  \n",
       "mean      3.620909  \n",
       "std       1.081282  \n",
       "min       1.000000  \n",
       "25%       3.000000  \n",
       "50%       4.000000  \n",
       "75%       4.000000  \n",
       "max       5.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check description\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Educational_level</th>\n",
       "      <th>Screening_time</th>\n",
       "      <th>health_problems</th>\n",
       "      <th>Irregular_eating_habits</th>\n",
       "      <th>hobby_hours</th>\n",
       "      <th>increased_sleep_hours</th>\n",
       "      <th>social_media_hours</th>\n",
       "      <th>Exercise</th>\n",
       "      <th>depressiveness</th>\n",
       "      <th>anxiety_interference</th>\n",
       "      <th>overthinking</th>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <th>focus_level</th>\n",
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       "      <th>count</th>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>1075</td>\n",
       "      <td>1100</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>18 to 22 years</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>4 to 5 hours</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Occasionally</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>355</td>\n",
       "      <td>361</td>\n",
       "      <td>326</td>\n",
       "      <td>578</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.958182</td>\n",
       "      <td>1.891818</td>\n",
       "      <td>2.385455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.590909</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.620909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.776167</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age Educational_level Screening_time health_problems  \\\n",
       "count             1100              1100           1100            1075   \n",
       "unique               4                 4              4               2   \n",
       "top     18 to 22 years    Under Graduate   4 to 5 hours              No   \n",
       "freq               355               361            326             578   \n",
       "mean               NaN               NaN            NaN             NaN   \n",
       "std                NaN               NaN            NaN             NaN   \n",
       "min                NaN               NaN            NaN             NaN   \n",
       "25%                NaN               NaN            NaN             NaN   \n",
       "50%                NaN               NaN            NaN             NaN   \n",
       "75%                NaN               NaN            NaN             NaN   \n",
       "max                NaN               NaN            NaN             NaN   \n",
       "\n",
       "       Irregular_eating_habits  hobby_hours  \\\n",
       "count                     1100  1100.000000   \n",
       "unique                       3          NaN   \n",
       "top                         No          NaN   \n",
       "freq                       562          NaN   \n",
       "mean                       NaN     0.958182   \n",
       "std                        NaN     0.776167   \n",
       "min                        NaN     0.000000   \n",
       "25%                        NaN     0.000000   \n",
       "50%                        NaN     1.000000   \n",
       "75%                        NaN     2.000000   \n",
       "max                        NaN     2.000000   \n",
       "\n",
       "        increased_sleep_hours                                                                                                                            \\\n",
       "count                                         1100.000000                                                                                                 \n",
       "unique                                                NaN                                                                                                 \n",
       "top                                                   NaN                                                                                                 \n",
       "freq                                                  NaN                                                                                                 \n",
       "mean                                             1.891818                                                                                                 \n",
       "std                                              1.266969                                                                                                 \n",
       "min                                              0.000000                                                                                                 \n",
       "25%                                              1.000000                                                                                                 \n",
       "50%                                              2.000000                                                                                                 \n",
       "75%                                              3.000000                                                                                                 \n",
       "max                                              4.000000                                                                                                 \n",
       "\n",
       "        social_media_hours   Exercise depressiveness anxiety_interference  \\\n",
       "count          1100.000000       1100           1100                 1100   \n",
       "unique                 NaN          4              4                    3   \n",
       "top                    NaN  Sometimes   Occasionally                  Yes   \n",
       "freq                   NaN        428            478                  484   \n",
       "mean              2.385455        NaN            NaN                  NaN   \n",
       "std               1.112657        NaN            NaN                  NaN   \n",
       "min               0.000000        NaN            NaN                  NaN   \n",
       "25%               2.000000        NaN            NaN                  NaN   \n",
       "50%               2.000000        NaN            NaN                  NaN   \n",
       "75%               3.000000        NaN            NaN                  NaN   \n",
       "max               4.000000        NaN            NaN                  NaN   \n",
       "\n",
       "       overthinking  concentration_difficulty  online_difficulty_level  \\\n",
       "count          1100               1100.000000              1100.000000   \n",
       "unique            3                       NaN                      NaN   \n",
       "top             Yes                       NaN                      NaN   \n",
       "freq            562                       NaN                      NaN   \n",
       "mean            NaN                  3.590909                 3.200000   \n",
       "std             NaN                  1.077484                 1.090116   \n",
       "min             NaN                  1.000000                 1.000000   \n",
       "25%             NaN                  3.000000                 2.000000   \n",
       "50%             NaN                  4.000000                 3.000000   \n",
       "75%             NaN                  4.000000                 4.000000   \n",
       "max             NaN                  5.000000                 5.000000   \n",
       "\n",
       "        focus_level  \n",
       "count   1100.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "mean       3.620909  \n",
       "std        1.081282  \n",
       "min        1.000000  \n",
       "25%        3.000000  \n",
       "50%        4.000000  \n",
       "75%        4.000000  \n",
       "max        5.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check description\n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                                                                                                                                 0\n",
       "Educational_level                                                                                                                                   0\n",
       "Screening_time                                                                                                                                      0\n",
       "health_problems                                                                                                                                    25\n",
       "Irregular_eating_habits                                                                                                                             0\n",
       "hobby_hours                                                                                                                                         0\n",
       "increased_sleep_hours                                                                                                                               0\n",
       "social_media_hours                                                                                                                                  0\n",
       "Exercise                                                                                                                                            0\n",
       "depressiveness                                                                                                                                      0\n",
       "anxiety_interference                                                                                                                                0\n",
       "overthinking                                                                                                                                        0\n",
       "concentration_difficulty                                                                                                                            0\n",
       "online_difficulty_level                                                                                                                             0\n",
       "focus_level                                                                                                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                                                                                                                                0\n",
       "Educational_level                                                                                                                                  0\n",
       "Screening_time                                                                                                                                     0\n",
       "health_problems                                                                                                                                    0\n",
       "Irregular_eating_habits                                                                                                                            0\n",
       "hobby_hours                                                                                                                                        0\n",
       "increased_sleep_hours                                                                                                                              0\n",
       "social_media_hours                                                                                                                                 0\n",
       "Exercise                                                                                                                                           0\n",
       "depressiveness                                                                                                                                     0\n",
       "anxiety_interference                                                                                                                               0\n",
       "overthinking                                                                                                                                       0\n",
       "concentration_difficulty                                                                                                                           0\n",
       "online_difficulty_level                                                                                                                            0\n",
       "focus_level                                                                                                                                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv('finalmodel.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is OS\n",
      " Volume Serial Number is 6C70-2416\n",
      "\n",
      " Directory of C:\\Users\\Admin\n",
      "\n",
      "29-04-2022  11:26    <DIR>          .\n",
      "11-01-2022  12:40    <DIR>          ..\n",
      "23-04-2022  15:10    <DIR>          .android\n",
      "07-08-2020  17:53    <DIR>          .AndroidStudio4.0\n",
      "26-02-2021  14:09    <DIR>          .atom\n",
      "02-09-2021  17:21             2,781 .bash_history\n",
      "22-07-2021  11:29    <DIR>          .cache\n",
      "21-09-2020  14:55    <DIR>          .conda\n",
      "21-09-2020  14:44                43 .condarc\n",
      "09-10-2021  23:32    <DIR>          .config\n",
      "14-09-2020  15:54                16 .emulator_console_auth_token\n",
      "12-03-2022  16:31                60 .gitconfig\n",
      "14-09-2020  16:52    <DIR>          .gradle\n",
      "20-07-2020  17:46    <DIR>          .idea\n",
      "13-02-2021  11:49    <DIR>          .idlerc\n",
      "29-04-2022  10:05    <DIR>          .ipynb_checkpoints\n",
      "21-09-2020  14:45    <DIR>          .ipython\n",
      "03-08-2020  19:29    <DIR>          .jdks\n",
      "26-06-2021  22:27    <DIR>          .jupyter\n",
      "26-09-2021  11:26    <DIR>          .matplotlib\n",
      "04-10-2021  13:28               168 .packettracer\n",
      "31-08-2021  15:07    <DIR>          .ssh\n",
      "27-11-2020  20:46    <DIR>          .thumbnails\n",
      "02-09-2021  17:21             1,569 .viminfo\n",
      "09-03-2022  09:41    <DIR>          .VirtualBox\n",
      "12-09-2020  15:43    <DIR>          .vscode\n",
      "31-08-2021  17:54               202 _netrc\n",
      "26-03-2021  12:58    <DIR>          3D Objects\n",
      "10-10-2021  23:46            93,759 Agglomerative Exp6.ipynb\n",
      "09-10-2021  23:10    <DIR>          anaconda3\n",
      "27-03-2022  20:43    <DIR>          AndroidStudioProjects\n",
      "09-10-2021  13:18            31,934 Apriori Exp7.ipynb\n",
      "18-08-2021  20:07    <DIR>          Art_Gallery_Management_System_Django\n",
      "24-01-2020  23:35    <DIR>          Autodesk\n",
      "26-01-2021  11:43    <DIR>          calculator\n",
      "26-09-2021  11:21            53,645 car_data1.csv\n",
      "12-03-2022  12:07    <DIR>          charitable\n",
      "02-10-2021  01:04    <DIR>          Cisco Packet Tracer 8.0\n",
      "09-10-2021  12:36            51,388 classification Exp4.ipynb\n",
      "06-06-2021  15:34    <DIR>          clock_app\n",
      "11-01-2022  13:23    <DIR>          Contacts\n",
      "15-04-2022  19:37         1,875,009 Covid19_data_report.html\n",
      "29-04-2022  09:46         2,838,463 covid19_impact_on_students_data_analysis.ipynb\n",
      "29-04-2022  10:42           173,974 covid19_impact_on_students_data_model.ipynb\n",
      "22-07-2021  11:30    <DIR>          Cryptable-master\n",
      "11-11-2021  17:02            43,592 data.csv\n",
      "05-04-2022  21:43           872,576 data_analysis.ipynb\n",
      "11-11-2021  17:59         1,748,099 data_report.html\n",
      "26-03-2022  01:41            13,828 data1.csv\n",
      "23-07-2021  19:29    <DIR>          DB\n",
      "05-10-2021  16:37            23,873 diabetes.csv\n",
      "28-04-2021  14:57    <DIR>          django\n",
      "02-09-2021  16:54    <DIR>          django_prj\n",
      "02-09-2021  17:26           114,322 django_prj.zip\n",
      "18-07-2021  00:38    <DIR>          django_project\n",
      "18-07-2021  00:59        20,358,211 django_project.rar\n",
      "03-04-2022  19:21    <DIR>          Documents\n",
      "06-03-2022  17:48    <DIR>          Donate\n",
      "29-04-2022  11:26    <DIR>          Downloads\n",
      "26-09-2021  11:21             1,672 dwmexp4 (1).py\n",
      "28-04-2022  19:46    <DIR>          EDA project\n",
      "11-10-2021  16:05            23,516 exp 5 K-means.ipynb\n",
      "10-10-2021  23:38           138,708 exp 6 Agglomerative.ipynb\n",
      "11-01-2022  13:23    <DIR>          Favorites\n",
      "06-04-2022  00:05         1,830,851 final_data_report.html\n",
      "29-04-2022  09:59           155,254 finalanalysis.csv\n",
      "06-04-2022  00:04            35,114 finaldata.csv\n",
      "29-04-2022  11:26           102,085 finalmodel.csv\n",
      "10-04-2021  21:25    <DIR>          first-react-app\n",
      "13-11-2020  16:04    <DIR>          flutter_dev\n",
      "31-08-2021  18:15    <DIR>          heroku\n",
      "26-09-2021  13:09            43,413 house_price_prediction.ipynb\n",
      "06-08-2020  16:35    <DIR>          IdeaProjects\n",
      "20-02-2021  13:48    <DIR>          info_app\n",
      "08-07-2021  22:00            20,414 IRIS DATASET.ipynb\n",
      "11-10-2021  16:02             5,107 Iris.csv\n",
      "19-06-2020  18:54                 0 java\n",
      "11-10-2021  16:07            18,179 KMeans Clustering Exp5.ipynb\n",
      "02-07-2021  13:46               555 linear_regression_with_python.ipynb\n",
      "11-01-2022  13:23    <DIR>          Links\n",
      "19-02-2021  11:48    <DIR>          liquid_animation\n",
      "25-03-2022  00:03            46,100 Logistic Regression1.ipynb\n",
      "15-04-2022  15:50            60,105 Logistic_Regression_Model.ipynb\n",
      "29-04-2022  11:26           102,110 logistic_regression_model_data.csv\n",
      "26-03-2022  23:50            13,781 lr_model_data.csv\n",
      "15-04-2022  19:25            65,676 ML_model_data.csv\n",
      "11-01-2022  13:23    <DIR>          Music\n",
      "06-01-2021  14:04    <DIR>          my_flutterapp\n",
      "06-03-2022  22:35    <DIR>          myapp\n",
      "06-04-2022  00:03            35,121 mydata.csv\n",
      "28-04-2021  15:17    <DIR>          mydjangoproject\n",
      "25-04-2022  17:31    <DIR>          OneDrive\n",
      "12-07-2020  13:55    <DIR>          opera autoupdate\n",
      "14-02-2021  17:24    <DIR>          output\n",
      "05-10-2021  16:41            18,802 page rank Exp9.ipynb\n",
      "29-04-2022  10:05            99,527 pandemic_impact_on_students_data_analysis.ipynb\n",
      "29-04-2022  11:21           326,435 pandemic_impact_on_students_data_modelling.ipynb\n",
      "29-04-2022  09:59           155,279 pandemic_Survey_Data_Analysis.csv\n",
      "03-04-2022  01:39    <DIR>          Pictures\n",
      "25-03-2022  00:01            13,850 predict1.csv\n",
      "26-03-2022  23:32            13,858 prj_final.csv\n",
      "10-05-2021  17:13    <DIR>          project\n",
      "15-04-2022  12:20         1,419,107 project_data_analysis.ipynb\n",
      "05-08-2020  17:52    <DIR>          PycharmProjects\n",
      "27-06-2021  12:43             2,640 que3.ipynb\n",
      "11-04-2021  15:00    <DIR>          quote-app\n",
      "03-11-2021  17:25            43,613 responses.csv\n",
      "24-04-2019  03:13    <DIR>          Roaming\n",
      "11-01-2022  13:23    <DIR>          Saved Games\n",
      "11-01-2022  13:23    <DIR>          Searches\n",
      "26-06-2021  22:41             1,650 series.ipynb\n",
      "09-10-2021  11:22             4,286 shopping-data.csv\n",
      "05-10-2021  15:01           302,908 store_data.csv\n",
      "05-10-2021  15:36               964 store1_data.csv\n",
      "15-04-2022  19:26           155,285 Survey_Data_Analysis.csv\n",
      "07-03-2021  13:47    <DIR>          test\n",
      "27-03-2022  17:28            13,825 test_data.csv\n",
      "06-07-2020  22:02               420 This PC - Shortcut.lnk\n",
      "26-09-2021  11:22                 0 untitled\n",
      "26-03-2022  01:42                72 Untitled.ipynb\n",
      "05-10-2021  16:41               555 Untitled2.ipynb\n",
      "26-03-2022  22:54            41,200 Untitled3.ipynb\n",
      "26-03-2022  22:56             5,288 Untitled4.ipynb\n",
      "03-10-2021  22:31           113,749 Untitled5.ipynb\n",
      "09-10-2021  11:21            16,963 untitled6.ipynb\n",
      "11-01-2022  13:23    <DIR>          Videos\n",
      "09-03-2022  08:21    <DIR>          VirtualBox VMs\n",
      "11-11-2021  13:17    <DIR>          wekafiles\n",
      "              63 File(s)     33,745,549 bytes\n",
      "              65 Dir(s)  1,730,422,865,920 bytes free\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating instance of one-hot-encoder\n",
    "encoder = OneHotEncoder(handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  Yes\n",
       "0   0    1\n",
       "1   0    1\n",
       "2   0    1\n",
       "3   1    0\n",
       "4   0    1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data['health_problems'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Yes\n",
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    0\n",
       "4    1"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_df=pd.get_dummies(data['health_problems'],drop_first=True)\n",
    "encoder_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = data.join(encoder_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Age        Educational_level Screening_time health_problems  \\\n",
      "0     16 to 18 years  Higher Secondary School   3 to 4 hours             Yes   \n",
      "1     16 to 18 years  Higher Secondary School   2 to 3 hours             Yes   \n",
      "2     16 to 18 years  Higher Secondary School   4 to 5 hours             Yes   \n",
      "3     16 to 18 years  Higher Secondary School   4 to 5 hours              No   \n",
      "4     16 to 18 years  Higher Secondary School   4 to 5 hours             Yes   \n",
      "...              ...                      ...            ...             ...   \n",
      "1095  18 to 22 years           Under Graduate   4 to 5 hours              No   \n",
      "1096  13 to 15 years         Secondary School   2 to 3 hours              No   \n",
      "1097  23 to 26 years            Post Graduate   5 to 6 hours              No   \n",
      "1098  23 to 26 years            Post Graduate   5 to 6 hours             Yes   \n",
      "1099  23 to 26 years            Post Graduate   5 to 6 hours              No   \n",
      "\n",
      "     Irregular_eating_habits  hobby_hours  \\\n",
      "0                        Yes            1   \n",
      "1                        Yes            1   \n",
      "2                        Yes            0   \n",
      "3                         No            1   \n",
      "4                        Yes            2   \n",
      "...                      ...          ...   \n",
      "1095                      No            1   \n",
      "1096                      No            2   \n",
      "1097                      No            0   \n",
      "1098                     Yes            0   \n",
      "1099                      No            0   \n",
      "\n",
      "      increased_sleep_hours                                                                                                                            \\\n",
      "0                                                     4                                                                                                 \n",
      "1                                                     2                                                                                                 \n",
      "2                                                     2                                                                                                 \n",
      "3                                                     2                                                                                                 \n",
      "4                                                     2                                                                                                 \n",
      "...                                                 ...                                                                                                 \n",
      "1095                                                  2                                                                                                 \n",
      "1096                                                  4                                                                                                 \n",
      "1097                                                  0                                                                                                 \n",
      "1098                                                  0                                                                                                 \n",
      "1099                                                  0                                                                                                 \n",
      "\n",
      "      social_media_hours   Exercise depressiveness anxiety_interference  \\\n",
      "0                      4  Sometimes          Never                  Yes   \n",
      "1                      3     Rarely   Occasionally                Maybe   \n",
      "2                      2  Sometimes   Occasionally                  Yes   \n",
      "3                      2     Rarely          Never                   No   \n",
      "4                      1     Rarely   Occasionally                   No   \n",
      "...                  ...        ...            ...                  ...   \n",
      "1095                   2     Always   Occasionally                  Yes   \n",
      "1096                   2  Sometimes   Occasionally                  Yes   \n",
      "1097                   0  Sometimes   Occasionally                   No   \n",
      "1098                   0  Sometimes   Occasionally                  Yes   \n",
      "1099                   0     Always         Rarely                   No   \n",
      "\n",
      "     overthinking  concentration_difficulty  online_difficulty_level  \\\n",
      "0          May be                         1                        1   \n",
      "1             Yes                         5                        4   \n",
      "2             Yes                         4                        5   \n",
      "3              No                         2                        3   \n",
      "4             Yes                         3                        3   \n",
      "...           ...                       ...                      ...   \n",
      "1095          Yes                         4                        4   \n",
      "1096          Yes                         2                        2   \n",
      "1097          Yes                         5                        3   \n",
      "1098          Yes                         4                        5   \n",
      "1099           No                         4                        4   \n",
      "\n",
      "      focus_level  Yes  \n",
      "0               5    1  \n",
      "1               5    1  \n",
      "2               3    1  \n",
      "3               3    0  \n",
      "4               3    1  \n",
      "...           ...  ...  \n",
      "1095            4    0  \n",
      "1096            2    0  \n",
      "1097            4    0  \n",
      "1098            4    1  \n",
      "1099            4    0  \n",
      "\n",
      "[1100 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "print(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Age        Educational_level Screening_time  \\\n",
      "0     16 to 18 years  Higher Secondary School   3 to 4 hours   \n",
      "1     16 to 18 years  Higher Secondary School   2 to 3 hours   \n",
      "2     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "3     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "4     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "...              ...                      ...            ...   \n",
      "1095  18 to 22 years           Under Graduate   4 to 5 hours   \n",
      "1096  13 to 15 years         Secondary School   2 to 3 hours   \n",
      "1097  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "1098  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "1099  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "\n",
      "     Irregular_eating_habits  hobby_hours  \\\n",
      "0                        Yes            1   \n",
      "1                        Yes            1   \n",
      "2                        Yes            0   \n",
      "3                         No            1   \n",
      "4                        Yes            2   \n",
      "...                      ...          ...   \n",
      "1095                      No            1   \n",
      "1096                      No            2   \n",
      "1097                      No            0   \n",
      "1098                     Yes            0   \n",
      "1099                      No            0   \n",
      "\n",
      "      increased_sleep_hours                                                                                                                            \\\n",
      "0                                                     4                                                                                                 \n",
      "1                                                     2                                                                                                 \n",
      "2                                                     2                                                                                                 \n",
      "3                                                     2                                                                                                 \n",
      "4                                                     2                                                                                                 \n",
      "...                                                 ...                                                                                                 \n",
      "1095                                                  2                                                                                                 \n",
      "1096                                                  4                                                                                                 \n",
      "1097                                                  0                                                                                                 \n",
      "1098                                                  0                                                                                                 \n",
      "1099                                                  0                                                                                                 \n",
      "\n",
      "      social_media_hours   Exercise depressiveness anxiety_interference  \\\n",
      "0                      4  Sometimes          Never                  Yes   \n",
      "1                      3     Rarely   Occasionally                Maybe   \n",
      "2                      2  Sometimes   Occasionally                  Yes   \n",
      "3                      2     Rarely          Never                   No   \n",
      "4                      1     Rarely   Occasionally                   No   \n",
      "...                  ...        ...            ...                  ...   \n",
      "1095                   2     Always   Occasionally                  Yes   \n",
      "1096                   2  Sometimes   Occasionally                  Yes   \n",
      "1097                   0  Sometimes   Occasionally                   No   \n",
      "1098                   0  Sometimes   Occasionally                  Yes   \n",
      "1099                   0     Always         Rarely                   No   \n",
      "\n",
      "     overthinking  concentration_difficulty  online_difficulty_level  \\\n",
      "0          May be                         1                        1   \n",
      "1             Yes                         5                        4   \n",
      "2             Yes                         4                        5   \n",
      "3              No                         2                        3   \n",
      "4             Yes                         3                        3   \n",
      "...           ...                       ...                      ...   \n",
      "1095          Yes                         4                        4   \n",
      "1096          Yes                         2                        2   \n",
      "1097          Yes                         5                        3   \n",
      "1098          Yes                         4                        5   \n",
      "1099           No                         4                        4   \n",
      "\n",
      "      focus_level  Yes  \n",
      "0               5    1  \n",
      "1               5    1  \n",
      "2               3    1  \n",
      "3               3    0  \n",
      "4               3    1  \n",
      "...           ...  ...  \n",
      "1095            4    0  \n",
      "1096            2    0  \n",
      "1097            4    0  \n",
      "1098            4    1  \n",
      "1099            4    0  \n",
      "\n",
      "[1100 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "#drop 'health_problems' column\n",
    "final_df.drop('health_problems', axis=1, inplace=True)\n",
    "print(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Age        Educational_level Screening_time  \\\n",
      "0     16 to 18 years  Higher Secondary School   3 to 4 hours   \n",
      "1     16 to 18 years  Higher Secondary School   2 to 3 hours   \n",
      "2     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "3     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "4     16 to 18 years  Higher Secondary School   4 to 5 hours   \n",
      "...              ...                      ...            ...   \n",
      "1095  18 to 22 years           Under Graduate   4 to 5 hours   \n",
      "1096  13 to 15 years         Secondary School   2 to 3 hours   \n",
      "1097  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "1098  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "1099  23 to 26 years            Post Graduate   5 to 6 hours   \n",
      "\n",
      "     Irregular_eating_habits  hobby_hours  increased_sleeping_hours  \\\n",
      "0                        Yes            1                         4   \n",
      "1                        Yes            1                         2   \n",
      "2                        Yes            0                         2   \n",
      "3                         No            1                         2   \n",
      "4                        Yes            2                         2   \n",
      "...                      ...          ...                       ...   \n",
      "1095                      No            1                         2   \n",
      "1096                      No            2                         4   \n",
      "1097                      No            0                         0   \n",
      "1098                     Yes            0                         0   \n",
      "1099                      No            0                         0   \n",
      "\n",
      "      social_media_hours   Exercise depressiveness anxiety_interference  \\\n",
      "0                      4  Sometimes          Never                  Yes   \n",
      "1                      3     Rarely   Occasionally                Maybe   \n",
      "2                      2  Sometimes   Occasionally                  Yes   \n",
      "3                      2     Rarely          Never                   No   \n",
      "4                      1     Rarely   Occasionally                   No   \n",
      "...                  ...        ...            ...                  ...   \n",
      "1095                   2     Always   Occasionally                  Yes   \n",
      "1096                   2  Sometimes   Occasionally                  Yes   \n",
      "1097                   0  Sometimes   Occasionally                   No   \n",
      "1098                   0  Sometimes   Occasionally                  Yes   \n",
      "1099                   0     Always         Rarely                   No   \n",
      "\n",
      "     overthinking  concentration_difficulty  online_difficulty_level  \\\n",
      "0          May be                         1                        1   \n",
      "1             Yes                         5                        4   \n",
      "2             Yes                         4                        5   \n",
      "3              No                         2                        3   \n",
      "4             Yes                         3                        3   \n",
      "...           ...                       ...                      ...   \n",
      "1095          Yes                         4                        4   \n",
      "1096          Yes                         2                        2   \n",
      "1097          Yes                         5                        3   \n",
      "1098          Yes                         4                        5   \n",
      "1099           No                         4                        4   \n",
      "\n",
      "      focus_level  health_problems  \n",
      "0               5                1  \n",
      "1               5                1  \n",
      "2               3                1  \n",
      "3               3                0  \n",
      "4               3                1  \n",
      "...           ...              ...  \n",
      "1095            4                0  \n",
      "1096            2                0  \n",
      "1097            4                0  \n",
      "1098            4                1  \n",
      "1099            4                0  \n",
      "\n",
      "[1100 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "#renaming columns\n",
    "final_df.columns = ['Age', 'Educational_level', 'Screening_time', 'Irregular_eating_habits', 'hobby_hours', 'increased_sleeping_hours', 'social_media_hours', 'Exercise', 'depressiveness', 'anxiety_interference', 'overthinking', 'concentration_difficulty', 'online_difficulty_level', 'focus_level', 'health_problems']\n",
    "\n",
    "#view final df\n",
    "print(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Educational_level']= data['Educational_level'].map({'Secondary School':0,'Higher Secondary School':1,'Under Graduate':2,'Post Graduate':3})\n",
    "\n",
    "final_df['Age']=data['Age'].map({'13 to 15 years':0, '16 to 18 years':1, '18 to 22 years':2, '23 to 26 years':3})\n",
    "\n",
    "final_df['Screening_time']=data['Screening_time'].map({'2 to 3 hours':0, '3 to 4 hours':1, '4 to 5 hours':2, '5 to 6 hours':3})\n",
    "\n",
    "final_df['Irregular_eating_habits']=data['Irregular_eating_habits'].map({'No':0,'Yes':1,'May be':2})\n",
    "\n",
    "final_df['Exercise']=data['Exercise'].map({'Never':0, 'Rarely':1, 'Sometimes':2, 'Always':3})\n",
    "\n",
    "final_df['depressiveness']=data['depressiveness'].map({'Never':0, 'Rarely':1, 'Occasionally':2, 'All the time':3})\n",
    "\n",
    "final_df['anxiety_interference']=data['anxiety_interference'].map({'No':0,'Yes':1,'Maybe':2})\n",
    "\n",
    "final_df['overthinking']=data['overthinking'].map({'No':0,'Yes':1,'May be':2})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Irregular_eating_habits</th>\n",
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       "      <th>online_difficulty_level</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Educational_level  Screening_time  Irregular_eating_habits  \\\n",
       "0    1                  1               1                        1   \n",
       "1    1                  1               0                        1   \n",
       "2    1                  1               2                        1   \n",
       "3    1                  1               2                        0   \n",
       "4    1                  1               2                        1   \n",
       "\n",
       "   hobby_hours  increased_sleeping_hours  social_media_hours  Exercise  \\\n",
       "0            1                         4                   4         2   \n",
       "1            1                         2                   3         1   \n",
       "2            0                         2                   2         2   \n",
       "3            1                         2                   2         1   \n",
       "4            2                         2                   1         1   \n",
       "\n",
       "   depressiveness  anxiety_interference  overthinking  \\\n",
       "0               0                     1             2   \n",
       "1               2                     2             1   \n",
       "2               2                     1             1   \n",
       "3               0                     0             0   \n",
       "4               2                     0             1   \n",
       "\n",
       "   concentration_difficulty  online_difficulty_level  focus_level  \\\n",
       "0                         1                        1            5   \n",
       "1                         5                        4            5   \n",
       "2                         4                        5            3   \n",
       "3                         2                        3            3   \n",
       "4                         3                        3            3   \n",
       "\n",
       "   health_problems  \n",
       "0                1  \n",
       "1                1  \n",
       "2                1  \n",
       "3                0  \n",
       "4                1  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# separate independent and dependent features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = final_df.drop('health_problems',axis=1)\n",
    "y = final_df['health_problems']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Irregular_eating_habits</th>\n",
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       "      <th>anxiety_interference</th>\n",
       "      <th>overthinking</th>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <th>focus_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Educational_level  Screening_time  Irregular_eating_habits  \\\n",
       "0    1                  1               1                        1   \n",
       "1    1                  1               0                        1   \n",
       "\n",
       "   hobby_hours  increased_sleeping_hours  social_media_hours  Exercise  \\\n",
       "0            1                         4                   4         2   \n",
       "1            1                         2                   3         1   \n",
       "\n",
       "   depressiveness  anxiety_interference  overthinking  \\\n",
       "0               0                     1             2   \n",
       "1               2                     2             1   \n",
       "\n",
       "   concentration_difficulty  online_difficulty_level  focus_level  \n",
       "0                         1                        1            5  \n",
       "1                         5                        4            5  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       0\n",
       "4       1\n",
       "       ..\n",
       "1095    0\n",
       "1096    0\n",
       "1097    0\n",
       "1098    1\n",
       "1099    0\n",
       "Name: health_problems, Length: 1100, dtype: uint8"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply SelectKBest Algorithm\n",
    "ordered_rank_features=SelectKBest(score_func=chi2,k=14)\n",
    "ordered_feature=ordered_rank_features.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfscores=pd.DataFrame(ordered_feature.scores_,columns = [\"health_problems\"])\n",
    "dfcolumns=pd.DataFrame(x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_rank=pd.concat([dfcolumns,dfscores],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>health_problems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>38.624972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Educational_level</td>\n",
       "      <td>40.724851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Screening_time</td>\n",
       "      <td>43.391514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irregular_eating_habits</td>\n",
       "      <td>219.384983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hobby_hours</td>\n",
       "      <td>42.407008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>increased_sleeping_hours</td>\n",
       "      <td>63.024771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>social_media_hours</td>\n",
       "      <td>17.102861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Exercise</td>\n",
       "      <td>57.230739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>depressiveness</td>\n",
       "      <td>0.001571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anxiety_interference</td>\n",
       "      <td>2.488937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>overthinking</td>\n",
       "      <td>30.643615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>concentration_difficulty</td>\n",
       "      <td>2.797821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>online_difficulty_level</td>\n",
       "      <td>0.873750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>focus_level</td>\n",
       "      <td>3.948066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0  health_problems\n",
       "0                        Age        38.624972\n",
       "1          Educational_level        40.724851\n",
       "2             Screening_time        43.391514\n",
       "3    Irregular_eating_habits       219.384983\n",
       "4                hobby_hours        42.407008\n",
       "5   increased_sleeping_hours        63.024771\n",
       "6         social_media_hours        17.102861\n",
       "7                   Exercise        57.230739\n",
       "8             depressiveness         0.001571\n",
       "9       anxiety_interference         2.488937\n",
       "10              overthinking        30.643615\n",
       "11  concentration_difficulty         2.797821\n",
       "12   online_difficulty_level         0.873750\n",
       "13               focus_level         3.948066"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>health_problems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irregular_eating_habits</td>\n",
       "      <td>219.384983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>increased_sleeping_hours</td>\n",
       "      <td>63.024771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Exercise</td>\n",
       "      <td>57.230739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Screening_time</td>\n",
       "      <td>43.391514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hobby_hours</td>\n",
       "      <td>42.407008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Educational_level</td>\n",
       "      <td>40.724851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>38.624972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>overthinking</td>\n",
       "      <td>30.643615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>social_media_hours</td>\n",
       "      <td>17.102861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>focus_level</td>\n",
       "      <td>3.948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>concentration_difficulty</td>\n",
       "      <td>2.797821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anxiety_interference</td>\n",
       "      <td>2.488937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>online_difficulty_level</td>\n",
       "      <td>0.873750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>depressiveness</td>\n",
       "      <td>0.001571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0  health_problems\n",
       "3    Irregular_eating_habits       219.384983\n",
       "5   increased_sleeping_hours        63.024771\n",
       "7                   Exercise        57.230739\n",
       "2             Screening_time        43.391514\n",
       "4                hobby_hours        42.407008\n",
       "1          Educational_level        40.724851\n",
       "0                        Age        38.624972\n",
       "10              overthinking        30.643615\n",
       "6         social_media_hours        17.102861\n",
       "13               focus_level         3.948066\n",
       "11  concentration_difficulty         2.797821\n",
       "9       anxiety_interference         2.488937\n",
       "12   online_difficulty_level         0.873750\n",
       "8             depressiveness         0.001571"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank.nlargest(14,'health_problems')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model=ExtraTreesClassifier()\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03279583 0.02928271 0.03600283 0.29851619 0.04199241 0.05063906\n",
      " 0.08713514 0.09913526 0.06207796 0.04310165 0.05895256 0.05677393\n",
      " 0.05581584 0.04777862]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)\n",
    "#This technique gives you a score for each feature of your data, the higher the score more relevant it will be ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x237e6e8c700>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranked_features=pd.Series(model.feature_importances_,index=x.columns)\n",
    "ranked_features.nlargest(14).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Educational_level</th>\n",
       "      <th>Screening_time</th>\n",
       "      <th>Irregular_eating_habits</th>\n",
       "      <th>hobby_hours</th>\n",
       "      <th>increased_sleeping_hours</th>\n",
       "      <th>social_media_hours</th>\n",
       "      <th>Exercise</th>\n",
       "      <th>depressiveness</th>\n",
       "      <th>anxiety_interference</th>\n",
       "      <th>overthinking</th>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <th>focus_level</th>\n",
       "      <th>health_problems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990910</td>\n",
       "      <td>0.942445</td>\n",
       "      <td>0.287309</td>\n",
       "      <td>-0.810054</td>\n",
       "      <td>-0.872236</td>\n",
       "      <td>-0.465106</td>\n",
       "      <td>-0.043166</td>\n",
       "      <td>-0.077052</td>\n",
       "      <td>-0.019313</td>\n",
       "      <td>0.172368</td>\n",
       "      <td>0.413106</td>\n",
       "      <td>0.117638</td>\n",
       "      <td>0.424853</td>\n",
       "      <td>0.219973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Educational_level</th>\n",
       "      <td>0.990910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.951342</td>\n",
       "      <td>0.295803</td>\n",
       "      <td>-0.796335</td>\n",
       "      <td>-0.871524</td>\n",
       "      <td>-0.466498</td>\n",
       "      <td>-0.037606</td>\n",
       "      <td>-0.076925</td>\n",
       "      <td>-0.015599</td>\n",
       "      <td>0.159861</td>\n",
       "      <td>0.402548</td>\n",
       "      <td>0.109709</td>\n",
       "      <td>0.404957</td>\n",
       "      <td>0.223023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Screening_time</th>\n",
       "      <td>0.942445</td>\n",
       "      <td>0.951342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276676</td>\n",
       "      <td>-0.784416</td>\n",
       "      <td>-0.837244</td>\n",
       "      <td>-0.435066</td>\n",
       "      <td>-0.027278</td>\n",
       "      <td>-0.078059</td>\n",
       "      <td>-0.037018</td>\n",
       "      <td>0.136313</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.102676</td>\n",
       "      <td>0.349420</td>\n",
       "      <td>0.222121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irregular_eating_habits</th>\n",
       "      <td>0.287309</td>\n",
       "      <td>0.295803</td>\n",
       "      <td>0.276676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.334045</td>\n",
       "      <td>-0.275755</td>\n",
       "      <td>-0.004970</td>\n",
       "      <td>-0.345262</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.018954</td>\n",
       "      <td>0.212677</td>\n",
       "      <td>0.117452</td>\n",
       "      <td>0.142965</td>\n",
       "      <td>0.132529</td>\n",
       "      <td>0.512206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby_hours</th>\n",
       "      <td>-0.810054</td>\n",
       "      <td>-0.796335</td>\n",
       "      <td>-0.784416</td>\n",
       "      <td>-0.334045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.778197</td>\n",
       "      <td>0.391665</td>\n",
       "      <td>0.114401</td>\n",
       "      <td>0.057602</td>\n",
       "      <td>-0.039672</td>\n",
       "      <td>-0.211386</td>\n",
       "      <td>-0.366464</td>\n",
       "      <td>-0.158946</td>\n",
       "      <td>-0.369102</td>\n",
       "      <td>-0.247736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increased_sleeping_hours</th>\n",
       "      <td>-0.872236</td>\n",
       "      <td>-0.871524</td>\n",
       "      <td>-0.837244</td>\n",
       "      <td>-0.275755</td>\n",
       "      <td>0.778197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419470</td>\n",
       "      <td>0.036357</td>\n",
       "      <td>0.100587</td>\n",
       "      <td>-0.011772</td>\n",
       "      <td>-0.177830</td>\n",
       "      <td>-0.403710</td>\n",
       "      <td>-0.051519</td>\n",
       "      <td>-0.388630</td>\n",
       "      <td>-0.259974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social_media_hours</th>\n",
       "      <td>-0.465106</td>\n",
       "      <td>-0.466498</td>\n",
       "      <td>-0.435066</td>\n",
       "      <td>-0.004970</td>\n",
       "      <td>0.391665</td>\n",
       "      <td>0.419470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074793</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>0.074109</td>\n",
       "      <td>-0.015433</td>\n",
       "      <td>-0.216723</td>\n",
       "      <td>-0.133383</td>\n",
       "      <td>-0.218019</td>\n",
       "      <td>0.173165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exercise</th>\n",
       "      <td>-0.043166</td>\n",
       "      <td>-0.037606</td>\n",
       "      <td>-0.027278</td>\n",
       "      <td>-0.345262</td>\n",
       "      <td>0.114401</td>\n",
       "      <td>0.036357</td>\n",
       "      <td>-0.074793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.123529</td>\n",
       "      <td>-0.010443</td>\n",
       "      <td>-0.323161</td>\n",
       "      <td>-0.047309</td>\n",
       "      <td>-0.129501</td>\n",
       "      <td>-0.105904</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depressiveness</th>\n",
       "      <td>-0.077052</td>\n",
       "      <td>-0.076925</td>\n",
       "      <td>-0.078059</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.057602</td>\n",
       "      <td>0.100587</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>-0.123529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169779</td>\n",
       "      <td>0.059452</td>\n",
       "      <td>0.091822</td>\n",
       "      <td>0.294892</td>\n",
       "      <td>0.057593</td>\n",
       "      <td>0.001692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anxiety_interference</th>\n",
       "      <td>-0.019313</td>\n",
       "      <td>-0.015599</td>\n",
       "      <td>-0.037018</td>\n",
       "      <td>0.018954</td>\n",
       "      <td>-0.039672</td>\n",
       "      <td>-0.011772</td>\n",
       "      <td>0.074109</td>\n",
       "      <td>-0.010443</td>\n",
       "      <td>0.169779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155755</td>\n",
       "      <td>-0.011795</td>\n",
       "      <td>0.029885</td>\n",
       "      <td>-0.046020</td>\n",
       "      <td>-0.064038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overthinking</th>\n",
       "      <td>0.172368</td>\n",
       "      <td>0.159861</td>\n",
       "      <td>0.136313</td>\n",
       "      <td>0.212677</td>\n",
       "      <td>-0.211386</td>\n",
       "      <td>-0.177830</td>\n",
       "      <td>-0.015433</td>\n",
       "      <td>-0.323161</td>\n",
       "      <td>0.059452</td>\n",
       "      <td>0.155755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051703</td>\n",
       "      <td>0.091438</td>\n",
       "      <td>0.090949</td>\n",
       "      <td>0.228477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concentration_difficulty</th>\n",
       "      <td>0.413106</td>\n",
       "      <td>0.402548</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.117452</td>\n",
       "      <td>-0.366464</td>\n",
       "      <td>-0.403710</td>\n",
       "      <td>-0.216723</td>\n",
       "      <td>-0.047309</td>\n",
       "      <td>0.091822</td>\n",
       "      <td>-0.011795</td>\n",
       "      <td>0.051703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160357</td>\n",
       "      <td>0.870356</td>\n",
       "      <td>0.088736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_difficulty_level</th>\n",
       "      <td>0.117638</td>\n",
       "      <td>0.109709</td>\n",
       "      <td>0.102676</td>\n",
       "      <td>0.142965</td>\n",
       "      <td>-0.158946</td>\n",
       "      <td>-0.051519</td>\n",
       "      <td>-0.133383</td>\n",
       "      <td>-0.129501</td>\n",
       "      <td>0.294892</td>\n",
       "      <td>0.029885</td>\n",
       "      <td>0.091438</td>\n",
       "      <td>0.160357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132313</td>\n",
       "      <td>0.046270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>focus_level</th>\n",
       "      <td>0.424853</td>\n",
       "      <td>0.404957</td>\n",
       "      <td>0.349420</td>\n",
       "      <td>0.132529</td>\n",
       "      <td>-0.369102</td>\n",
       "      <td>-0.388630</td>\n",
       "      <td>-0.218019</td>\n",
       "      <td>-0.105904</td>\n",
       "      <td>0.057593</td>\n",
       "      <td>-0.046020</td>\n",
       "      <td>0.090949</td>\n",
       "      <td>0.870356</td>\n",
       "      <td>0.132313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_problems</th>\n",
       "      <td>0.219973</td>\n",
       "      <td>0.223023</td>\n",
       "      <td>0.222121</td>\n",
       "      <td>0.512206</td>\n",
       "      <td>-0.247736</td>\n",
       "      <td>-0.259974</td>\n",
       "      <td>0.173165</td>\n",
       "      <td>-0.319802</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>-0.064038</td>\n",
       "      <td>0.228477</td>\n",
       "      <td>0.088736</td>\n",
       "      <td>0.046270</td>\n",
       "      <td>0.105478</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Age  Educational_level  Screening_time  \\\n",
       "Age                       1.000000           0.990910        0.942445   \n",
       "Educational_level         0.990910           1.000000        0.951342   \n",
       "Screening_time            0.942445           0.951342        1.000000   \n",
       "Irregular_eating_habits   0.287309           0.295803        0.276676   \n",
       "hobby_hours              -0.810054          -0.796335       -0.784416   \n",
       "increased_sleeping_hours -0.872236          -0.871524       -0.837244   \n",
       "social_media_hours       -0.465106          -0.466498       -0.435066   \n",
       "Exercise                 -0.043166          -0.037606       -0.027278   \n",
       "depressiveness           -0.077052          -0.076925       -0.078059   \n",
       "anxiety_interference     -0.019313          -0.015599       -0.037018   \n",
       "overthinking              0.172368           0.159861        0.136313   \n",
       "concentration_difficulty  0.413106           0.402548        0.413807   \n",
       "online_difficulty_level   0.117638           0.109709        0.102676   \n",
       "focus_level               0.424853           0.404957        0.349420   \n",
       "health_problems           0.219973           0.223023        0.222121   \n",
       "\n",
       "                          Irregular_eating_habits  hobby_hours  \\\n",
       "Age                                      0.287309    -0.810054   \n",
       "Educational_level                        0.295803    -0.796335   \n",
       "Screening_time                           0.276676    -0.784416   \n",
       "Irregular_eating_habits                  1.000000    -0.334045   \n",
       "hobby_hours                             -0.334045     1.000000   \n",
       "increased_sleeping_hours                -0.275755     0.778197   \n",
       "social_media_hours                      -0.004970     0.391665   \n",
       "Exercise                                -0.345262     0.114401   \n",
       "depressiveness                           0.012160     0.057602   \n",
       "anxiety_interference                     0.018954    -0.039672   \n",
       "overthinking                             0.212677    -0.211386   \n",
       "concentration_difficulty                 0.117452    -0.366464   \n",
       "online_difficulty_level                  0.142965    -0.158946   \n",
       "focus_level                              0.132529    -0.369102   \n",
       "health_problems                          0.512206    -0.247736   \n",
       "\n",
       "                          increased_sleeping_hours  social_media_hours  \\\n",
       "Age                                      -0.872236           -0.465106   \n",
       "Educational_level                        -0.871524           -0.466498   \n",
       "Screening_time                           -0.837244           -0.435066   \n",
       "Irregular_eating_habits                  -0.275755           -0.004970   \n",
       "hobby_hours                               0.778197            0.391665   \n",
       "increased_sleeping_hours                  1.000000            0.419470   \n",
       "social_media_hours                        0.419470            1.000000   \n",
       "Exercise                                  0.036357           -0.074793   \n",
       "depressiveness                            0.100587            0.004888   \n",
       "anxiety_interference                     -0.011772            0.074109   \n",
       "overthinking                             -0.177830           -0.015433   \n",
       "concentration_difficulty                 -0.403710           -0.216723   \n",
       "online_difficulty_level                  -0.051519           -0.133383   \n",
       "focus_level                              -0.388630           -0.218019   \n",
       "health_problems                          -0.259974            0.173165   \n",
       "\n",
       "                          Exercise  depressiveness  anxiety_interference  \\\n",
       "Age                      -0.043166       -0.077052             -0.019313   \n",
       "Educational_level        -0.037606       -0.076925             -0.015599   \n",
       "Screening_time           -0.027278       -0.078059             -0.037018   \n",
       "Irregular_eating_habits  -0.345262        0.012160              0.018954   \n",
       "hobby_hours               0.114401        0.057602             -0.039672   \n",
       "increased_sleeping_hours  0.036357        0.100587             -0.011772   \n",
       "social_media_hours       -0.074793        0.004888              0.074109   \n",
       "Exercise                  1.000000       -0.123529             -0.010443   \n",
       "depressiveness           -0.123529        1.000000              0.169779   \n",
       "anxiety_interference     -0.010443        0.169779              1.000000   \n",
       "overthinking             -0.323161        0.059452              0.155755   \n",
       "concentration_difficulty -0.047309        0.091822             -0.011795   \n",
       "online_difficulty_level  -0.129501        0.294892              0.029885   \n",
       "focus_level              -0.105904        0.057593             -0.046020   \n",
       "health_problems          -0.319802        0.001692             -0.064038   \n",
       "\n",
       "                          overthinking  concentration_difficulty  \\\n",
       "Age                           0.172368                  0.413106   \n",
       "Educational_level             0.159861                  0.402548   \n",
       "Screening_time                0.136313                  0.413807   \n",
       "Irregular_eating_habits       0.212677                  0.117452   \n",
       "hobby_hours                  -0.211386                 -0.366464   \n",
       "increased_sleeping_hours     -0.177830                 -0.403710   \n",
       "social_media_hours           -0.015433                 -0.216723   \n",
       "Exercise                     -0.323161                 -0.047309   \n",
       "depressiveness                0.059452                  0.091822   \n",
       "anxiety_interference          0.155755                 -0.011795   \n",
       "overthinking                  1.000000                  0.051703   \n",
       "concentration_difficulty      0.051703                  1.000000   \n",
       "online_difficulty_level       0.091438                  0.160357   \n",
       "focus_level                   0.090949                  0.870356   \n",
       "health_problems               0.228477                  0.088736   \n",
       "\n",
       "                          online_difficulty_level  focus_level  \\\n",
       "Age                                      0.117638     0.424853   \n",
       "Educational_level                        0.109709     0.404957   \n",
       "Screening_time                           0.102676     0.349420   \n",
       "Irregular_eating_habits                  0.142965     0.132529   \n",
       "hobby_hours                             -0.158946    -0.369102   \n",
       "increased_sleeping_hours                -0.051519    -0.388630   \n",
       "social_media_hours                      -0.133383    -0.218019   \n",
       "Exercise                                -0.129501    -0.105904   \n",
       "depressiveness                           0.294892     0.057593   \n",
       "anxiety_interference                     0.029885    -0.046020   \n",
       "overthinking                             0.091438     0.090949   \n",
       "concentration_difficulty                 0.160357     0.870356   \n",
       "online_difficulty_level                  1.000000     0.132313   \n",
       "focus_level                              0.132313     1.000000   \n",
       "health_problems                          0.046270     0.105478   \n",
       "\n",
       "                          health_problems  \n",
       "Age                              0.219973  \n",
       "Educational_level                0.223023  \n",
       "Screening_time                   0.222121  \n",
       "Irregular_eating_habits          0.512206  \n",
       "hobby_hours                     -0.247736  \n",
       "increased_sleeping_hours        -0.259974  \n",
       "social_media_hours               0.173165  \n",
       "Exercise                        -0.319802  \n",
       "depressiveness                   0.001692  \n",
       "anxiety_interference            -0.064038  \n",
       "overthinking                     0.228477  \n",
       "concentration_difficulty         0.088736  \n",
       "online_difficulty_level          0.046270  \n",
       "focus_level                      0.105478  \n",
       "health_problems                  1.000000  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x237e53a98b0>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr=final_df.corr()\n",
    "top_features=corr.index\n",
    "plt.figure(figsize=(14,14))\n",
    "sns.heatmap(final_df[top_features].corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutual_info=mutual_info_classif(x,y)\n",
    "#tells which features are more correlated to our dependent feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Irregular_eating_habits     0.265413\n",
       "Exercise                    0.068491\n",
       "hobby_hours                 0.057484\n",
       "increased_sleeping_hours    0.042230\n",
       "social_media_hours          0.038330\n",
       "Screening_time              0.030450\n",
       "Educational_level           0.029866\n",
       "Age                         0.029539\n",
       "overthinking                0.022198\n",
       "concentration_difficulty    0.014037\n",
       "online_difficulty_level     0.011480\n",
       "focus_level                 0.001684\n",
       "anxiety_interference        0.000000\n",
       "depressiveness              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_data=pd.Series(mutual_info,index=x.columns)\n",
    "mutual_data.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# split the data into training and testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(825, 14) (825,)\n",
      "(275, 14) (275,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix,accuracy_score\n",
    "#prediction\n",
    "tr_predict = lr_model.predict(x_train)\n",
    "ts_predict = lr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.793939393939394\n",
      "Testing Accuracy: 0.8218181818181818\n"
     ]
    }
   ],
   "source": [
    "#check accuracy scpre\n",
    "print('Training Accuracy:',accuracy_score(y_train,tr_predict))\n",
    "print('Testing Accuracy:',accuracy_score(y_test,ts_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x237e6e8c940>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lr_model,x_train,y_train,values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[129,  26],\n",
       "       [ 23,  97]], dtype=int64)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix=metrics.confusion_matrix(y_test,ts_predict)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8218181818181818\n",
      "Precision: 0.7886178861788617\n",
      "Recall: 0.8083333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,ts_predict))\n",
    "print(\"Precision:\",metrics.precision_score(y_test,ts_predict))\n",
    "print(\"Recall:\",metrics.recall_score(y_test,ts_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting AUC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = lr_model.predict_proba(x_test)[::,1]\n",
    "fpr,tpr,_ = metrics.roc_curve(y_test,y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test,y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}