Depression-Anxiety-Stress-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f5b285a",
   "metadata": {},
   "source": [
    "# Depression, Anxiety, & Stress estimation using AI and Data Science\n",
    "*A project made for HealthCoder-2023*\n",
    "\n",
    "**Team Members:**\n",
    "   - Shraddha Pattnaik (B420047)\n",
    "   - Soumyajeet Muni (B420051)\n",
    "   - Swastik Babu (B420055)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4ac578",
   "metadata": {},
   "source": [
    "## Import Necessaries Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5a533a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39775 entries, 0 to 39774\n",
      "Columns: 172 entries, Q1A to major\n",
      "dtypes: int64(170), object(2)\n",
      "memory usage: 52.2+ MB\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from scipy.stats import mode\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(r'C:\\Users\\Dell\\Documents\\dsaiProject\\data.csv',sep='\\t')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca96a2ba",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "In the dataset, VCL6, VCL9, VCL12 are not real words and are used for validity check.\n",
    "1. Create a new column in the DataFrame called 'wrongansw' and initializes it to zero for all rows.\n",
    "2. Update the 'wrongansw' column based on a condition:\n",
    "    It uses the where method of the DataFrame to apply a condition where df[''] equals 0. \n",
    "    If the condition is true, then the 'wrongansw' column remains unchanged.\n",
    "    If the condition is false, then the 'wrongansw' column is incremented by 1.\n",
    "3. Use answers that have checked at least 2 of the words\n",
    "4. Drop 'wrongansw' and 'VCL' columns\n",
    "5. Drop 'major' columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a581352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34583\n",
       "1     4044\n",
       "2      822\n",
       "3      326\n",
       "Name: wrongansw, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wrongansw'] = 0\n",
    "df['wrongansw'] = df['wrongansw'].where(df['VCL6']== 0, df['wrongansw'] + 1)\n",
    "df['wrongansw'] = df['wrongansw'].where(df['VCL9']== 0, df['wrongansw'] + 1)\n",
    "df['wrongansw'] = df['wrongansw'].where(df['VCL12']== 0, df['wrongansw'] + 1)\n",
    "\n",
    "df.wrongansw.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "564c41eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1148, 172)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['wrongansw'].isin([2, 3])]\n",
    "df = df.drop(columns='wrongansw')\n",
    "df.head(3)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af90c47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1148, 156)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the validity check columns\n",
    "vcls = []\n",
    "for i in range(1, 17):\n",
    "    vcls.append('VCL' + str(i))\n",
    "    \n",
    "df = df.drop(columns=vcls)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b30ac72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Columns:  Index(['country', 'major'], dtype='object')\n",
      "country     68\n",
      "major      366\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "categorical = df.select_dtypes('object').columns\n",
    "\n",
    "print('Categorical Columns: ', df[categorical].columns)\n",
    "print(df[categorical].nunique())\n",
    "\n",
    "# remove 'major' column\n",
    "df = df.drop(['major'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f5dad9",
   "metadata": {},
   "source": [
    "## Labelling the questions that are for specific mental disorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da0c9b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "DASS_keys = {\n",
    "    'Depression': [3, 5, 10, 13, 16, 17, 21, 24, 26, 31, 34, 37, 38, 42],\n",
    "    'Anxiety': [2, 4, 7, 9, 15, 19, 20, 23, 25, 28, 30, 36, 40, 41],\n",
    "    'Stress': [1, 6, 8, 11, 12, 14, 18, 22, 27, 29, 32, 33, 35, 39]\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e2b0918f",
   "metadata": {},
   "source": [
    "## Data Collection\n",
    "The scores for anxiety, depression and stress were calculated by adding the values associated with the answers to each question of the particular class. Once the final scores had been calculated, they were labelled according to severity, i.e. Extremely Severe, Severe, Moderate, Mild and Normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76dadc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "depr = []\n",
    "for i in DASS_keys[\"Depression\"]:\n",
    "    depr.append('Q' + str(i) + 'A')\n",
    "    \n",
    "anx = []\n",
    "for i in DASS_keys[\"Anxiety\"]:\n",
    "    anx.append('Q' + str(i) + 'A')\n",
    "\n",
    "stre = []\n",
    "for i in DASS_keys[\"Stress\"]:\n",
    "    stre.append('Q' + str(i) + 'A')\n",
    "\n",
    "# making a new dataframe for each disorder \n",
    "\n",
    "df_depr=df.filter(depr) \n",
    "df_anx=df.filter(anx)\n",
    "df_stre=df.filter(stre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d0aa6ef",
   "metadata": {},
   "outputs": [
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       "    Q1A  Q1I   Q1E  Q2A  Q2I   Q2E  Q3A  Q3I    Q3E  Q4A  ...  hand  religion  \\\n",
       "26    3    1  4820    3   39  1109    3    8   1833    3  ...     1         7   \n",
       "31    1   25  3671    1   26  5743    1   13   3154    0  ...     1         2   \n",
       "57    1   27  3827    2   33  6293    1   22  12153    2  ...     1         1   \n",
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       "87    2    4  8405    1   29  5819    3   32   1925    3  ...     1         1   \n",
       "\n",
       "    orientation  race  voted  married  familysize  ScoresDepr  ScoresAnx  \\\n",
       "26            1    60      2        1           3          38         41   \n",
       "31            1    60      2        1           3          30          8   \n",
       "57            1    60      1        1           2          13         15   \n",
       "81            1    70      2        1           6          20         10   \n",
       "87            1    30      2        1           3          42         36   \n",
       "\n",
       "    ScoresStre  \n",
       "26          41  \n",
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       "57          20  \n",
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       "\n",
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     },
     "execution_count": 8,
     "metadata": {},
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   "source": [
    "disorders = [depr, anx, stre]\n",
    "\n",
    "def scores(df): # adds new columns to store total scores for each        \n",
    "    df[\"ScoresDepr\"] = df[depr].sum(axis=1) \n",
    "    df[\"ScoresAnx\"] = df[anx].sum(axis=1)\n",
    "    df[\"ScoresStre\"] = df[stre].sum(axis=1)\n",
    "    return df\n",
    "\n",
    "for i in disorders:\n",
    "        df[i] -= 1 # to get the scores\n",
    "        \n",
    "df = scores(df)\n",
    "        \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e136ebfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CATEGORY: DEPRESSION\n",
    "CategoryDepr=[]\n",
    "\n",
    "for i in df['ScoresDepr']:\n",
    "    if i in range(0,10):\n",
    "        CategoryDepr.append('Normal')\n",
    "    elif i in range(10,14):\n",
    "        CategoryDepr.append('Minimal')\n",
    "    elif i in range(14,21):\n",
    "        CategoryDepr.append('Moderate')\n",
    "    elif i in range(21,28):\n",
    "        CategoryDepr.append('Severe')\n",
    "    else:\n",
    "        CategoryDepr.append('Extremely Severe')\n",
    "        \n",
    "df['CategoryDepr']= CategoryDepr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbcd6f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CATEGORY: ANXIETY\n",
    "CategoryAnx=[]\n",
    "\n",
    "for i in df['ScoresAnx']:\n",
    "    if i in range(0,8):\n",
    "        CategoryAnx.append('Normal')\n",
    "    elif i in range(8,10):\n",
    "        CategoryAnx.append('Minimal')\n",
    "    elif i in range(10,15):\n",
    "        CategoryAnx.append('Moderate')\n",
    "    elif i in range(15,20):\n",
    "        CategoryAnx.append('Severe')\n",
    "    else:\n",
    "        CategoryAnx.append('Extremely Severe')\n",
    "        \n",
    "df['CategoryAnx']= CategoryAnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63b04644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CATEGORY: STRESS\n",
    "CategoryStre=[]\n",
    "\n",
    "for i in df['ScoresStre']:\n",
    "    if i in range(0,15):\n",
    "        CategoryStre.append('Normal')\n",
    "    elif i in range(15,19):\n",
    "        CategoryStre.append('Minimal')\n",
    "    elif i in range(19,26):\n",
    "        CategoryStre.append('Moderate')\n",
    "    elif i in range(26,34):\n",
    "        CategoryStre.append('Severe')\n",
    "    else:\n",
    "        CategoryStre.append('Extremely Severe')\n",
    "        \n",
    "df['CategoryStre']= CategoryStre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46e32acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# checking for duplicates\n",
    "df.isnull().sum()\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "631ffa0b",
   "metadata": {},
   "outputs": [
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       "      <td>3154</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Minimal</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>3827</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>6293</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>12153</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>Minimal</td>\n",
       "      <td>Severe</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2205</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>3320</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2803</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8405</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>5819</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>1925</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Extremely Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39621</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3293</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2830</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3594</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Severe</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39643</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>3731</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1623</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2876</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>Severe</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39644</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>412</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11824</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39734</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>3099</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4454</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11688</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Extremely Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39750</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2892</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1848</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2395</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Extremely Severe</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1148 rows × 161 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1A  Q1I   Q1E  Q2A  Q2I    Q2E  Q3A  Q3I    Q3E  Q4A  ...  race  \\\n",
       "26       3    1  4820    3   39   1109    3    8   1833    3  ...    60   \n",
       "31       1   25  3671    1   26   5743    1   13   3154    0  ...    60   \n",
       "57       1   27  3827    2   33   6293    1   22  12153    2  ...    60   \n",
       "81       3   14  2205    0   25   3320    1   40   2803    0  ...    70   \n",
       "87       2    4  8405    1   29   5819    3   32   1925    3  ...    30   \n",
       "...    ...  ...   ...  ...  ...    ...  ...  ...    ...  ...  ...   ...   \n",
       "39621    2   10  3293    0   16   2830    2   12   3594    2  ...    70   \n",
       "39643    1   18  3731    0   10   1623    2   13   2876    0  ...    10   \n",
       "39644    0   25   412    0    2  11824    0   29    349    0  ...    30   \n",
       "39734    3   37  3099    1   21   4454    3    1  11688    0  ...    60   \n",
       "39750    2   15  2892    3   24   1848    3   12   2395    3  ...    10   \n",
       "\n",
       "       voted  married  familysize  ScoresDepr  ScoresAnx  ScoresStre  \\\n",
       "26         2        1           3          38         41          41   \n",
       "31         2        1           3          30          8           7   \n",
       "57         1        1           2          13         15          20   \n",
       "81         2        1           6          20         10          31   \n",
       "87         2        1           3          42         36          39   \n",
       "...      ...      ...         ...         ...        ...         ...   \n",
       "39621      1        1           5          21         11          10   \n",
       "39643      1        1           3          23          7          11   \n",
       "39644      1        0           7           0          1           3   \n",
       "39734      1        3           3          42         26          41   \n",
       "39750      2        1           5          41         38          32   \n",
       "\n",
       "           CategoryDepr       CategoryAnx      CategoryStre  \n",
       "26     Extremely Severe  Extremely Severe  Extremely Severe  \n",
       "31     Extremely Severe           Minimal            Normal  \n",
       "57              Minimal            Severe          Moderate  \n",
       "81             Moderate          Moderate            Severe  \n",
       "87     Extremely Severe  Extremely Severe  Extremely Severe  \n",
       "...                 ...               ...               ...  \n",
       "39621            Severe          Moderate            Normal  \n",
       "39643            Severe            Normal            Normal  \n",
       "39644            Normal            Normal            Normal  \n",
       "39734  Extremely Severe  Extremely Severe  Extremely Severe  \n",
       "39750  Extremely Severe  Extremely Severe            Severe  \n",
       "\n",
       "[1148 rows x 161 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b138aea",
   "metadata": {},
   "source": [
    "## Data Classification Models for Depression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7a816ff5",
   "metadata": {},
   "source": [
    "### Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0b3f393",
   "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>source</th>\n",
       "      <th>TIPI1</th>\n",
       "      <th>TIPI2</th>\n",
       "      <th>TIPI3</th>\n",
       "      <th>TIPI4</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>TIPI6</th>\n",
       "      <th>TIPI7</th>\n",
       "      <th>TIPI8</th>\n",
       "      <th>TIPI9</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>ScoresDepr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    source  TIPI1  TIPI2  TIPI3  TIPI4  TIPI5  TIPI6  TIPI7  TIPI8  TIPI9  \\\n",
       "26       2      4      1      7      7      7      2      6      5      1   \n",
       "31       2      5      5      5      5      7      7      7      5      5   \n",
       "57       0      2      4      1      5      6      4      3      6      6   \n",
       "81       2      7      6      3      6      4      1      7      5      3   \n",
       "87       2      1      3      5      6      6      7      2      7      1   \n",
       "\n",
       "    ...  gender  age  hand  religion  orientation  race  voted  married  \\\n",
       "26  ...       2   15     1         7            1    60      2        1   \n",
       "31  ...       1   18     1         2            1    60      2        1   \n",
       "57  ...       2   21     1         1            1    60      1        1   \n",
       "81  ...       2   14     1         4            1    70      2        1   \n",
       "87  ...       1   17     1         1            1    30      2        1   \n",
       "\n",
       "    familysize  ScoresDepr  \n",
       "26           3          38  \n",
       "31           3          30  \n",
       "57           2          13  \n",
       "81           6          20  \n",
       "87           3          42  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df['CategoryDepr']\n",
    "X = df.drop(columns=['Q2A','Q4A','Q7A','Q9A','Q15A','Q19A','Q20A','Q23A','Q25A','Q28A','Q30A','Q36A','Q40A','Q41A','introelapse','testelapse', 'surveyelapse','engnat','CategoryAnx', 'CategoryDepr', 'CategoryStre','country', 'ScoresAnx' ,'ScoresStre','screensize','uniquenetworklocation','Q1A' ,'Q1I', 'Q1E', 'Q2I', 'Q2E', 'Q3A', 'Q3I', 'Q3E','Q4I','Q4E', 'Q5E', 'Q5A' ,'Q5I', 'Q6E', 'Q6A' ,'Q6I', 'Q7E' ,'Q7I', 'Q8E','Q8A' ,'Q8I', 'Q9E', 'Q9I', 'Q10E', 'Q10A', 'Q10I', 'Q11E', 'Q11A' ,'Q11I', 'Q12E', 'Q12A' ,'Q12I', 'Q13E', 'Q13A' ,'Q13I', 'Q14E', 'Q14A' ,'Q14I', 'Q15E', 'Q15I', 'Q16E', 'Q16A' ,'Q16I', 'Q17E', 'Q17A' ,'Q17I', 'Q18E', 'Q18A' ,'Q18I', 'Q19I', 'Q19E',  'Q20I', 'Q20E', 'Q21A', 'Q21I', 'Q21E', 'Q22A', 'Q22I','Q22E', 'Q23I','Q23E','Q24A','Q24I','Q24E', 'Q25I', 'Q25E', 'Q26A', 'Q26I', 'Q26E', 'Q27A', 'Q27I', 'Q27E', 'Q28I', 'Q28E', 'Q29A', 'Q29I', 'Q29E',  'Q30I', 'Q30E', 'Q31A', 'Q31I', 'Q31E', 'Q32A', 'Q32I', 'Q32E', 'Q33A', 'Q33I', 'Q33E', 'Q34A', 'Q34I', 'Q34E', 'Q35A', 'Q35I', 'Q35E', 'Q36I', 'Q36E', 'Q37A', 'Q37I', 'Q37E', 'Q38A', 'Q38I', 'Q38E', 'Q39A', 'Q39I', 'Q39E', 'Q40I', 'Q40E', 'Q41I', 'Q41E', 'Q42A', 'Q42I', 'Q42E'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c1082ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c7508d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7978277",
   "metadata": {},
   "source": [
    "### Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc0acde5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN Model - Depression: 0.59930\n",
      "Confusion Matrix of KNN Model - Depression: \n",
      " [[108   0   0   7   1]\n",
      " [  2   0   1  13   0]\n",
      " [ 29   0   2   7   0]\n",
      " [  9   0   5  62   0]\n",
      " [ 36   0   1   4   0]]\n",
      "Confusion Matrix of KNN Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.59      0.93      0.72       116\n",
      "         Minimal       0.00      0.00      0.00        16\n",
      "        Moderate       0.22      0.05      0.09        38\n",
      "          Normal       0.67      0.82      0.73        76\n",
      "          Severe       0.00      0.00      0.00        41\n",
      "\n",
      "        accuracy                           0.60       287\n",
      "       macro avg       0.30      0.36      0.31       287\n",
      "    weighted avg       0.44      0.60      0.50       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# KNN Model for Depression Classification\n",
    "\n",
    "# Create a KNN classifier with k (number of neighbors)\n",
    "knn = KNeighborsClassifier(n_neighbors=95)\n",
    "\n",
    "# Fit the model to the training data\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = knn.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_knn = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of KNN Model - Depression: {:.5f}'.format(accuracy_knn))\n",
    "print('Confusion Matrix of KNN Model - Depression: \\n', conf_matrix)\n",
    "print('Confusion Matrix of KNN Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37f4893d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM Model - Depression: 0.59930\n",
      "Confusion Matrix of SVM Model - Depression: \n",
      " [[108   0   0   7   1]\n",
      " [  2   0   1  13   0]\n",
      " [ 29   0   2   7   0]\n",
      " [  9   0   5  62   0]\n",
      " [ 36   0   1   4   0]]\n",
      "Confusion Matrix of SVM Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.59      0.93      0.72       116\n",
      "         Minimal       0.00      0.00      0.00        16\n",
      "        Moderate       0.22      0.05      0.09        38\n",
      "          Normal       0.67      0.82      0.73        76\n",
      "          Severe       0.00      0.00      0.00        41\n",
      "\n",
      "        accuracy                           0.60       287\n",
      "       macro avg       0.30      0.36      0.31       287\n",
      "    weighted avg       0.44      0.60      0.50       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SVC Model for Depression Classification\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "svm_model.fit(X_train, Y_train)\n",
    "\n",
    "preds = svm_model.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_svc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of SVM Model - Depression: {:.5f}'.format(accuracy_svc))\n",
    "print('Confusion Matrix of SVM Model - Depression: \\n', conf_matrix)\n",
    "print('Confusion Matrix of SVM Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9ab4aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of NB Model - Depression: 0.94425\n",
      "Confusion Matrix of NB Model - Depression: \n",
      " [[113   0   0   0   3]\n",
      " [  0  12   1   3   0]\n",
      " [  0   0  37   0   1]\n",
      " [  0   5   0  71   0]\n",
      " [  2   0   1   0  38]]\n",
      "Classification Report of NB Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.98      0.97      0.98       116\n",
      "         Minimal       0.71      0.75      0.73        16\n",
      "        Moderate       0.95      0.97      0.96        38\n",
      "          Normal       0.96      0.93      0.95        76\n",
      "          Severe       0.90      0.93      0.92        41\n",
      "\n",
      "        accuracy                           0.94       287\n",
      "       macro avg       0.90      0.91      0.91       287\n",
      "    weighted avg       0.95      0.94      0.94       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes Model for Depression Classification\n",
    "\n",
    "clfNB = GaussianNB()\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfNB.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfNB.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_nb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of NB Model - Depression: {:.5f}'.format(accuracy_nb))\n",
    "print('Confusion Matrix of NB Model - Depression: \\n', conf_matrix)\n",
    "print('Classification Report of NB Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e1366e2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RFC Model - Depression: 0.97561\n",
      "Confusion Matrix of RFC Model - Depression: \n",
      " [[116   0   0   0   0]\n",
      " [  0  12   3   1   0]\n",
      " [  0   1  37   0   0]\n",
      " [  0   0   2  74   0]\n",
      " [  0   0   0   0  41]]\n",
      "Classification report of RFC Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00       116\n",
      "         Minimal       0.92      0.75      0.83        16\n",
      "        Moderate       0.88      0.97      0.93        38\n",
      "          Normal       0.99      0.97      0.98        76\n",
      "          Severe       1.00      1.00      1.00        41\n",
      "\n",
      "        accuracy                           0.98       287\n",
      "       macro avg       0.96      0.94      0.95       287\n",
      "    weighted avg       0.98      0.98      0.98       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier Model for Depression Classification\n",
    "\n",
    "clfRFC = RandomForestClassifier(n_estimators=110, random_state=110)\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfRFC.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfRFC.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_rfc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "\n",
    "print('Accuracy of RFC Model - Depression: {:.5f}'.format(accuracy_rfc))\n",
    "print('Confusion Matrix of RFC Model - Depression: \\n', conf_matrix)\n",
    "print('Classification report of RFC Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "718b3661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGB Model - Depression: 1.00000\n",
      "Confusion Matrix of XGB Model - Depression: \n",
      " [[116   0   0   0   0]\n",
      " [  0  16   0   0   0]\n",
      " [  0   0  38   0   0]\n",
      " [  0   0   0  76   0]\n",
      " [  0   0   0   0  41]]\n",
      "Classification report of XGB Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00       116\n",
      "         Minimal       1.00      1.00      1.00        16\n",
      "        Moderate       1.00      1.00      1.00        38\n",
      "          Normal       1.00      1.00      1.00        76\n",
      "          Severe       1.00      1.00      1.00        41\n",
      "\n",
      "        accuracy                           1.00       287\n",
      "       macro avg       1.00      1.00      1.00       287\n",
      "    weighted avg       1.00      1.00      1.00       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# XG Boost Classifier For Depression Classifier\n",
    "xgb_model = XGBClassifier()\n",
    "le = LabelEncoder()\n",
    "\n",
    "xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "\n",
    "Y_pred = xgb_model.predict(X_test)\n",
    "Y_pred = le.inverse_transform(Y_pred)\n",
    "\n",
    "accuracy_xgb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of XGB Model - Depression: {:.5f}'.format(accuracy_xgb))\n",
    "print('Confusion Matrix of XGB Model - Depression: \\n', conf_matrix)\n",
    "print('Classification report of XGB Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1463a42b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Combined Model - Depression: 0.99303\n",
      "Confusion Matrix of Combined Model - Depression: \n",
      " [[116   0   0   0   0]\n",
      " [  0  15   0   1   0]\n",
      " [  0   0  38   0   0]\n",
      " [  0   1   0  75   0]\n",
      " [  0   0   0   0  41]]\n",
      "Classification report of Combined Model - Depression: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00       116\n",
      "         Minimal       0.94      0.94      0.94        16\n",
      "        Moderate       1.00      1.00      1.00        38\n",
      "          Normal       0.99      0.99      0.99        76\n",
      "          Severe       1.00      1.00      1.00        41\n",
      "\n",
      "        accuracy                           0.99       287\n",
      "       macro avg       0.98      0.98      0.98       287\n",
      "    weighted avg       0.99      0.99      0.99       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combining Naive Bayes, XG Boost and Random Forest\n",
    "\n",
    "# Training the models on whole data\n",
    "final_xgb_model = XGBClassifier()\n",
    "final_nb_model = GaussianNB()\n",
    "final_rfc_model = RandomForestClassifier(n_estimators=110, random_state=110)\n",
    "\n",
    "\n",
    "final_xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "final_nb_model.fit(X_train, Y_train)\n",
    "final_rfc_model.fit(X_train, Y_train)\n",
    "\n",
    "# Making prediction by take mode of predictions made by all the classifiers\n",
    "xgb_preds = final_xgb_model.predict(X_test)\n",
    "xgb_preds = le.inverse_transform(xgb_preds)\n",
    "\n",
    "nb_preds = final_nb_model.predict(X_test)\n",
    "rfc_preds = final_rfc_model.predict(X_test)\n",
    "\n",
    "final_preds = [mode([i,j,k])[0][0] for i,j, k in zip(xgb_preds, nb_preds, rfc_preds)]\n",
    "\n",
    "accuracy_combined = accuracy_score(Y_test, final_preds)\n",
    "conf_matrix = confusion_matrix(Y_test, final_preds)\n",
    "classif_report = classification_report(Y_test, final_preds)\n",
    "\n",
    "print('Accuracy of Combined Model - Depression: {:.5f}'.format(accuracy_combined))\n",
    "print('Confusion Matrix of Combined Model - Depression: \\n', conf_matrix)\n",
    "print('Classification report of Combined Model - Depression: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e6c54de",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_depression = [accuracy_knn, accuracy_svc, accuracy_nb, accuracy_rfc, accuracy_xgb, accuracy_combined]\n",
    "\n",
    "depression_analysis = pd.DataFrame({\n",
    "                        'Models' : ['KNN', 'SVC', 'Naive Bayes', 'Random Forest', 'XG Boost', 'Combined Model'],\n",
    "                        'Accuracy_Depression': [x * 100 for x in accuracy_depression]\n",
    "                    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e4824c",
   "metadata": {},
   "source": [
    "## Data Classification Models for Anxiety"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ffaccf",
   "metadata": {},
   "source": [
    "### Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "36560bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>TIPI1</th>\n",
       "      <th>TIPI2</th>\n",
       "      <th>TIPI3</th>\n",
       "      <th>TIPI4</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>TIPI6</th>\n",
       "      <th>TIPI7</th>\n",
       "      <th>TIPI8</th>\n",
       "      <th>TIPI9</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>ScoresAnx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
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       "      <td>7</td>\n",
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       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>57</th>\n",
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       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>17</td>\n",
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       "      <td>30</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    source  TIPI1  TIPI2  TIPI3  TIPI4  TIPI5  TIPI6  TIPI7  TIPI8  TIPI9  \\\n",
       "26       2      4      1      7      7      7      2      6      5      1   \n",
       "31       2      5      5      5      5      7      7      7      5      5   \n",
       "57       0      2      4      1      5      6      4      3      6      6   \n",
       "81       2      7      6      3      6      4      1      7      5      3   \n",
       "87       2      1      3      5      6      6      7      2      7      1   \n",
       "\n",
       "    ...  gender  age  hand  religion  orientation  race  voted  married  \\\n",
       "26  ...       2   15     1         7            1    60      2        1   \n",
       "31  ...       1   18     1         2            1    60      2        1   \n",
       "57  ...       2   21     1         1            1    60      1        1   \n",
       "81  ...       2   14     1         4            1    70      2        1   \n",
       "87  ...       1   17     1         1            1    30      2        1   \n",
       "\n",
       "    familysize  ScoresAnx  \n",
       "26           3         41  \n",
       "31           3          8  \n",
       "57           2         15  \n",
       "81           6         10  \n",
       "87           3         36  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df['CategoryAnx']\n",
    "X = df.drop(columns=['Q2A','Q4A','Q7A','Q9A','Q15A','Q19A','Q20A','Q23A','Q25A','Q28A','Q30A','Q36A','Q40A','Q41A','introelapse','testelapse', 'surveyelapse','engnat','CategoryAnx','CategoryDepr', 'CategoryStre','country', 'ScoresDepr' ,'ScoresStre','screensize','uniquenetworklocation','Q1A' ,'Q1I', 'Q1E', 'Q2I', 'Q2E', 'Q3A', 'Q3I', 'Q3E','Q4I','Q4E', 'Q5E', 'Q5A' ,'Q5I', 'Q6E', 'Q6A' ,'Q6I', 'Q7E' ,'Q7I', 'Q8E','Q8A' ,'Q8I', 'Q9E', 'Q9I', 'Q10E', 'Q10A', 'Q10I', 'Q11E', 'Q11A' ,'Q11I', 'Q12E', 'Q12A' ,'Q12I', 'Q13E', 'Q13A' ,'Q13I', 'Q14E', 'Q14A' ,'Q14I', 'Q15E', 'Q15I', 'Q16E', 'Q16A' ,'Q16I', 'Q17E', 'Q17A' ,'Q17I', 'Q18E', 'Q18A' ,'Q18I', 'Q19I', 'Q19E',  'Q20I', 'Q20E', 'Q21A', 'Q21I', 'Q21E', 'Q22A', 'Q22I','Q22E', 'Q23I','Q23E','Q24A','Q24I','Q24E', 'Q25I', 'Q25E', 'Q26A', 'Q26I', 'Q26E', 'Q27A', 'Q27I', 'Q27E', 'Q28I', 'Q28E', 'Q29A', 'Q29I', 'Q29E',  'Q30I', 'Q30E', 'Q31A', 'Q31I', 'Q31E', 'Q32A', 'Q32I', 'Q32E', 'Q33A', 'Q33I', 'Q33E', 'Q34A', 'Q34I', 'Q34E', 'Q35A', 'Q35I', 'Q35E', 'Q36I', 'Q36E', 'Q37A', 'Q37I', 'Q37E', 'Q38A', 'Q38I', 'Q38E', 'Q39A', 'Q39I', 'Q39E', 'Q40I', 'Q40E', 'Q41I', 'Q41E', 'Q42A', 'Q42I', 'Q42E'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd6768ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "535405f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1e0659",
   "metadata": {},
   "source": [
    "### Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "762b920d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN Model - Anxiety: 0.63066\n",
      "Confusion Matrix of KNN Model - Anxiety: \n",
      " [[112   0   0   0   0]\n",
      " [  9   0   0  11   0]\n",
      " [ 24   0   0  10   0]\n",
      " [ 17   0   0  69   0]\n",
      " [ 35   0   0   0   0]]\n",
      "Confusion Matrix of KNN Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.57      1.00      0.72       112\n",
      "         Minimal       0.00      0.00      0.00        20\n",
      "        Moderate       0.00      0.00      0.00        34\n",
      "          Normal       0.77      0.80      0.78        86\n",
      "          Severe       0.00      0.00      0.00        35\n",
      "\n",
      "        accuracy                           0.63       287\n",
      "       macro avg       0.27      0.36      0.30       287\n",
      "    weighted avg       0.45      0.63      0.52       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# KNN Classification\n",
    "# Create a KNN classifier with k (number of neighbors)\n",
    "knn = KNeighborsClassifier(n_neighbors=95)\n",
    "\n",
    "# Fit the model to the training data\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = knn.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_knn = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of KNN Model - Anxiety: {:.5f}'.format(accuracy_knn))\n",
    "print('Confusion Matrix of KNN Model - Anxiety: \\n', conf_matrix)\n",
    "print('Confusion Matrix of KNN Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22a2641d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM Model - Anxiety: 0.63066\n",
      "Confusion Matrix of SVM Model - Anxiety: \n",
      " [[112   0   0   0   0]\n",
      " [  9   0   0  11   0]\n",
      " [ 24   0   0  10   0]\n",
      " [ 17   0   0  69   0]\n",
      " [ 35   0   0   0   0]]\n",
      "Confusion Matrix of SVM Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.57      1.00      0.72       112\n",
      "         Minimal       0.00      0.00      0.00        20\n",
      "        Moderate       0.00      0.00      0.00        34\n",
      "          Normal       0.77      0.80      0.78        86\n",
      "          Severe       0.00      0.00      0.00        35\n",
      "\n",
      "        accuracy                           0.63       287\n",
      "       macro avg       0.27      0.36      0.30       287\n",
      "    weighted avg       0.45      0.63      0.52       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SVC Model for Anxiety Classification\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "svm_model.fit(X_train, Y_train)\n",
    "\n",
    "preds = svm_model.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_svc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of SVM Model - Anxiety: {:.5f}'.format(accuracy_svc))\n",
    "print('Confusion Matrix of SVM Model - Anxiety: \\n', conf_matrix)\n",
    "print('Confusion Matrix of SVM Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d25eaf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of NB Model - Anxiety: 0.97213\n",
      "Confusion Matrix of NB Model - Anxiety: \n",
      " [[110   0   0   0   2]\n",
      " [  1  19   0   0   0]\n",
      " [  0   0  32   0   2]\n",
      " [  1   0   0  85   0]\n",
      " [  2   0   0   0  33]]\n",
      "Classification Report of NB Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.96      0.98      0.97       112\n",
      "         Minimal       1.00      0.95      0.97        20\n",
      "        Moderate       1.00      0.94      0.97        34\n",
      "          Normal       1.00      0.99      0.99        86\n",
      "          Severe       0.89      0.94      0.92        35\n",
      "\n",
      "        accuracy                           0.97       287\n",
      "       macro avg       0.97      0.96      0.97       287\n",
      "    weighted avg       0.97      0.97      0.97       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes Model for Anxiety Classification\n",
    "clfNB = GaussianNB()\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfNB.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfNB.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_nb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of NB Model - Anxiety: {:.5f}'.format(accuracy_nb))\n",
    "print('Confusion Matrix of NB Model - Anxiety: \\n', conf_matrix)\n",
    "print('Classification Report of NB Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac250cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RFC Model - Anxiety: 0.94077\n",
      "Confusion Matrix of RFC Model - Anxiety: \n",
      " [[112   0   0   0   0]\n",
      " [  0   3  15   2   0]\n",
      " [  0   0  34   0   0]\n",
      " [  0   0   0  86   0]\n",
      " [  0   0   0   0  35]]\n",
      "Classification report of KNN Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00       112\n",
      "         Minimal       1.00      0.15      0.26        20\n",
      "        Moderate       0.69      1.00      0.82        34\n",
      "          Normal       0.98      1.00      0.99        86\n",
      "          Severe       1.00      1.00      1.00        35\n",
      "\n",
      "        accuracy                           0.94       287\n",
      "       macro avg       0.93      0.83      0.81       287\n",
      "    weighted avg       0.96      0.94      0.92       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier Model for Anxiety Classification\n",
    "clfRFC = RandomForestClassifier(n_estimators=95, random_state=85)\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfRFC.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfRFC.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_rfc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "\n",
    "print('Accuracy of RFC Model - Anxiety: {:.5f}'.format(accuracy_rfc))\n",
    "print('Confusion Matrix of RFC Model - Anxiety: \\n', conf_matrix)\n",
    "print('Classification report of KNN Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63b10489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGB Model - Anxiety: 1.00000\n",
      "Confusion Matrix of XGB Model - Anxiety: \n",
      " [[112   0   0   0   0]\n",
      " [  0  20   0   0   0]\n",
      " [  0   0  34   0   0]\n",
      " [  0   0   0  86   0]\n",
      " [  0   0   0   0  35]]\n",
      "Classification report of XGB Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00       112\n",
      "         Minimal       1.00      1.00      1.00        20\n",
      "        Moderate       1.00      1.00      1.00        34\n",
      "          Normal       1.00      1.00      1.00        86\n",
      "          Severe       1.00      1.00      1.00        35\n",
      "\n",
      "        accuracy                           1.00       287\n",
      "       macro avg       1.00      1.00      1.00       287\n",
      "    weighted avg       1.00      1.00      1.00       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# XG Boost Classifier For Anxiety Classifier\n",
    "xgb_model = XGBClassifier()\n",
    "le = LabelEncoder()\n",
    "\n",
    "xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "\n",
    "Y_pred = xgb_model.predict(X_test)\n",
    "Y_pred = le.inverse_transform(Y_pred)\n",
    "\n",
    "accuracy_xgb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of XGB Model - Anxiety: {:.5f}'.format(accuracy_xgb))\n",
    "print('Confusion Matrix of XGB Model - Anxiety: \\n', conf_matrix)\n",
    "print('Classification report of XGB Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68a04e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Combined Model - Anxiety: 0.99652\n",
      "Confusion Matrix of Combined Model - Anxiety: \n",
      " [[112   0   0   0   0]\n",
      " [  1  19   0   0   0]\n",
      " [  0   0  34   0   0]\n",
      " [  0   0   0  86   0]\n",
      " [  0   0   0   0  35]]\n",
      "Classification report of Combined Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.99      1.00      1.00       112\n",
      "         Minimal       1.00      0.95      0.97        20\n",
      "        Moderate       1.00      1.00      1.00        34\n",
      "          Normal       1.00      1.00      1.00        86\n",
      "          Severe       1.00      1.00      1.00        35\n",
      "\n",
      "        accuracy                           1.00       287\n",
      "       macro avg       1.00      0.99      0.99       287\n",
      "    weighted avg       1.00      1.00      1.00       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combining Naive Bayes, XG Boost and Random Forest\n",
    "\n",
    "# Training the models on whole data\n",
    "final_xgb_model = XGBClassifier()\n",
    "final_nb_model = GaussianNB()\n",
    "final_rfc_model = RandomForestClassifier(n_estimators=110, random_state=110)\n",
    "\n",
    "\n",
    "final_xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "final_nb_model.fit(X_train, Y_train)\n",
    "final_rfc_model.fit(X_train, Y_train)\n",
    "\n",
    "# Making prediction by take mode of predictions made by all the classifiers\n",
    "xgb_preds = final_xgb_model.predict(X_test)\n",
    "xgb_preds = le.inverse_transform(xgb_preds)\n",
    "\n",
    "nb_preds = final_nb_model.predict(X_test)\n",
    "rfc_preds = final_rfc_model.predict(X_test)\n",
    "\n",
    "final_preds = [mode([i,j,k])[0][0] for i,j, k in zip(xgb_preds, nb_preds, rfc_preds)]\n",
    "\n",
    "accuracy_combined = accuracy_score(Y_test, final_preds)\n",
    "conf_matrix = confusion_matrix(Y_test, final_preds)\n",
    "classif_report = classification_report(Y_test, final_preds)\n",
    "\n",
    "print('Accuracy of Combined Model - Anxiety: {:.5f}'.format(accuracy_combined))\n",
    "print('Confusion Matrix of Combined Model - Anxiety: \\n', conf_matrix)\n",
    "print('Classification report of Combined Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "09588f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_anxiety = [accuracy_knn, accuracy_svc, accuracy_nb, accuracy_rfc, accuracy_xgb, accuracy_combined]\n",
    "\n",
    "anxiety_analysis = pd.DataFrame({\n",
    "                        'Models' : ['KNN', 'SVC', 'Naive Bayes', 'Random Forest', 'XG Boost', 'Combined Model'],\n",
    "                        'Accuracy_Anxiety': [x * 100 for x in accuracy_anxiety]\n",
    "                    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d1ebe9",
   "metadata": {},
   "source": [
    "## Data Classification Models for Stress"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8136006b",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f45d38ae",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    source  TIPI1  TIPI2  TIPI3  TIPI4  TIPI5  TIPI6  TIPI7  TIPI8  TIPI9  \\\n",
       "26       2      4      1      7      7      7      2      6      5      1   \n",
       "31       2      5      5      5      5      7      7      7      5      5   \n",
       "57       0      2      4      1      5      6      4      3      6      6   \n",
       "81       2      7      6      3      6      4      1      7      5      3   \n",
       "87       2      1      3      5      6      6      7      2      7      1   \n",
       "\n",
       "    ...  gender  age  hand  religion  orientation  race  voted  married  \\\n",
       "26  ...       2   15     1         7            1    60      2        1   \n",
       "31  ...       1   18     1         2            1    60      2        1   \n",
       "57  ...       2   21     1         1            1    60      1        1   \n",
       "81  ...       2   14     1         4            1    70      2        1   \n",
       "87  ...       1   17     1         1            1    30      2        1   \n",
       "\n",
       "    familysize  ScoresStre  \n",
       "26           3          41  \n",
       "31           3           7  \n",
       "57           2          20  \n",
       "81           6          31  \n",
       "87           3          39  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df['CategoryStre']\n",
    "X = df.drop(columns=['Q2A','Q4A','Q7A','Q9A','Q15A','Q19A','Q20A','Q23A','Q25A','Q28A','Q30A','Q36A','Q40A','Q41A','introelapse','testelapse', 'surveyelapse','engnat','CategoryStre','CategoryAnx', 'CategoryDepr', 'country', 'ScoresDepr' ,'ScoresAnx','screensize','uniquenetworklocation','Q1A' ,'Q1I', 'Q1E', 'Q2I', 'Q2E', 'Q3A', 'Q3I', 'Q3E','Q4I','Q4E', 'Q5E', 'Q5A' ,'Q5I', 'Q6E', 'Q6A' ,'Q6I', 'Q7E' ,'Q7I', 'Q8E','Q8A' ,'Q8I', 'Q9E', 'Q9I', 'Q10E', 'Q10A', 'Q10I', 'Q11E', 'Q11A' ,'Q11I', 'Q12E', 'Q12A' ,'Q12I', 'Q13E', 'Q13A' ,'Q13I', 'Q14E', 'Q14A' ,'Q14I', 'Q15E', 'Q15I', 'Q16E', 'Q16A' ,'Q16I', 'Q17E', 'Q17A' ,'Q17I', 'Q18E', 'Q18A' ,'Q18I', 'Q19I', 'Q19E',  'Q20I', 'Q20E', 'Q21A', 'Q21I', 'Q21E', 'Q22A', 'Q22I','Q22E', 'Q23I','Q23E','Q24A','Q24I','Q24E', 'Q25I', 'Q25E', 'Q26A', 'Q26I', 'Q26E', 'Q27A', 'Q27I', 'Q27E', 'Q28I', 'Q28E', 'Q29A', 'Q29I', 'Q29E',  'Q30I', 'Q30E', 'Q31A', 'Q31I', 'Q31E', 'Q32A', 'Q32I', 'Q32E', 'Q33A', 'Q33I', 'Q33E', 'Q34A', 'Q34I', 'Q34E', 'Q35A', 'Q35I', 'Q35E', 'Q36I', 'Q36E', 'Q37A', 'Q37I', 'Q37E', 'Q38A', 'Q38I', 'Q38E', 'Q39A', 'Q39I', 'Q39E', 'Q40I', 'Q40E', 'Q41I', 'Q41E', 'Q42A', 'Q42I', 'Q42E'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "01761cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "03b4cc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cf0a3d",
   "metadata": {},
   "source": [
    "### Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "35b76986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN Model - Stress: 0.49477\n",
      "Confusion Matrix of KNN Model - Stress: \n",
      " [[14  0  0  1 45]\n",
      " [ 0  0 10 13 12]\n",
      " [ 1  0 11 10 26]\n",
      " [ 0  0 11 72  3]\n",
      " [ 0  0  6  7 45]]\n",
      "Confusion Matrix of KNN Model - Stress: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.93      0.23      0.37        60\n",
      "         Minimal       0.00      0.00      0.00        35\n",
      "        Moderate       0.29      0.23      0.26        48\n",
      "          Normal       0.70      0.84      0.76        86\n",
      "          Severe       0.34      0.78      0.48        58\n",
      "\n",
      "        accuracy                           0.49       287\n",
      "       macro avg       0.45      0.42      0.37       287\n",
      "    weighted avg       0.52      0.49      0.45       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# KNN Model for Stress Classification\n",
    "\n",
    "# Create a KNN classifier with k (number of neighbors)\n",
    "knn = KNeighborsClassifier(n_neighbors=95)\n",
    "\n",
    "# Fit the model to the training data\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = knn.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_knn = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of KNN Model - Stress: {:.5f}'.format(accuracy_knn))\n",
    "print('Confusion Matrix of KNN Model - Stress: \\n', conf_matrix)\n",
    "print('Confusion Matrix of KNN Model - Stress: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31b252ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM Model - Anxiety: 0.49477\n",
      "Confusion Matrix of SVM Model - Anxiety: \n",
      " [[14  0  0  1 45]\n",
      " [ 0  0 10 13 12]\n",
      " [ 1  0 11 10 26]\n",
      " [ 0  0 11 72  3]\n",
      " [ 0  0  6  7 45]]\n",
      "Confusion Matrix of SVM Model - Anxiety: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.93      0.23      0.37        60\n",
      "         Minimal       0.00      0.00      0.00        35\n",
      "        Moderate       0.29      0.23      0.26        48\n",
      "          Normal       0.70      0.84      0.76        86\n",
      "          Severe       0.34      0.78      0.48        58\n",
      "\n",
      "        accuracy                           0.49       287\n",
      "       macro avg       0.45      0.42      0.37       287\n",
      "    weighted avg       0.52      0.49      0.45       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SVC Model for Stress Classification\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "svm_model.fit(X_train, Y_train)\n",
    "\n",
    "preds = svm_model.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_svc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of SVM Model - Anxiety: {:.5f}'.format(accuracy_svc))\n",
    "print('Confusion Matrix of SVM Model - Anxiety: \\n', conf_matrix)\n",
    "print('Confusion Matrix of SVM Model - Anxiety: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0aa3bd60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of NB Model - Stress: 0.93728\n",
      "Confusion Matrix of NB Model - Stress: \n",
      " [[59  0  0  0  1]\n",
      " [ 0 32  0  3  0]\n",
      " [ 1  1 43  1  2]\n",
      " [ 0  1  0 85  0]\n",
      " [ 3  0  5  0 50]]\n",
      "Classification Report of KNN Model - Stress: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       0.94      0.98      0.96        60\n",
      "         Minimal       0.94      0.91      0.93        35\n",
      "        Moderate       0.90      0.90      0.90        48\n",
      "          Normal       0.96      0.99      0.97        86\n",
      "          Severe       0.94      0.86      0.90        58\n",
      "\n",
      "        accuracy                           0.94       287\n",
      "       macro avg       0.93      0.93      0.93       287\n",
      "    weighted avg       0.94      0.94      0.94       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes Model for Stress Classification\n",
    "\n",
    "clfNB = GaussianNB()\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfNB.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfNB.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_nb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of NB Model - Stress: {:.5f}'.format(accuracy_nb))\n",
    "print('Confusion Matrix of NB Model - Stress: \\n', conf_matrix)\n",
    "print('Classification Report of KNN Model - Stress: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16efa556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RFC Model - Stress: 0.96516\n",
      "Confusion Matrix of RFC Model - Stress: \n",
      " [[60  0  0  0  0]\n",
      " [ 0 25 10  0  0]\n",
      " [ 0  0 48  0  0]\n",
      " [ 0  0  0 86  0]\n",
      " [ 0  0  0  0 58]]\n",
      "Classification report of KNN Model - Stress: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00        60\n",
      "         Minimal       1.00      0.71      0.83        35\n",
      "        Moderate       0.83      1.00      0.91        48\n",
      "          Normal       1.00      1.00      1.00        86\n",
      "          Severe       1.00      1.00      1.00        58\n",
      "\n",
      "        accuracy                           0.97       287\n",
      "       macro avg       0.97      0.94      0.95       287\n",
      "    weighted avg       0.97      0.97      0.96       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier Model for Stress Classification\n",
    "\n",
    "clfRFC = RandomForestClassifier(n_estimators=95, random_state=85)\n",
    "\n",
    "# Fit the model to the training data\n",
    "clfRFC.fit(X_train, Y_train)\n",
    "\n",
    "# Predict the classes of the testing set\n",
    "Y_pred = clfRFC.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy score of the model\n",
    "accuracy_rfc = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "\n",
    "print('Accuracy of RFC Model - Stress: {:.5f}'.format(accuracy_rfc))\n",
    "print('Confusion Matrix of RFC Model - Stress: \\n', conf_matrix)\n",
    "print('Classification report of KNN Model - Stress: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a7eae8d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGB Model - Stress: 1.00000\n",
      "Confusion Matrix of XGB Model - Stress: \n",
      " [[60  0  0  0  0]\n",
      " [ 0 35  0  0  0]\n",
      " [ 0  0 48  0  0]\n",
      " [ 0  0  0 86  0]\n",
      " [ 0  0  0  0 58]]\n",
      "Classification report of XGB Model - Stress: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00        60\n",
      "         Minimal       1.00      1.00      1.00        35\n",
      "        Moderate       1.00      1.00      1.00        48\n",
      "          Normal       1.00      1.00      1.00        86\n",
      "          Severe       1.00      1.00      1.00        58\n",
      "\n",
      "        accuracy                           1.00       287\n",
      "       macro avg       1.00      1.00      1.00       287\n",
      "    weighted avg       1.00      1.00      1.00       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# XG Boost Classifier For Anxiety Classifier\n",
    "xgb_model = XGBClassifier()\n",
    "le = LabelEncoder()\n",
    "\n",
    "xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "\n",
    "Y_pred = xgb_model.predict(X_test)\n",
    "Y_pred = le.inverse_transform(Y_pred)\n",
    "\n",
    "accuracy_xgb = accuracy_score(Y_test, Y_pred)\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "classif_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "print('Accuracy of XGB Model - Stress: {:.5f}'.format(accuracy_xgb))\n",
    "print('Confusion Matrix of XGB Model - Stress: \\n', conf_matrix)\n",
    "print('Classification report of XGB Model - Stress: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4e4a0979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Combined Model - Stress: 0.99652\n",
      "Confusion Matrix of Combined Model - Stress: \n",
      " [[60  0  0  0  0]\n",
      " [ 0 35  0  0  0]\n",
      " [ 0  0 47  0  1]\n",
      " [ 0  0  0 86  0]\n",
      " [ 0  0  0  0 58]]\n",
      "Classification report of Combined Model - Stress: \n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Extremely Severe       1.00      1.00      1.00        60\n",
      "         Minimal       1.00      1.00      1.00        35\n",
      "        Moderate       1.00      0.98      0.99        48\n",
      "          Normal       1.00      1.00      1.00        86\n",
      "          Severe       0.98      1.00      0.99        58\n",
      "\n",
      "        accuracy                           1.00       287\n",
      "       macro avg       1.00      1.00      1.00       287\n",
      "    weighted avg       1.00      1.00      1.00       287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combining Naive Bayes, XG Boost and Random Forest\n",
    "\n",
    "# Training the models on whole data\n",
    "final_xgb_model = XGBClassifier()\n",
    "final_nb_model = GaussianNB()\n",
    "final_rfc_model = RandomForestClassifier(n_estimators=110, random_state=110)\n",
    "\n",
    "\n",
    "final_xgb_model.fit(X_train, le.fit_transform(Y_train))\n",
    "final_nb_model.fit(X_train, Y_train)\n",
    "final_rfc_model.fit(X_train, Y_train)\n",
    "\n",
    "# Making prediction by take mode of predictions made by all the classifiers\n",
    "xgb_preds = final_xgb_model.predict(X_test)\n",
    "xgb_preds = le.inverse_transform(xgb_preds)\n",
    "\n",
    "nb_preds = final_nb_model.predict(X_test)\n",
    "rfc_preds = final_rfc_model.predict(X_test)\n",
    "\n",
    "final_preds = [mode([i,j,k])[0][0] for i,j, k in zip(xgb_preds, nb_preds, rfc_preds)]\n",
    "\n",
    "accuracy_combined = accuracy_score(Y_test, final_preds)\n",
    "conf_matrix = confusion_matrix(Y_test, final_preds)\n",
    "classif_report = classification_report(Y_test, final_preds)\n",
    "\n",
    "print('Accuracy of Combined Model - Stress: {:.5f}'.format(accuracy_combined))\n",
    "print('Confusion Matrix of Combined Model - Stress: \\n', conf_matrix)\n",
    "print('Classification report of Combined Model - Stress: \\n', classif_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6a5f8e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_stress = [accuracy_knn, accuracy_svc, accuracy_nb, accuracy_rfc, accuracy_xgb, accuracy_combined]\n",
    "\n",
    "stress_analysis = pd.DataFrame({\n",
    "                        'Models' : ['KNN', 'SVC', 'Naive Bayes', 'Random Forest', 'XG Boost', 'Combined Model'],\n",
    "                        'Accuracy_Stress': [x * 100 for x in accuracy_stress]\n",
    "                    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47a8d4b",
   "metadata": {},
   "source": [
    "## Analysis of the Models Used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c99bbb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = pd.concat([stress_analysis.set_index('Models'), \n",
    "                      anxiety_analysis.set_index('Models'), \n",
    "                      depression_analysis.set_index('Models')],\n",
    "                     axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e5c70804",
   "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",
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       "\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>Accuracy_Stress</th>\n",
       "      <th>Accuracy_Anxiety</th>\n",
       "      <th>Accuracy_Depression</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Models</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>49.477352</td>\n",
       "      <td>63.066202</td>\n",
       "      <td>59.930314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>49.477352</td>\n",
       "      <td>63.066202</td>\n",
       "      <td>59.930314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>93.728223</td>\n",
       "      <td>97.212544</td>\n",
       "      <td>94.425087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>96.515679</td>\n",
       "      <td>94.076655</td>\n",
       "      <td>97.560976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XG Boost</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy_Stress  Accuracy_Anxiety  Accuracy_Depression\n",
       "Models                                                               \n",
       "KNN                  49.477352         63.066202            59.930314\n",
       "SVC                  49.477352         63.066202            59.930314\n",
       "Naive Bayes          93.728223         97.212544            94.425087\n",
       "Random Forest        96.515679         94.076655            97.560976\n",
       "XG Boost            100.000000        100.000000           100.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "59a57a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = analysis.plot(kind='barh')\n",
    "plt.title(\"Accuracy of classification using different ML algorithms for DASS42\")\n",
    "plt.xlabel(\"Accuracy\")\n",
    "plt.ylabel(\"Models\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}