dass_depression_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depression Severity Classification using DASS and Demographical Data\n",
    "\n",
    "This notebook looks at modelling the severity of depression based on the scores given to the DAS assessment and certain demographical indicators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "import pickle\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Size: (39775, 172)\n"
     ]
    },
    {
     "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>Q1A</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E</th>\n",
       "      <th>Q2A</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E</th>\n",
       "      <th>Q3A</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E</th>\n",
       "      <th>Q4A</th>\n",
       "      <th>...</th>\n",
       "      <th>screensize</th>\n",
       "      <th>uniquenetworklocation</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>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>3890</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>2122</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1944</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8118</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2890</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>4777</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5784</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>4373</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>3242</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>5081</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6837</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>5521</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>3215</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>7731</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4156</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Psychology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 172 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q1A  Q1I   Q1E  Q2A  Q2I   Q2E  Q3A  Q3I   Q3E  Q4A  ...  screensize  \\\n",
       "0    4   28  3890    4   25  2122    2   16  1944    4  ...           1   \n",
       "1    4    2  8118    1   36  2890    2   35  4777    3  ...           2   \n",
       "2    3    7  5784    1   33  4373    4   41  3242    1  ...           2   \n",
       "3    2   23  5081    3   11  6837    2   37  5521    1  ...           2   \n",
       "4    2   36  3215    2   13  7731    3    5  4156    4  ...           2   \n",
       "\n",
       "   uniquenetworklocation  hand  religion  orientation  race  voted  married  \\\n",
       "0                      1     1        12            1    10      2        1   \n",
       "1                      1     2         7            0    70      2        1   \n",
       "2                      1     1         4            3    60      1        1   \n",
       "3                      1     2         4            5    70      2        1   \n",
       "4                      2     3        10            1    10      2        1   \n",
       "\n",
       "   familysize       major  \n",
       "0           2         NaN  \n",
       "1           4         NaN  \n",
       "2           3         NaN  \n",
       "3           5     biology  \n",
       "4           4  Psychology  \n",
       "\n",
       "[5 rows x 172 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/dass_data/data.csv\", delimiter=\"\\t\")\n",
    "print(\"Dataset Size:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Out Columns and Check for Missing Values\n",
    "\n",
    "This section creates the filters for the relevant columns, i.e for demography data and for the questions related to depression. The entire dataframe is then scanned to check for missing values. As seen below, the dataframe is relatively clean where most missing values only exist in the major column and requires analysis and preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country        2\n",
       "major      11403\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_columns = df.columns\n",
    "\n",
    "# http://www2.psy.unsw.edu.au/dass/Download%20files/Dass_template.pdf\n",
    "# The question numbers are taken from the actual Dass template answering sheet.\n",
    "# These questions specifically relate to depression and the scores given to these questions can classify the severity of depression.\n",
    "depression_question_numbers = [3, 5, 10, 13, 16, 17, 21, 24, 26, 31, 34, 37, 38, 42]\n",
    "demography_columns = [\"education\", \"urban\", \"gender\", \"engnat\", \"age\", \"hand\", \"religion\", \"orientation\", \"race\", \"married\", \"familysize\", \"major\"]\n",
    "depression_question_columns = [f\"Q{i}A\" for i in depression_question_numbers]\n",
    "\n",
    "nan_counts = df.isna().sum()\n",
    "nan_counts[nan_counts > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing and Inconsistent Values\n",
    "\n",
    "The NaN values are replaced with the string \"None\" and treated as category 0. We see that even after replacing the NaN values, there are over 5000 unique values for the \"major\" column and by inspecting the counts for each unique value, there are actually duplicate majors with inconsistent spelling, abbreviations, etc. This column would hence need to be normalized before encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None                  11425\n",
       "English                1026\n",
       "Psychology              850\n",
       "Business                621\n",
       "Accounting              607\n",
       "                      ...  \n",
       "Comp Science              1\n",
       "Art and Education         1\n",
       "Veterinary science        1\n",
       "criminal law              1\n",
       "computer sciece           1\n",
       "Name: major, Length: 5309, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({\"country\":\"None\", \"major\":\"None\"}, inplace=True)\n",
    "df[\"major\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "none                      11446\n",
      "psychology                 1440\n",
      "english                    1242\n",
      "engineering                 899\n",
      "business                    820\n",
      "accounting                  774\n",
      "law                         670\n",
      "computer science            545\n",
      "medicine                    446\n",
      "civil engineering           444\n",
      "biology                     402\n",
      "science                     397\n",
      "psychology                  347\n",
      "education                   346\n",
      "nursing                     345\n",
      "mechanical engineering      327\n",
      "finance                     276\n",
      "architecture                267\n",
      "management                  249\n",
      "medical                     248\n",
      "Name: major, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Just by lowercasing and standardizing the possible ways of capitalisation, the number of unique majors drops to 4000\n",
    "normalized_majors = df[\"major\"].str.lower()\n",
    "print(normalized_majors.value_counts()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def condition(title):\n",
    "    if 'busin' in str(title).lower() or 'manage' in str(title).lower() or 'Buss' in str(title) or 'Bisness' in str(title) or 'Manag' in str(title) or 'buis' in str(title) or 'Entrepreneur' in str(title) or 'entrepr' in str(title).lower() or 'managment' in str(title).lower() or 'Buis' in str(title) or 'Busni' in str(title) or 'Mana' in str(title) or 'buss' in str(title).lower() or 'Bi' in str(title) or 'Mgt' in str(title) or 'MBA' in str(title) or 'Mgmt' in str(title) or 'MD' in str(title):\n",
    "        return 'Business/Management'\n",
    "    elif 'information technology' in str(title).lower() or 'IT' in str(title) or 'it' in str(title):\n",
    "        return 'I.T'\n",
    "    elif 'math' in str(title).lower() or 'LOGISTICS' in str(title) or 'st' in str(title).lower() or 'marh' in str(title).lower() or 'Mate' in str(title):\n",
    "        return 'Mathematics'\n",
    "    elif 'computer' in str(title).lower():\n",
    "        return 'I.T'\n",
    "    elif 'bio' in str(title).lower() or 'Plant' in str(title) or 'plant' in str(title).lower() or 'Micro' in str(title):\n",
    "        return 'Biology'\n",
    "    elif 'tesl' in str(title).lower() or 'TES' in str(title) or 'Teso' in str(title) or 'Enhlish' in str(title):\n",
    "        return 'English'\n",
    "    elif 'account' in str(title).lower() or 'Accoun' in str(title) or 'Acc' in str(title) or 'acc' in str(title).lower() or 'Acouunt' in str(title) or 'Acvount' in str(title) or 'Count' in str(title):\n",
    "        return 'Accountacy'\n",
    "    elif 'CA' in str(title):\n",
    "        return 'CA'\n",
    "    elif 'none' in str(title).lower() or '0' in str(title) or  '_' in str(title) or '.' in str(title) or 'Nine' in str(title) or '19' in str(title):\n",
    "        return 'No Degree'\n",
    "    elif 'nurs' in str(title).lower() or 'BSN' in str(title):\n",
    "        return 'Nursing'\n",
    "    elif '-' in str(title).lower() or 'NIL' in str(title):\n",
    "        return 'No Degree'\n",
    "    elif 'teach' in str(title).lower() or 'Lect' in str(title) or 'eet' in str(title).lower():\n",
    "        return 'Teaching'\n",
    "    elif 'pharma' in str(title).lower() or 'medic' in str(title).lower() or 'med' in str(title).lower() or 'hospi' in str(title).lower() or 'Mwdicine' in str(title) or 'Farmacy' in str(title) or 'Pharacology' in str(title) or 'farmasi' in str(title).lower() or 'Farmasy' in str(title):\n",
    "        return 'Pharmacy/Medical'\n",
    "    elif 'doctor' in str(title).lower() or  'MBBS' in str(title) or 'Mbbs' in str(title) or 'Surge' in str(title) or 'surge' in str(title) or 'mbbs' in str(title).lower()or 'dermat' in str(title).lower() or 'Podiat' in str(title) :\n",
    "        return 'Doctor'\n",
    "    elif 'no' in str(title).lower() or 'Undec' in str(title) or 'Idk' in str(title) or 'idk' in str(title).lower() or 'Hahaha' in str(title) or 'never' in str(title).lower() or 'T' in str(title) or 'Good' in str(title):\n",
    "        return 'No Degree'\n",
    "    elif 'film' in str(title).lower() or 'Cinema' in str(title) or 'fil' in str(title).lower() or 'Adver' in str(title) or 'adver' in str(title) or 'Act' in str(title) or 'Enter' in str(title) or 'digital' in str(title).lower() or 'cinema' in str(title).lower() or 'Video' in str(title) or 'Direct' in str(title) or 'Theat' in str(title) or 'Radio' in str(title) or 'theat' in str(title).lower() or 'drama' in str(title).lower():\n",
    "        return 'Media'\n",
    "    elif 'international' in str(title).lower() or 'Internatianal' in str(title):\n",
    "        return 'International Relations'\n",
    "    elif 'human' in str(title).lower() or 'hr' in str(title).lower() or 'Hs' in str(title) or 'Hm' in str(title) or 'Humam' in str(title):\n",
    "        return 'Human Resources'\n",
    "    elif 'art' in str(title).lower() or 'Painting' in str(title) or 'Drawing' in str(title) or 'ba' in str(title) or 'Printing' in str(title) or 'las' in str(title).lower() or 'Ma' in str(title) or 'paint' in str(title).lower() or 'creative' in str(title).lower() or 'AA' in str(title) or 'BA' in str(title):\n",
    "        return 'Arts'\n",
    "    elif 'islam' in str(title).lower() or 'Muamalat' in str(title) or 'Quran' in str(title) or 'Halal' in str(title) or 'Usul' in str(title) or 'Zakat' in str(title) or 'usul' in str(title).lower():\n",
    "        return 'Islamic Studies'\n",
    "    elif 'physio' in str(title).lower() or 'fis' in str(title).lower():\n",
    "        return 'Physiotherapy'\n",
    "    elif 'socio' in str(title).lower() or 'social' in str(title).lower() or 'soical' in str(title).lower() or 'Sis' in str(title) or 'Sosio' in str(title) or 'Sicio' in str(title) or 'sosiality' in str(title).lower():\n",
    "        return 'Sociology'\n",
    "    elif 'bank' in str(title).lower():\n",
    "        return 'Banking'\n",
    "    elif 'agri' in str(title).lower():\n",
    "        return 'Agriculture'\n",
    "    elif 'Market' in str(title) or 'Finan' in str(title) or 'finance' in str(title).lower() or 'MARKETING' in str(title) or 'market' in str(title).lower() or 'retail' in str(title).lower() or 'CMP' in str(title) or 'Merket' in str(title):\n",
    "        return 'Marketing/Finance'\n",
    "    elif 'counsel' in str(title).lower() or 'cauns' in str(title) or 'Kaunseling' in str(title) or 'kaunseling' in str(title) or 'Caunsel' in str(title):\n",
    "        return 'Counselling'\n",
    "    elif 'programming' in str(title).lower() or 'coding' in str(title).lower() or 'Ibm' in str(title) or 'ceit' in str(title) or 'Hacking' in str(title):\n",
    "        return 'I.T'\n",
    "    elif 'civil' in str(title).lower() or 'comp' in str(title).lower() or 'Mechanical' in str(title) or 'Electrical' in str(title) or 'Mechatronics' in str(title) or 'Eee' in str(title) or 'cs' in str(title).lower() or 'mecha' in str(title) or 'Chemical' in str(title) or 'chemical' in str(title) or 'tech' in str(title) or 'ec' in str(title).lower() or 'egineering' in str(title).lower() or 'manufacturing' in str(title).lower():\n",
    "        return 'Engineering'\n",
    "    elif 'ict' in str(title).lower() or 'developer' in str(title).lower() or 'I.T' in str(title) or 'CAE&D' in str(title) or 'It' in str(title):\n",
    "        return 'I.T'\n",
    "    elif 'commu' in str(title).lower() or 'comm' in str(title).lower() or 'com' in str(title).lower() or 'Conmunication' in str(title):\n",
    "        return 'Communications'\n",
    "    elif 'administration' in str(title).lower() or 'admin' in str(title).lower():\n",
    "        return 'Administration'\n",
    "    elif 'psycho' in str(title).lower() or 'psy' in str(title).lower() or 'Clinical osychology' in str(title) or 'hschology' in str(title) or 'Pysch' in str(title) or 'pys' in str(title).lower() or 'Pych' in str(title) or 'pscy' in str(title) or 'payc' in str(title).lower() or 'Phyc' in str(title) or 'psicologia' in str(title) or 'Phsychology' in str(title) or 'Phichology' in str(title) or 'psuchology' in str(title) or 'Pschology' in str(title) or 'psikologi' in str(title).lower():\n",
    "        return 'Psychology'\n",
    "    elif 'english' in str(title).lower() or 'Elglish' in str(title) or 'esl' in str(title).lower() or 'Emg' in str(title) or 'emglisj' in str(title).lower():\n",
    "        return 'English'\n",
    "    elif 'law' in str(title).lower() or 'BBA' in str(title) or 'llb' in str(title) or 'lew' in str(title).lower() or 'kaw' in str(title).lower() or 'enforcement' in str(title).lower() or 'Kaw' in str(title):\n",
    "        return 'Law'\n",
    "    elif 'engineering' in str(title).lower() or 'engi' in str(title).lower() or 'eng' in str(title).lower() or 'Software' in str(title) or 'soft' in str(title).lower() or 'mechanical' in str(title).lower() or 'Egineeering' in  str(title) or 'electronic' in str(title).lower() or 'CE' in str(title) or 'mech' in str(title).lower() or 'Ciclvil' in str(title) or 'Eggineering' in str(title) or 'Tech' in str(title) or 'Teol' in str(title) or 'EEE' in str(title) or 'PE' in str(title):\n",
    "        return 'Engineering'\n",
    "    elif 'architecture' in str(title).lower() or 'aechitecture' in str(title).lower() or 'archirecture' in str(title).lower() or 'architect' in str(title).lower() or 'Arsitechture' in str(title) or 'Building' in str(title) or 'building' in str(title).lower() or 'Arc' in str(title):\n",
    "        return 'Architecture'\n",
    "    elif 'design' in str(title).lower() or 'Desig' in str(title) or 'Dssign' in str(title):\n",
    "        return 'Designer'\n",
    "    elif 'science' in str(title).lower() or 'Sceince' in str(title) or 'Sci' in str(title) or 'sciene' in str(title) or 'BS' in str(title):\n",
    "        return 'Pure Sciences'\n",
    "    elif 'physics' in str(title).lower() or 'Phsyics' in str(title) or 'EMC' in str(title) or 'Physic' in str(title) or 'physi' in str(title):\n",
    "        return 'Physics'\n",
    "    elif 'chemistry' in str(title).lower() or 'CIS' in str(title) or 'Chem' in str(title):\n",
    "        return 'Chemistry'\n",
    "    elif 'french' in str(title).lower() or 'Fr' in str(title):\n",
    "        return 'French'\n",
    "    elif 'religi' in str(title).lower() or 'Relegion' in str(title) or 'Rel' in str(title) or 'Hukum' in str(title) or 'Sains' in str(title):\n",
    "        return 'Religious Studies'\n",
    "    elif title=='&#1593;&#1604;&#1605; &#1606;&#1601;&#1587;' or title=='&#22810;&#23186;&#39636;&#35373;&#35336;' or title=='nil' or title=='drop out' or title=='&#1055;&#1089;&#1080;&#1093;&#1086;&#1083;&#1' or title=='75' or title=='Secondary education' or title=='Thiê&#769;t kê&#769; &#273;ô&#768; ho&#803;a' or title=='18' or title=='ongoing' or title=='&#28888;&#22521;' or title=='lol' or title=='In college currently' or title=='secondary education' or title=='Dropped out' or title=='na' or title=='didnt attend' or title=='im going on the next year. ' or title=='&#304;lahiyat' or title=='lmfao, im 15' or title=='Elem Ed' or title=='yes' or title=='N/a' or title=='/' or title=='???' or title=='cocaine 101' or title=='doesnt matter' or title== 'oooo' or title=='G' or title=='Yes' or title=='Na' or title=='Na 'or title=='Want sure':\n",
    "        return 'No Degree'\n",
    "    elif 'Music' in str(title) or 'Dance' in str(title) or 'danc' in str(title).lower() or 'Vocational' in str(title) or 'Muisc' in str(title) or 'music' in str(title).lower() or 'Performance' in str(title):\n",
    "        return 'Music/Dance'\n",
    "    elif 'pol' in str(title).lower() or 'Govern' in str(title) or 'Right' in str(title):\n",
    "        return 'Politics'\n",
    "    elif 'photo' in str(title).lower() or 'Foto' in str(title) or 'Photo' in str(title):\n",
    "        return 'Photography'\n",
    "    elif 'Television' in str(title) or 'telev' in str(title).lower():\n",
    "        return 'Television'\n",
    "    elif 'bahasa' in str(title).lower() or 'Bahasa' in str(title) or 'Malay' in str(title) or 'malay' in str(title).lower():\n",
    "        return 'Malaysian languages'\n",
    "    elif 'Urban' in str(title) or 'Town' in str(title) or 'town' in str(title).lower() or 'planning' in str(title) or 'Plann' in str(title) or 'development' in str(title):\n",
    "        return 'Economic Developments'\n",
    "    elif 'Public' in str(title) or 'public' in str(title).lower():\n",
    "        return 'Public Relations'\n",
    "    elif 'Writing' in str(title) or 'writing' in str(title).lower() or 'Screenwritinf' in str(title) or 'Author' in str(title):\n",
    "        return 'Writing/Author'\n",
    "    elif 'philosophy' in str(title).lower() or 'Phil' in str(title) or 'philos' in str(title).lower() or 'Filo' in str(title) or 'Phylosophy' in str(title):\n",
    "        return 'Philosophy'\n",
    "    elif 'Actua' in str(title):\n",
    "        return 'Acturial Studies'\n",
    "    elif 'DENTALWORKS' in str(title) or 'dental' in str(title) or 'Dental' in str(title) or 'Odont' in str(title):\n",
    "        return 'Dentist'\n",
    "    elif 'beaut' in str(title).lower() or 'Fashion' in str(title) or 'make' in str(title) or 'fashion' in str(title).lower() or 'hair' in str(title).lower() or 'cosmet' in str(title).lower():\n",
    "        return 'Fashion'\n",
    "    elif 'Health' in str(title) or 'health' in str(title).lower() or 'wellness' in str(title).lower() or 'Healtcare' in str(title):\n",
    "        return 'Healthcare'\n",
    "    elif 'Language' in str(title) or 'lang' in str(title).lower() or 'Laq' in str(title):\n",
    "        return 'Languages'\n",
    "    elif 'cook' in str(title).lower() or 'bakery' in str(title).lower() or 'Bak' in str(title) or 'CULINARY' in str(title) or 'Food' in str(title) or 'food' in str(title) or 'chef' in str(title).lower() or 'Cul' in str(title) or 'Patiss' in str(title) or 'culi' in str(title).lower():\n",
    "        return 'Cookings'\n",
    "    elif 'Hotel' in str(title) or 'hotel' in str(title).lower() or 'food service' in str(title) or 'cater' in str(title).lower():\n",
    "        return 'Hotel Management'\n",
    "    elif 'therapy' in str(title).lower() or 'ot' in str(title).lower() or 'theraphy' in str(title):\n",
    "        return 'Therapeutical Studies'\n",
    "    elif 'veter' in str(title).lower() or 'Veter' in str(title) or 'Vet' in str(title):\n",
    "        return 'Veterinary'\n",
    "    elif 'Survey' in str(title) or 'survey' in str(title) or 'serveyors' in str(title).lower() or 'Qs' in str(title) or 'SURVEYING' in str(title) or 'QS' in str(title) or 'Surver' in str(title):\n",
    "        return 'Surveyour Studies'\n",
    "    elif 'Aircraft' in str(title) or 'aircraft' in str(title).lower() or 'aircr' in str(title).lower() or 'aviation' in str(title).lower() or 'Aero' in str(title) or 'navigation' in str(title).lower():\n",
    "        return 'Aircrafts'\n",
    "    elif 'environment' in str(title).lower() or 'Environment' in str(title) or 'envi' in str(title).lower():\n",
    "        return 'Environmental Educations'\n",
    "    elif 'Syariah' in str(title) or 'syariah' in str(title):\n",
    "        return 'Syrian Languages'\n",
    "    elif 'judicial' in str(title).lower() or 'juri' in str(title).lower() or 'legal' in str(title).lower():\n",
    "        return 'Judicial Studies'\n",
    "    elif 'Liter' in str(title) or 'literature' in str(title) or 'litt' in str(title).lower():\n",
    "        return 'Literature'\n",
    "    elif 'child' in str(title).lower() or 'Child' in str(title) or 'Preschool' in str(title):\n",
    "        return 'Child Educations'\n",
    "    elif 'Tour' in str(title) or 'tour'  in str(title).lower():\n",
    "        return 'Tourisms'\n",
    "    elif 'Gam' in str(title) or 'game' in str(title).lower():\n",
    "        return 'Gaming'\n",
    "    elif 'education' in str(title).lower() or 'Education' in str(title) or 'ed' in str(title).lower() or 'acad' in str(title) or 'Dploma' in str(title):\n",
    "        return 'B.Ed or M.Ed'\n",
    "    elif 'Sport' in str(title) or 'sport' in str(title).lower():\n",
    "        return 'Sports'\n",
    "    elif 'Petro' in str(title):\n",
    "        return 'Petroleum'\n",
    "    elif 'Journ' in str(title) or 'jour' in str(title).lower() or 'Joun' in str(title) or 'Jurn' in str(title):\n",
    "        return 'Journalism'\n",
    "    elif 'Mandarin' in str(title):\n",
    "        return 'Chinese/Mandarin Languages'\n",
    "    elif 'Electrician' in str(title):\n",
    "        return 'Electrician'\n",
    "    elif 'Network' in str(title) or 'network' in str(title).lower():\n",
    "        return 'Networking'\n",
    "    elif 'geo' in str(title).lower() or 'GEO' in str(title):\n",
    "        return 'Geography'\n",
    "    elif 'Librarian' in str(title) or 'lib' in str(title).lower():\n",
    "        return 'Librarian'\n",
    "    elif 'Mission' in str(title) or 'mission' in str(title).lower():\n",
    "        return 'Missionary Studies'\n",
    "    elif 'Forensic' in str(title) or 'foren' in str(title).lower() or 'Crime' in str(title) or 'crim' in str(title).lower():\n",
    "        return 'Forensic/Criminal studies'\n",
    "    elif 'Animation' in str(title) or 'animation' in str(title).lower() or 'imag' in str(title) or 'graphic' in str(title) or 'Graphic' in str(title):\n",
    "        return 'Animations'\n",
    "    elif 'aqua' in str(title).lower() or 'Aqu' in str(title):\n",
    "        return 'Aquaculture'\n",
    "    elif 'soldier' in str(title).lower() or 'lwa' in str(title).lower() or 'defence' in str(title):\n",
    "        return 'Army'\n",
    "    elif 'Kinesi' in str(title) or 'kines' in str(title).lower() or 'hod' in str(title):\n",
    "        return 'Human Kinetics'\n",
    "    elif 'Horti' in str(title) or 'horti' in str(title) or 'Landscape' in str(title):\n",
    "        return 'Horticulture'\n",
    "    elif 'commerce' in str(title).lower() or 'Coome' in str(title):\n",
    "        return 'Commerce'\n",
    "    elif 'Speech' in str(title) or 'speech' in str(title).lower():\n",
    "        return 'Speech Pathology'\n",
    "    elif 'SECRET' in str(title) or 'secret' in str(title).lower():\n",
    "        return 'Secretary'\n",
    "    elif 'Animals' in str(title) or 'animal' in str(title).lower() or 'Pet' in str(title):\n",
    "        return 'Animal Care'\n",
    "    elif 'Organisation' in str(title) or 'organi' in str(title).lower():\n",
    "        return 'Organizational Behaviour'\n",
    "    elif 'event' in str(title).lower() or 'Event' in str(title):\n",
    "        return 'Event Managment'\n",
    "    elif 'radiology' in str(title).lower() or 'Radiography' in str(title) or 'radiograpghy' in str(title).lower() or 'Radiation' in str(title) or 'radiography' in str(title):\n",
    "        return 'Radiography'\n",
    "    elif 'nutrition' in str(title).lower() or 'Nutrition' in str(title):\n",
    "        return 'Nutritionist'\n",
    "    elif 'Audit' in str(title) or 'audit' in str(title).lower():\n",
    "        return 'Auditing'\n",
    "    elif 'Neuro' in str(title) or 'neuroligy' in str(title).lower():\n",
    "        return 'Neurology'\n",
    "    elif 'Anato' in str(title) or 'anat' in str(title).lower():\n",
    "        return 'Anatomy'\n",
    "    elif 'trade' in str(title).lower():\n",
    "        return 'Trading'\n",
    "    elif 'Interpre' in str(title) or 'translation' in str(title).lower():\n",
    "        return 'Interpreter'\n",
    "    elif 'audio' in str(title).lower() or 'Audio' in str(title):\n",
    "        return 'Audiology'\n",
    "    elif 'insurance' in str(title).lower() or 'Insurance' in str(title):\n",
    "        return 'Insurances'\n",
    "    elif 'archaeology' in str(title).lower() or 'archaeology' in str(title).lower() or 'archeology' in str(title).lower() or 'treasury' in str(title):\n",
    "        return 'Archeology'\n",
    "    elif 'SERV'in str(title) or 'service' in str(title).lower():\n",
    "        return 'Service Training'\n",
    "    elif 'GERMAN' in str(title) or 'german' in str(title).lower():\n",
    "        return 'German'\n",
    "    elif 'KOREAN' in str(title) or 'Korea' in str(title):\n",
    "        return 'Korean'\n",
    "    elif 'valuat' in str(title).lower() or 'valuer' in str(title).lower():\n",
    "        return 'Registered Valuer'\n",
    "    elif 'skil' in str(title).lower() or 'Skill' in str(title) or 'Professional' in str(title) or 'practical' in str(title).lower():\n",
    "        return 'Skilled Labour'\n",
    "    elif 'virology' in str(title):\n",
    "        return 'Virology'\n",
    "    elif 'lab' in str(title).lower() or 'Lab' in str(title) or 'MLT' in str(title):\n",
    "        return 'Laboratory Worker'\n",
    "    elif 'GENERAL' in str(title) or 'General' in str(title):\n",
    "        return 'General'\n",
    "    elif 'Opto' in str(title) or 'opto' in str(title).lower():\n",
    "        return 'Optometry'\n",
    "    elif 'Zoo' in str(title) or 'zoo' in str(title).lower():\n",
    "        return 'Zoology'\n",
    "    elif 'office' in str(title).lower() or 'Office' in str(title):\n",
    "        return 'Office Skills'\n",
    "    elif 'found' in str(title).lower() or 'Found' in str(title):\n",
    "        return 'Foundation Education'\n",
    "    elif 'general' in str(title).lower() or 'General' in str(title):\n",
    "        return 'General Education'\n",
    "    elif 'real estate' in str(title).lower() or 'property' in str(title).lower():\n",
    "        return 'Realtor'\n",
    "    elif 'Meteorology' in str(title) or 'Metrology' in str(title):\n",
    "        return 'Meterology'\n",
    "    elif 'operations' in str(title).lower() or 'Operation' in str(title):\n",
    "        return 'Operational Managment'\n",
    "    elif 'Merchandising' in str(title) or 'merchand' in str(title).lower():\n",
    "        return 'Merchandising'\n",
    "    elif 'Spanish' in str(title):\n",
    "        return 'Spanish'\n",
    "    elif 'Nature' in str(title) or 'natur' in str(title).lower():\n",
    "        return 'Nature Conservation/Resources'\n",
    "    elif title=='a level ' or title==' ':\n",
    "        return 'No Degree'\n",
    "    elif 'Corporate' in str(title) or 'corporate' in str(title).lower():\n",
    "        return 'Corporate'\n",
    "    elif 'greek' in str(title).lower() or 'Greek' in str(title):\n",
    "        return 'Greek'\n",
    "    elif 'Behaviour' in str(title) or 'Behavior' in str(title) or 'Organizational Behaviour' in str(title):\n",
    "        return 'Behaviour Analysis'\n",
    "    elif 'publish' in str(title).lower():\n",
    "        return 'Publishing'\n",
    "    elif 'Safety' in str(title) or 'safety' in str(title).lower():\n",
    "        return 'Safety Training'\n",
    "    elif 'genetic' in str(title).lower() or 'Genetic' in str(title):\n",
    "        return 'Genetics'\n",
    "    elif 'Dietetic' in str(title):\n",
    "        return 'Dietician'\n",
    "    elif 'Production' in str(title) or 'manufacturing' in str(title).lower():\n",
    "        return 'Production And Manufacturing'\n",
    "    elif 'Welding' in str(title):\n",
    "        return 'Welding'\n",
    "    elif 'Geron' in str(title):\n",
    "        return 'Gerontology'\n",
    "    elif 'Research' in str(title) or 'Ph D' in str(title):\n",
    "        return 'Ph.D'\n",
    "    elif 'arabic' in str(title).lower() or 'Arabic' in str(title):\n",
    "        return 'Arabic'\n",
    "\n",
    "    else:\n",
    "        return \"Others\"\n",
    "\n",
    "normalized_majors = normalized_majors.apply(lambda x: condition(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "major_list = list(set(normalized_majors.values))\n",
    "\n",
    "def encode_majors(major):\n",
    "    return major_list.index(major)\n",
    "\n",
    "normalized_majors = normalized_majors.apply(lambda x: encode_majors(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration on Demographic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>education</th>\n",
       "      <th>urban</th>\n",
       "      <th>gender</th>\n",
       "      <th>engnat</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.00000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "      <td>39775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.503834</td>\n",
       "      <td>2.220264</td>\n",
       "      <td>1.789541</td>\n",
       "      <td>1.635852</td>\n",
       "      <td>23.612168</td>\n",
       "      <td>1.13516</td>\n",
       "      <td>7.555852</td>\n",
       "      <td>1.642992</td>\n",
       "      <td>31.312885</td>\n",
       "      <td>1.159547</td>\n",
       "      <td>3.510270</td>\n",
       "      <td>43.941647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.885414</td>\n",
       "      <td>0.804761</td>\n",
       "      <td>0.444180</td>\n",
       "      <td>0.483906</td>\n",
       "      <td>21.581722</td>\n",
       "      <td>0.40030</td>\n",
       "      <td>3.554395</td>\n",
       "      <td>1.351362</td>\n",
       "      <td>25.871272</td>\n",
       "      <td>0.445882</td>\n",
       "      <td>2.141518</td>\n",
       "      <td>25.397999</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>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          education         urban        gender        engnat           age  \\\n",
       "count  39775.000000  39775.000000  39775.000000  39775.000000  39775.000000   \n",
       "mean       2.503834      2.220264      1.789541      1.635852     23.612168   \n",
       "std        0.885414      0.804761      0.444180      0.483906     21.581722   \n",
       "min        0.000000      0.000000      0.000000      0.000000     13.000000   \n",
       "25%        2.000000      2.000000      2.000000      1.000000     18.000000   \n",
       "50%        3.000000      2.000000      2.000000      2.000000     21.000000   \n",
       "75%        3.000000      3.000000      2.000000      2.000000     25.000000   \n",
       "max        4.000000      3.000000      3.000000      2.000000   1998.000000   \n",
       "\n",
       "              hand      religion   orientation          race       married  \\\n",
       "count  39775.00000  39775.000000  39775.000000  39775.000000  39775.000000   \n",
       "mean       1.13516      7.555852      1.642992     31.312885      1.159547   \n",
       "std        0.40030      3.554395      1.351362     25.871272      0.445882   \n",
       "min        0.00000      0.000000      0.000000     10.000000      0.000000   \n",
       "25%        1.00000      4.000000      1.000000     10.000000      1.000000   \n",
       "50%        1.00000     10.000000      1.000000     10.000000      1.000000   \n",
       "75%        1.00000     10.000000      2.000000     60.000000      1.000000   \n",
       "max        3.00000     12.000000      5.000000     70.000000      3.000000   \n",
       "\n",
       "         familysize         major  \n",
       "count  39775.000000  39775.000000  \n",
       "mean       3.510270     43.941647  \n",
       "std        2.141518     25.397999  \n",
       "min        0.000000      0.000000  \n",
       "25%        2.000000     15.000000  \n",
       "50%        3.000000     55.000000  \n",
       "75%        4.000000     55.000000  \n",
       "max      133.000000    101.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"major\"] = normalized_majors\n",
    "df[demography_columns].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An initial view of the summary of the demography columns show that most of the columns correctly follow the stated classes provided, especially since most of them are categorical variables. However, the age columns raises an issue as the maximum age of 1998 does not seem to make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df[\"age\"] > 100].index, inplace=True)\n",
    "# df[\"age\"].hist(bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
    "# plt.grid(False)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>education</th>\n",
       "      <th>urban</th>\n",
       "      <th>gender</th>\n",
       "      <th>engnat</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>married</th>\n",
       "      <th>...</th>\n",
       "      <th>Q21A</th>\n",
       "      <th>Q24A</th>\n",
       "      <th>Q26A</th>\n",
       "      <th>Q31A</th>\n",
       "      <th>Q34A</th>\n",
       "      <th>Q37A</th>\n",
       "      <th>Q38A</th>\n",
       "      <th>Q42A</th>\n",
       "      <th>total_score</th>\n",
       "      <th>severity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>Extremely Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>Extremely Severe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  urban  gender  engnat  age  hand  religion  orientation  race  \\\n",
       "0          2      3       2       2   16     1        12            1    10   \n",
       "1          2      3       2       1   16     2         7            0    70   \n",
       "2          2      3       2       2   17     1         4            3    60   \n",
       "3          1      3       2       1   13     2         4            5    70   \n",
       "4          3      2       2       2   19     3        10            1    10   \n",
       "\n",
       "   married  ...  Q21A  Q24A  Q26A  Q31A  Q34A  Q37A  Q38A  Q42A  total_score  \\\n",
       "0        1  ...     0     3     3     3     2     0     1     3           27   \n",
       "1        1  ...     1     1     2     1     1     3     1     1           24   \n",
       "2        1  ...     3     3     0     3     3     3     3     3           39   \n",
       "3        1  ...     0     0     1     2     3     1     0     1           16   \n",
       "4        1  ...     2     1     3     2     3     2     2     2           32   \n",
       "\n",
       "           severity  \n",
       "0            Severe  \n",
       "1            Severe  \n",
       "2  Extremely Severe  \n",
       "3          Moderate  \n",
       "4  Extremely Severe  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for dass depression severity scale and go to images\n",
    "def depression_severity(score):\n",
    "\n",
    "    if score <= 9:\n",
    "        severity = \"Normal\"\n",
    "    elif score >= 10 and score <= 13:\n",
    "        severity = \"Mild\"\n",
    "    elif score >= 14 and score <= 20:\n",
    "        severity = \"Moderate\" \n",
    "    elif score >= 21 and score <= 27:\n",
    "        severity = \"Severe\" \n",
    "    elif score >= 28:\n",
    "        severity = \"Extremely Severe\"\n",
    "    \n",
    "    return severity\n",
    "\n",
    "# Select the relevant columns from the original dataframe.\n",
    "depression_df = df[demography_columns + depression_question_columns].copy()\n",
    "\n",
    "# The data in the dataset usees a scale from 1 to 4 while the original dass score uses a scale of 0 to 3\n",
    "# Hence, the scores for each question is subtracted by 1\n",
    "depression_df[depression_question_columns] = depression_df[depression_question_columns].subtract(1)\n",
    "depression_df[\"total_score\"] = depression_df[depression_question_columns].sum(axis=1)\n",
    "\n",
    "depression_df[\"severity\"] = depression_df[\"total_score\"].apply(lambda x: depression_severity(x))\n",
    "depression_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "depression_df[\"severity\"].hist()\n",
    "plt.grid(False)\n",
    "plt.title(\"Depression Severity Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAE/CAYAAAAKbMRsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc4UlEQVR4nO3df7DddX3n8edrE7UqWkBuU0iIwRrbottGSZFOtWtLhYCO0N0uhbWSUmrKCLO642432ulgtbTYrbVltDhUUsJWQVa0ZGssTanVdrYgQRh+ShMRJDGESESoWCrw3j/O5+ox3ssN997kc8/l+Zg5c77n/fl8v9/P4czJvPh+Pt9zU1VIkiSpj3/XewCSJElPZ4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJmleS/LOJB+exeP9S5IXte1LkvzuLB77Q0l+e7aOJ2k0GMYk7RNJ/j7J15M8ax+f41+TPJzkoSQ3JFk7fM6q+r2q+vW9PNaU/arqgKq6axbG/qtJ/nGPY59VVe+Z6bEljRbDmKRZl2QZ8GqggDfs49OdU1XPAw4F3g6cCmxMktk8SZKFs3k8SRpnGJO0L5wOXAtcAqwebkjygiT/t13Juj7J7w5fIUryY0k2Jdmd5M4kp+zNCavqm1X19wzC308Dr2vHe1eSv2jbP5DkL5I8kOTBdv5FSc5jEB4/0KYhP9D6V5Kzk2wBtgzVXjx06kPaeB9O8tkkL2z9lrW+3wlx41ffkvw48CHgp9v5Hmzt3zPtmeTNSba2/xYbkhw21FZJzkqypb2XD852AJW0fxjGJO0LpwMfaY/jkywaavsg8E3ghxkEte+EtSTPBTYBHwV+iMFVrj9NcuTenriqvgJsZhCu9rQa+EHgcOAFwFnAt6rqt4B/YHCV7YCqOmdon5OBVwKTjeGNwHuAQ4Cb2nueaox3tHP/UzvfgXv2SfLzwO8DpzC46ncPcPke3V4P/BTwE63f8VOdW9LcYxiTNKuSvAp4IXBFVd0AfAn4L61tAfCfgHOr6pGquh1YP7T764G7q+rPq+qxqroRuBL4z09xGF8FDp6g/m0GIezFVfV4Vd1QVQ9Ncazfr6rdVfWtSdo/VVWfq6pHgd9icLXr8Kc43om8EVhXVV9ox35HO/ayoT7nV9WDLYB+BlgxC+eVtJ8ZxiTNttXA31TV19rrj/Ldq19jwELg3qH+w9svBF7Zpt0ebNN3b2RwFe2pWAzsnqD+v4GrgcuTfDXJHyR5xhTHundv26vqX9p5D5u8+147jMHVsOFjP8DgvY27b2j7EeCAWTivpP3MBamSZk2SZzOYLluQZDwoPAs4MMlPArcCjwFLgH9u7cNXke4FPltVr53BGA4HjgLeu2dbVX0b+B3gd9oVpo3AncDFDG42mMhk9XHfGX+SAxhckfsq8K+t/Bxg/OrbcKic6rhfZRBOx4/9XAZX9bZPsZ+kEeOVMUmz6WTgcQbrq1a0x48zWI91elU9DnwCeFeS5yT5MQbry8b9FfCSJG9K8oz2+Km24P1JteP9B+Aq4PMMgtaefX4uyb9v06UPMZi2fKI17wReNI33fGKSVyV5JoO1Y9dW1b1VtYtBcPqVJAuS/BrwI0P77QSWtP0mchlwRpIV7ac6fg+4rqrunsYYJc1hhjFJs2k18OdV9ZWqum/8AXwAeGO7s/AcBovo72MwbXgZ8ChAVT0MHMdg4f5XW5/3Mri6NpkPJHmYQbj5YwZrzFZV1RMT9P1h4OMMgtgdwGfbGAD+BPil9ttoFzyF9/xR4FwG05NHAb8y1PZm4H8wmF58KfD/htr+DrgNuC/J19hDVf0t8Nvt/exgEOROfQrjkjQiUjXVlXJJ2neSvBf44apaPWVnSZqHvDImab9qvyP2Exk4GjgT+GTvcUlSLy7gl7S/PY/B1ORhDKYW38dgnZckPS05TSlJktSR05SSJEkdGcYkSZI6Gtk1Y4ccckgtW7as9zAkSZKmdMMNN3ytqsYmahvZMLZs2TI2b97cexiSJElTSnLPZG1OU0qSJHVkGJMkSerIMCZJktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLU0cj+bcr9YdnaT/Uewqy5+/zX9R6CJEmagFfGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjqaMowlOTzJZ5LcnuS2JG9t9YOTbEqypT0f1OpJckGSrUluTvKKoWOtbv23JFk9VD8qyS1tnwuSZF+8WUmSpLlmb66MPQa8vaqOBI4Bzk5yJLAWuKaqlgPXtNcAJwDL22MNcCEMwhtwLvBK4Gjg3PEA1/q8eWi/VTN/a5IkSXPflGGsqnZU1Rfa9sPAHcBi4CRgfeu2Hji5bZ8EXFoD1wIHJjkUOB7YVFW7q+rrwCZgVWt7flVdW1UFXDp0LEmSpHntKa0ZS7IMeDlwHbCoqna0pvuARW17MXDv0G7bWu3J6tsmqEuSJM17ex3GkhwAXAm8raoeGm5rV7Rqlsc20RjWJNmcZPOuXbv29ekkSZL2ub0KY0mewSCIfaSqPtHKO9sUI+35/lbfDhw+tPuSVnuy+pIJ6t+nqi6qqpVVtXJsbGxvhi5JkjSn7c3dlAEuBu6oqj8aatoAjN8RuRq4aqh+erur8hjgG20682rguCQHtYX7xwFXt7aHkhzTznX60LEkSZLmtYV70edngDcBtyS5qdXeCZwPXJHkTOAe4JTWthE4EdgKPAKcAVBVu5O8B7i+9Xt3Ve1u228BLgGeDXy6PSRJkua9KcNYVf0jMNnvfh07Qf8Czp7kWOuAdRPUNwMvm2oskiRJ842/wC9JktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpI8OYJElSR4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjqaMowlWZfk/iS3DtU+luSm9rg7yU2tvizJt4baPjS0z1FJbkmyNckFSdLqByfZlGRLez5oH7xPSZKkOWlvroxdAqwaLlTVL1fViqpaAVwJfGKo+UvjbVV11lD9QuDNwPL2GD/mWuCaqloOXNNeS5IkPS1MGcaq6nPA7ona2tWtU4DLnuwYSQ4Fnl9V11ZVAZcCJ7fmk4D1bXv9UF2SJGnem+masVcDO6tqy1DtiCQ3Jvlskle32mJg21Cfba0GsKiqdrTt+4BFMxyTJEnSyFg4w/1P43uviu0AllbVA0mOAv4yyUv39mBVVUlqsvYka4A1AEuXLp3mkCVJkuaOaV8ZS7IQ+I/Ax8ZrVfVoVT3Qtm8AvgS8BNgOLBnafUmrAexs05jj05n3T3bOqrqoqlZW1cqxsbHpDl2SJGnOmMk05S8AX6yq70w/JhlLsqBtv4jBQv272jTkQ0mOaevMTgeuarttAFa37dVDdUmSpHlvb37a4jLgn4AfTbItyZmt6VS+f+H+zwI3t5+6+DhwVlWNL/5/C/BhYCuDK2afbvXzgdcm2cIg4J0//bcjSZI0WqZcM1ZVp01S/9UJalcy+KmLifpvBl42Qf0B4NipxiFJkjQf+Qv8kiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHVkGJMkSerIMCZJktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLU0ZRhLMm6JPcnuXWo9q4k25Pc1B4nDrW9I8nWJHcmOX6ovqrVtiZZO1Q/Isl1rf6xJM+czTcoSZI0l+3NlbFLgFUT1N9fVSvaYyNAkiOBU4GXtn3+NMmCJAuADwInAEcCp7W+AO9tx3ox8HXgzJm8IUmSpFEyZRirqs8Bu/fyeCcBl1fVo1X1ZWArcHR7bK2qu6rq34DLgZOSBPh54ONt//XAyU/tLUiSJI2umawZOyfJzW0a86BWWwzcO9RnW6tNVn8B8GBVPbZHXZIk6WlhumHsQuBHgBXADuB9szWgJ5NkTZLNSTbv2rVrf5xSkiRpn5pWGKuqnVX1eFU9AfwZg2lIgO3A4UNdl7TaZPUHgAOTLNyjPtl5L6qqlVW1cmxsbDpDlyRJmlOmFcaSHDr08heB8TstNwCnJnlWkiOA5cDngeuB5e3OyWcyWOS/oaoK+AzwS23/1cBV0xmTJEnSKFo4VYcklwGvAQ5Jsg04F3hNkhVAAXcDvwFQVbcluQK4HXgMOLuqHm/HOQe4GlgArKuq29op/idweZLfBW4ELp6tNydJkjTXTRnGquq0CcqTBqaqOg84b4L6RmDjBPW7+O40pyRJ0tOKv8AvSZLUkWFMkiSpI8OYJElSR4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0t7D0A7R/L1n6q9xBmzd3nv673ECRJmjVeGZMkSerIMCZJktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1NGUYS7Iuyf1Jbh2q/a8kX0xyc5JPJjmw1Zcl+VaSm9rjQ0P7HJXkliRbk1yQJK1+cJJNSba054P2wfuUJEmak/bmytglwKo9apuAl1XVTwD/DLxjqO1LVbWiPc4aql8IvBlY3h7jx1wLXFNVy4Fr2mtJkqSnhSnDWFV9Dti9R+1vquqx9vJaYMmTHSPJocDzq+raqirgUuDk1nwSsL5trx+qS5IkzXuzsWbs14BPD70+IsmNST6b5NWtthjYNtRnW6sBLKqqHW37PmDRLIxJkiRpJMzoD4Un+S3gMeAjrbQDWFpVDyQ5CvjLJC/d2+NVVSWpJznfGmANwNKlS6c/cEmSpDli2lfGkvwq8HrgjW3qkap6tKoeaNs3AF8CXgJs53unMpe0GsDONo05Pp15/2TnrKqLqmplVa0cGxub7tAlSZLmjGmFsSSrgN8E3lBVjwzVx5IsaNsvYrBQ/642DflQkmPaXZSnA1e13TYAq9v26qG6JEnSvDflNGWSy4DXAIck2Qacy+DuyWcBm9ovVFzb7pz8WeDdSb4NPAGcVVXji//fwuDOzGczWGM2vs7sfOCKJGcC9wCnzMo7kyRJGgFThrGqOm2C8sWT9L0SuHKSts3AyyaoPwAcO9U4JEmS5iN/gV+SJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpI8OYJElSR4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHW0V2Esybok9ye5dah2cJJNSba054NaPUkuSLI1yc1JXjG0z+rWf0uS1UP1o5Lc0va5IElm801KkiTNVXt7ZewSYNUetbXANVW1HLimvQY4AVjeHmuAC2EQ3oBzgVcCRwPnjge41ufNQ/vteS5JkqR5aa/CWFV9Dti9R/kkYH3bXg+cPFS/tAauBQ5McihwPLCpqnZX1deBTcCq1vb8qrq2qgq4dOhYkiRJ89pM1owtqqodbfs+YFHbXgzcO9RvW6s9WX3bBPXvk2RNks1JNu/atWsGQ5ckSZobZmUBf7uiVbNxrCnOc1FVrayqlWNjY/v6dJIkSfvcTMLYzjbFSHu+v9W3A4cP9VvSak9WXzJBXZIkad6bSRjbAIzfEbkauGqofnq7q/IY4BttOvNq4LgkB7WF+8cBV7e2h5Ic0+6iPH3oWJIkSfPawr3plOQy4DXAIUm2Mbgr8nzgiiRnAvcAp7TuG4ETga3AI8AZAFW1O8l7gOtbv3dX1fhNAW9hcMfms4FPt4ckSdK8t1dhrKpOm6Tp2An6FnD2JMdZB6yboL4ZeNnejEWSJGk+8Rf4JUmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHVkGJMkSerIMCZJktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpo2mHsSQ/muSmocdDSd6W5F1Jtg/VTxza5x1Jtia5M8nxQ/VVrbY1ydqZvilJkqRRsXC6O1bVncAKgCQLgO3AJ4EzgPdX1R8O909yJHAq8FLgMOBvk7ykNX8QeC2wDbg+yYaqun26Y5MkSRoV0w5jezgW+FJV3ZNksj4nAZdX1aPAl5NsBY5ubVur6i6AJJe3voYxSZI0783WmrFTgcuGXp+T5OYk65Ic1GqLgXuH+mxrtcnqkiRJ896Mw1iSZwJvAP5PK10I/AiDKcwdwPtmeo6hc61JsjnJ5l27ds3WYSVJkrqZjStjJwBfqKqdAFW1s6oer6ongD/ju1OR24HDh/Zb0mqT1b9PVV1UVSurauXY2NgsDF2SJKmv2QhjpzE0RZnk0KG2XwRubdsbgFOTPCvJEcBy4PPA9cDyJEe0q2yntr6SJEnz3owW8Cd5LoO7IH9jqPwHSVYABdw93lZVtyW5gsHC/MeAs6vq8Xacc4CrgQXAuqq6bSbjkiRJGhUzCmNV9U3gBXvU3vQk/c8DzpugvhHYOJOxSJIkjSJ/gV+SJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpI8OYJElSR4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHU04zCW5O4ktyS5KcnmVjs4yaYkW9rzQa2eJBck2Zrk5iSvGDrO6tZ/S5LVMx2XJEnSKJitK2M/V1Urqmple70WuKaqlgPXtNcAJwDL22MNcCEMwhtwLvBK4Gjg3PEAJ0mSNJ/tq2nKk4D1bXs9cPJQ/dIauBY4MMmhwPHApqraXVVfBzYBq/bR2CRJkuaM2QhjBfxNkhuSrGm1RVW1o23fByxq24uBe4f23dZqk9UlSZLmtYWzcIxXVdX2JD8EbEryxeHGqqokNQvnoYW9NQBLly6djUNKkiR1NeMrY1W1vT3fD3ySwZqvnW36kfZ8f+u+HTh8aPclrTZZfc9zXVRVK6tq5djY2EyHLkmS1N2MwliS5yZ53vg2cBxwK7ABGL8jcjVwVdveAJze7qo8BvhGm868GjguyUFt4f5xrSZJkjSvzXSachHwySTjx/poVf11kuuBK5KcCdwDnNL6bwROBLYCjwBnAFTV7iTvAa5v/d5dVbtnODZJkqQ5b0ZhrKruAn5ygvoDwLET1As4e5JjrQPWzWQ8kiRJo8Zf4JckSerIMCZJktSRYUySJKkjw5gkSVJHhjFJkqSODGOSJEkdGcYkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpI8OYJElSR4YxSZKkjgxjkiRJHRnGJEmSOjKMSZIkdWQYkyRJ6sgwJkmS1JFhTJIkqSPDmCRJUkeGMUmSpI6mHcaSHJ7kM0luT3Jbkre2+ruSbE9yU3ucOLTPO5JsTXJnkuOH6qtabWuStTN7S5IkSaNj4Qz2fQx4e1V9IcnzgBuSbGpt76+qPxzunORI4FTgpcBhwN8meUlr/iDwWmAbcH2SDVV1+wzGJkmSNBKmHcaqagewo20/nOQOYPGT7HIScHlVPQp8OclW4OjWtrWq7gJIcnnraxiTJEnz3qysGUuyDHg5cF0rnZPk5iTrkhzUaouBe4d229Zqk9UlSZLmvZlMUwKQ5ADgSuBtVfVQkguB9wDVnt8H/NpMz9POtQZYA7B06dLZOKRG0LK1n+o9hFlx9/mv6z0ESdIcMKMrY0mewSCIfaSqPgFQVTur6vGqegL4M747FbkdOHxo9yWtNln9+1TVRVW1sqpWjo2NzWTokiRJc8JM7qYMcDFwR1X90VD90KFuvwjc2rY3AKcmeVaSI4DlwOeB64HlSY5I8kwGi/w3THdckiRJo2Qm05Q/A7wJuCXJTa32TuC0JCsYTFPeDfwGQFXdluQKBgvzHwPOrqrHAZKcA1wNLADWVdVtMxiXJEnSyJjJ3ZT/CGSCpo1Pss95wHkT1Dc+2X6SJEnzlb/AL0mS1JFhTJIkqSPDmCRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHVkGJMkSerIMCZJktSRYUySJKmjmfxtSkkzsGztp3oPYdbcff7reg9BkkaWV8YkSZI6MoxJkiR1ZBiTJEnqyDAmSZLUkWFMkiSpI8OYJElSR/60haQZ82c6JGn6vDImSZLUkWFMkiSpI8OYJElSR64Zk6Qhrn+TtL/NmStjSVYluTPJ1iRre49HkiRpf5gTYSzJAuCDwAnAkcBpSY7sOypJkqR9b06EMeBoYGtV3VVV/wZcDpzUeUySJEn73FxZM7YYuHfo9TbglZ3GIknzwnxa/zZfuI5PE5krYWyvJFkDrGkv/yXJnT3HM0ccAnyt9yA06/xc5yc/1/lprz/XvHcfj0Szaba/ry+crGGuhLHtwOFDr5e02veoqouAi/bXoEZBks1VtbL3ODS7/FznJz/X+cnPdX7an5/rXFkzdj2wPMkRSZ4JnAps6DwmSZKkfW5OXBmrqseSnANcDSwA1lXVbZ2HJUmStM/NiTAGUFUbgY29xzGCnLadn/xc5yc/1/nJz3V+2m+fa6pqf51LkiRJe5gra8YkSZKelgxjIyTJ4Uk+k+T2JLcleWurH5xkU5It7fmg3mPVU5dkQZIbk/xVe31Ekuvanwj7WLu5RSMkyYFJPp7ki0nuSPLTfl9HX5L/1v4NvjXJZUl+wO/r6EmyLsn9SW4dqk34/czABe3zvTnJK2ZzLIax0fIY8PaqOhI4Bji7/dmotcA1VbUcuKa91uh5K3DH0Ov3Au+vqhcDXwfO7DIqzcSfAH9dVT8G/CSDz9fv6whLshj4r8DKqnoZg5vOTsXv6yi6BFi1R22y7+cJwPL2WANcOJsDMYyNkKraUVVfaNsPM/iHfTGDPx21vnVbD5zcZYCatiRLgNcBH26vA/w88PHWxc91xCT5QeBngYsBqurfqupB/L7OBwuBZydZCDwH2IHf15FTVZ8Ddu9Rnuz7eRJwaQ1cCxyY5NDZGothbEQlWQa8HLgOWFRVO1rTfcCiXuPStP0x8JvAE+31C4AHq+qx9nobg+Ct0XEEsAv48zb9/OEkz8Xv60irqu3AHwJfYRDCvgHcgN/X+WKy7+dEf7Zx1j5jw9gISnIAcCXwtqp6aLitBrfHeovsCEnyeuD+qrqh91g0qxYCrwAurKqXA99kjylJv6+jp60hOolB2D4MeC7fP9WleWB/fj8NYyMmyTMYBLGPVNUnWnnn+OXS9nx/r/FpWn4GeEOSu4HLGUx3/AmDy+DjvwU44Z8I05y2DdhWVde11x9nEM78vo62XwC+XFW7qurbwCcYfIf9vs4Pk30/9+rPNk6XYWyEtHVEFwN3VNUfDTVtAFa37dXAVft7bJq+qnpHVS2pqmUMFgL/XVW9EfgM8Eutm5/riKmq+4B7k/xoKx0L3I7f11H3FeCYJM9p/yaPf65+X+eHyb6fG4DT212VxwDfGJrOnDF/9HWEJHkV8A/ALXx3bdE7GawbuwJYCtwDnFJVey5K1AhI8hrgv1fV65O8iMGVsoOBG4FfqapHOw5PT1GSFQxuyngmcBdwBoP/Cfb7OsKS/A7wywzucL8R+HUG64f8vo6QJJcBrwEOAXYC5wJ/yQTfzxa8P8BgSvoR4Iyq2jxrYzGMSZIk9eM0pSRJUkeGMUmSpI4MY5IkSR0ZxiRJkjoyjEmSJHVkGJMkSerIMCZJktSRYUySJKmj/w9QGxvFZNs7vwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "depression_df[\"age\"].hist()\n",
    "plt.grid(False)\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_labels(label):\n",
    "    classes = [\"Normal\", \"Mild\", \"Moderate\", \"Severe\", \"Extremely Severe\"]\n",
    "\n",
    "    return classes.index(label)\n",
    "\n",
    "label_encoded = depression_df[\"severity\"].apply(lambda x: convert_labels(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depression_df[\"severity_class\"] = label_encoded\n",
    "\n",
    "plt.figure(figsize=(5,10))\n",
    "heatmap = sns.heatmap(depression_df.corr()[[\"severity_class\"]].sort_values(by=\"severity_class\", ascending=False), vmin=-1, vmax=1, annot=True)\n",
    "heatmap.set_title(\"Feature Correlation with Depression Severity\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap shows that there exists high correlation between the scores of the individual questions and the severity of depression. This tallies with the fact that in the original DAS assessment, the severity is dependent on the sum of these scores and therefore, higher scores per question results in more severe conditions. However, when modelling, total score would not be used as a feature as this could result in redundancy with the original scores.\n",
    "\n",
    "The demographical features, although showing lower correlation, still have potential to model the severity of depression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling and Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['education', 'urban', 'gender', 'engnat', 'age', 'hand', 'religion',\n",
      "       'orientation', 'race', 'married', 'familysize', 'major', 'Q3A', 'Q5A',\n",
      "       'Q10A', 'Q13A', 'Q16A', 'Q17A', 'Q21A', 'Q24A', 'Q26A', 'Q31A', 'Q34A',\n",
      "       'Q37A', 'Q38A', 'Q42A', 'total_score', 'severity', 'severity_class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(depression_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23860, 26)\n",
      "(15908, 26)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "\n",
    "X = depression_df.drop([\"severity\", \"total_score\", \"severity_class\"], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label_encoded, stratify=label_encoded, train_size=0.6)\n",
    "scaler=MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=500)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(max_iter=500)\n",
    "clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test_scaled, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      3542\n",
      "           1       1.00      1.00      1.00      1515\n",
      "           2       1.00      1.00      1.00      2831\n",
      "           3       1.00      1.00      1.00      2591\n",
      "           4       1.00      1.00      1.00      5429\n",
      "\n",
      "    accuracy                           1.00     15908\n",
      "   macro avg       1.00      1.00      1.00     15908\n",
      "weighted avg       1.00      1.00      1.00     15908\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr = classification_report(y_test, y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('scaler', scaler), ('log_reg', clf)])\n",
    "\n",
    "model_date = datetime.now().strftime(\"%d%m%Y\")\n",
    "pkl_filename = f\"models/dass_demography_lr_pipe_{model_date}.pkl\"\n",
    "\n",
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(pipe, file)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f63f9a4941f5ced28d26fcf65c2dd5821b4bf0a34a4db067c79f512a822b48c3"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}