├── README.md
├── Student Mental health.csv
└── stud-mental-helalth.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # EDA on Student Mental health Analysis Using Python
 2 | This repository contains an Exploratory Data Analysis (EDA) project aimed at understanding the mental health status of students using a dataset from Kaggle.
 3 | ## Overview
 4 | Mental health is a critical aspect of students' well-being. This project explores various factors affecting students' mental health through data analysis and visualization.
 5 | ## Analysis
 6 | The notebook performs the following steps:
 7 | 1. **Data Extraction**: Using Kaggle API to download and extract the dataset.
 8 | 2. **Library Imports**: Importing essential libraries.
 9 | 3. **Data Loading**: Loading the dataset into pandas DataFrame.
10 | 4. **Data Cleaning**: Handling missing values and data types.
11 | 5.**Visualization**: Creating plots for data visualization.
12 | 6. **Statistical** Analysis: Performing statistical tests and analyses.
13 | 


--------------------------------------------------------------------------------
/Student Mental health.csv:
--------------------------------------------------------------------------------
  1 | Timestamp,Choose your gender,Age,What is your course?,Your current year of Study,What is your CGPA?,Marital status,Do you have Depression?,Do you have Anxiety?,Do you have Panic attack?,Did you seek any specialist for a treatment?
  2 | 8/7/2020 12:02,Female,18,Engineering,year 1,3.00 - 3.49,No,Yes,No,Yes,No
  3 | 8/7/2020 12:04,Male,21,Islamic education,year 2,3.00 - 3.49,No,No,Yes,No,No
  4 | 8/7/2020 12:05,Male,19,BIT,Year 1,3.00 - 3.49,No,Yes,Yes,Yes,No
  5 | 8/7/2020 12:06,Female,22,Laws,year 3,3.00 - 3.49,Yes,Yes,No,No,No
  6 | 8/7/2020 12:13,Male,23,Mathemathics,year 4,3.00 - 3.49,No,No,No,No,No
  7 | 8/7/2020 12:31,Male,19,Engineering,Year 2,3.50 - 4.00,No,No,No,Yes,No
  8 | 8/7/2020 12:32,Female,23,Pendidikan islam,year 2,3.50 - 4.00 ,Yes,Yes,No,Yes,No
  9 | 8/7/2020 12:33,Female,18,BCS,year 1,3.50 - 4.00,No,No,Yes,No,No
 10 | 8/7/2020 12:35,Female,19,Human Resources,Year 2,2.50 - 2.99,No,No,No,No,No
 11 | 8/7/2020 12:39,Male,18,Irkhs,year 1,3.50 - 4.00,No,No,Yes,Yes,No
 12 | 8/7/2020 12:39,Female,20,Psychology,year 1,3.50 - 4.00,No,No,No,No,No
 13 | 8/7/2020 12:39,Female,24,Engineering,Year 3,3.50 - 4.00,Yes,Yes,No,No,No
 14 | 8/7/2020 12:40,Female,18,BCS,year 1,3.00 - 3.49,No,Yes,No,No,No
 15 | 8/7/2020 12:41,Male,19,Engineering,year 1,3.00 - 3.49,No,No,No,No,No
 16 | 8/7/2020 12:43,Female,18,KENMS,Year 2,3.50 - 4.00,No,No,Yes,No,No
 17 | 8/7/2020 12:43,Male,24,BCS,Year 3,3.50 - 4.00,No,No,No,No,No
 18 | 8/7/2020 12:46,Female,24,Accounting ,year 3,3.00 - 3.49,No,No,No,No,No
 19 | 8/7/2020 12:52,Female,24,ENM,year 4,3.00 - 3.49,Yes,Yes,Yes,Yes,No
 20 | 8/7/2020 13:05,Female,20,BIT,Year 2,3.50 - 4.00,No,No,Yes,No,No
 21 | 8/7/2020 13:07,Female,18,Marine science,year 2,3.50 - 4.00,Yes,Yes,Yes,Yes,No
 22 | 8/7/2020 13:12,Female,19,Engineering,year 1,3.00 - 3.49,No,No,No,Yes,No
 23 | 8/7/2020 13:13,Female,18,KOE,Year 2,3.00 - 3.49,No,No,No,No,No
 24 | 8/7/2020 13:13,Female,24,BCS,year 1,3.50 - 4.00,No,No,No,No,No
 25 | 8/7/2020 13:15,Female,24,Engineering,year 1,3.00 - 3.49,No,No,No,No,No
 26 | 8/7/2020 13:17,Female,23,BCS,Year 3,3.50 - 4.00,No,Yes,Yes,Yes,No
 27 | 8/7/2020 13:29,Female,18,Banking Studies,year 1,3.50 - 4.00,No,No,No,No,No
 28 | 8/7/2020 13:35,Female,19,Engineering,year 1,3.50 - 4.00,No,No,No,No,No
 29 | 8/7/2020 13:41,Male,18,Engineering,Year 2,3.00 - 3.49,Yes,Yes,Yes,No,No
 30 | 8/7/2020 13:58,Female,24,BIT,Year 3,3.50 - 4.00,Yes,Yes,Yes,Yes,Yes
 31 | 8/7/2020 14:05,Female,24,BCS,year 4,3.50 - 4.00,No,No,No,No,No
 32 | 8/7/2020 14:27,Female,23,Business Administration,Year 2,3.00 - 3.49,No,No,No,No,No
 33 | 8/7/2020 14:29,Male,18,BCS,year 2,3.00 - 3.49,No,No,No,No,No
 34 | 8/7/2020 14:29,Male,19,BCS,year 1,3.50 - 4.00,No,No,No,Yes,No
 35 | 8/7/2020 14:31,Male,18,BCS,Year 2,3.50 - 4.00,Yes,Yes,Yes,No,Yes
 36 | 8/7/2020 14:41,Female,19,BIT,year 1,3.00 - 3.49,No,Yes,Yes,Yes,No
 37 | 8/7/2020 14:43,Female,18,Engineering,year 1,2.00 - 2.49,No,No,No,No,No
 38 | 8/7/2020 14:43,Female,18,Law,Year 3,3.00 - 3.49,No,Yes,Yes,No,No
 39 | 8/7/2020 14:45,Female,19,BIT,year 1,2.50 - 2.99,No,Yes,Yes,Yes,No
 40 | 8/7/2020 14:47,Female,18,KIRKHS,year 1,3.50 - 4.00,No,No,No,No,No
 41 | 8/7/2020 14:56,Female,24,Engineering,Year 2,2.50 - 2.99,Yes,Yes,No,Yes,Yes
 42 | 8/7/2020 14:57,Female,24,BIT,Year 3,3.00 - 3.49,No,No,Yes,No,No
 43 | 8/7/2020 14:57,Female,22,Engineering,year 4,3.50 - 4.00,No,No,No,No,No
 44 | 8/7/2020 14:58,Female,20,Usuluddin ,year 2,3.00 - 3.49,No,Yes,No,No,No
 45 | 8/7/2020 15:07,Male,,BIT,year 1,0 - 1.99,No,No,No,No,No
 46 | 8/7/2020 15:08,Male,23,TAASL,year 2,3.50 - 4.00,No,No,No,Yes,No
 47 | 8/7/2020 15:09,Male,18,BCS,year 1,3.50 - 4.00,No,No,Yes,Yes,No
 48 | 8/7/2020 15:12,Female,19,Engineering,year 1,3.50 - 4.00,No,No,Yes,No,No
 49 | 8/7/2020 15:14,Female,18,Engine,year 4,3.50 - 4.00,No,No,No,No,No
 50 | 8/7/2020 15:14,Male,24,BCS,year 2,3.00 - 3.49,No,Yes,No,No,No
 51 | 8/7/2020 15:18,Female,24,BCS,year 3,3.50 - 4.00,No,No,No,Yes,No
 52 | 8/7/2020 15:27,Female,23,ALA,year 1,2.50 - 2.99,Yes,Yes,No,Yes,Yes
 53 | 8/7/2020 15:37,Female,18,BCS,year 2,3.50 - 4.00,No,No,Yes,No,No
 54 | 8/7/2020 15:47,Female,19,Biomedical science,year 3,3.00 - 3.49,No,No,No,No,No
 55 | 8/7/2020 15:48,Female,20,koe,year 3,3.00 - 3.49,Yes,Yes,Yes,Yes,No
 56 | 8/7/2020 15:57,Female,19,BCS,year 1,3.50 - 4.00,No,Yes,No,Yes,Yes
 57 | 8/7/2020 15:58,Male,21,BCS,year 1,3.00 - 3.49,No,No,No,No,No
 58 | 8/7/2020 16:08,Male,23,Kirkhs,Year 3,3.50 - 4.00,No,No,No,No,No
 59 | 8/7/2020 16:21,Female,20,BENL,Year 3,3.00 - 3.49,No,Yes,Yes,No,No
 60 | 8/7/2020 16:22,Female,18,BCS,year 1,3.50 - 4.00,No,No,No,No,No
 61 | 8/7/2020 16:34,Female,23,Benl,year 1,3.00 - 3.49,No,No,No,No,No
 62 | 8/7/2020 16:34,Female,18,IT,Year 3,3.00 - 3.49,No,No,No,Yes,No
 63 | 8/7/2020 16:53,Female,19,BCS,year 1,3.50 - 4.00,No,No,No,No,No
 64 | 8/7/2020 17:05,Female,18,CTS,Year 1,3.50 - 4.00,No,No,No,Yes,No
 65 | 8/7/2020 17:37,Female,24,engin,year 1,3.50 - 4.00,No,No,No,Yes,No
 66 | 8/7/2020 17:46,Female,24,Engine,year 1,3.50 - 4.00,No,No,No,No,No
 67 | 8/7/2020 17:50,Female,23,Econs,year 1,3.50 - 4.00,No,Yes,Yes,No,No
 68 | 8/7/2020 18:10,Female,18,KOE,Year 3,3.00 - 3.49,No,No,Yes,No,No
 69 | 8/7/2020 18:11,Male,19,MHSC,Year 3,3.00 - 3.49,Yes,Yes,No,Yes,No
 70 | 8/7/2020 19:05,Female,18,Malcom,year 1,3.50 - 4.00,No,Yes,No,No,No
 71 | 8/7/2020 19:32,Female,24,Kop,year 4,3.00 - 3.49,No,No,Yes,No,No
 72 | 8/7/2020 20:36,Female,24,Biomedical science,year 1,3.00 - 3.49,No,No,No,No,No
 73 | 8/7/2020 21:21,Female,18,Laws,Year 3,3.50 - 4.00,No,No,No,Yes,No
 74 | 8/7/2020 22:35,Female,19,BIT,Year 3,3.00 - 3.49,Yes,Yes,No,No,No
 75 | 9/7/2020 6:57,Male,18,Biomedical science,year 1,0 - 1.99,No,No,No,No,No
 76 | 9/7/2020 11:43,Male,24,BIT,Year 3,3.50 - 4.00,No,No,Yes,No,No
 77 | 9/7/2020 11:57,Female,24,KOE,year 1,3.50 - 4.00,No,No,Yes,Yes,No
 78 | 9/7/2020 13:15,Female,23,Engineering,year 1,3.00 - 3.49,No,Yes,No,No,No
 79 | 9/7/2020 18:24,Female,18,Human Sciences ,Year 2,3.00 - 3.49,No,No,No,Yes,No
 80 | 13/07/2020 10:07:32,Female,19,Biotechnology,Year 3,0 - 1.99,No,No,No,No,No
 81 | 13/07/2020 10:10:30,Female,18,Engineering,year 4,3.50 - 4.00,No,No,No,No,No
 82 | 13/07/2020 10:11:26,Female,24,Communication ,Year 2,3.50 - 4.00,Yes,Yes,Yes,Yes,No
 83 | 13/07/2020 10:12:18,Female,24,Diploma Nursing,year 2,3.50 - 4.00,No,No,No,No,No
 84 | 13/07/2020 10:12:26,Female,19,Engineering,year 1,3.00 - 3.49,No,Yes,Yes,No,No
 85 | 13/07/2020 10:12:28,Female,19,Pendidikan Islam ,Year 2,3.00 - 3.49,No,No,No,No,No
 86 | 13/07/2020 10:14:46,Male,23,Radiography,year 1,3.00 - 3.49,No,No,No,No,No
 87 | 13/07/2020 10:33:47,Female,18,psychology,year 1,3.50 - 4.00,No,Yes,Yes,No,Yes
 88 | 13/07/2020 10:34:08,Female,19,Fiqh fatwa ,Year 3,3.00 - 3.49,No,No,No,No,No
 89 | 13/07/2020 11:46:13,Female,18,psychology,year 1,3.50 - 4.00,No,Yes,Yes,Yes,No
 90 | 13/07/2020 11:49:02,Male,24,BIT,year 1,3.00 - 3.49,No,No,Yes,No,No
 91 | 13/07/2020 11:54:58,Male,24,Engineering,Year 2,2.00 - 2.49,No,No,No,Yes,No
 92 | 13/07/2020 13:57:11,Female,23,DIPLOMA TESL,Year 3,3.50 - 4.00,No,No,No,Yes,No
 93 | 13/07/2020 14:38:12,Male,18,Koe,Year 2,3.00 - 3.49,No,No,Yes,No,No
 94 | 13/07/2020 14:48:05,Female,19,KOE,year 2,3.00 - 3.49,Yes,Yes,No,No,No
 95 | 13/07/2020 16:15:13,Female,18,BENL,year 1,3.00 - 3.49,No,Yes,No,No,No
 96 | 13/07/2020 17:30:44,Female,24,Fiqh,Year 3,0 - 1.99,No,No,No,Yes,No
 97 | 13/07/2020 19:08:32,Female,18,Islamic Education,year 1,3.50 - 4.00,No,No,No,No,No
 98 | 13/07/2020 19:56:49,Female,21,BCS,year 1,3.50 - 4.00,No,No,Yes,No,No
 99 | 13/07/2020 21:21:42,Male,18,Engineering,Year 2,3.00 - 3.49,No,Yes,Yes,No,No
100 | 13/07/2020 21:22:56,Female,19,Nursing ,Year 3,3.50 - 4.00,Yes,Yes,No,Yes,No
101 | 13/07/2020 21:23:57,Female,23,Pendidikan Islam,year 4,3.50 - 4.00,No,No,No,No,No
102 | 18/07/2020 20:16:21,Male,20,Biomedical science,Year 2,3.00 - 3.49,No,No,No,No,No
103 | 


--------------------------------------------------------------------------------
/stud-mental-helalth.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "Extract the dataset from kaggle using kaggle APi"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 1,
  13 |    "metadata": {},
  14 |    "outputs": [
  15 |     {
  16 |      "name": "stdout",
  17 |      "output_type": "stream",
  18 |      "text": [
  19 |       "Dataset URL: https://www.kaggle.com/datasets/shariful07/student-mental-health\n",
  20 |       "License(s): CC0-1.0\n",
  21 |       "Downloading student-mental-health.zip to d:\\artificial intelligence\\python EFA project\\student mental health analysis\n",
  22 |       "\n"
  23 |      ]
  24 |     },
  25 |     {
  26 |      "name": "stderr",
  27 |      "output_type": "stream",
  28 |      "text": [
  29 |       "\n",
  30 |       "  0%|          | 0.00/1.62k [00:00<?, ?B/s]\n",
  31 |       "100%|██████████| 1.62k/1.62k [00:00<00:00, 790kB/s]\n"
  32 |      ]
  33 |     }
  34 |    ],
  35 |    "source": [
  36 |     "import kaggle\n",
  37 |     "! kaggle datasets download -d shariful07/student-mental-health"
  38 |    ]
  39 |   },
  40 |   {
  41 |    "cell_type": "code",
  42 |    "execution_count": 2,
  43 |    "metadata": {},
  44 |    "outputs": [],
  45 |    "source": [
  46 |     "import zipfile as zp\n",
  47 |     "ref = zp.ZipFile('student-mental-health.zip')\n",
  48 |     "ref.extractall()\n",
  49 |     "ref.close()"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "markdown",
  54 |    "metadata": {},
  55 |    "source": [
  56 |     "### Import the necessary libraries"
  57 |    ]
  58 |   },
  59 |   {
  60 |    "cell_type": "code",
  61 |    "execution_count": 3,
  62 |    "metadata": {},
  63 |    "outputs": [],
  64 |    "source": [
  65 |     "import pandas as pd\n",
  66 |     "import seaborn as sns\n",
  67 |     "import matplotlib.pyplot as plt\n",
  68 |     "import warnings \n",
  69 |     "warnings.filterwarnings(\"ignore\")"
  70 |    ]
  71 |   },
  72 |   {
  73 |    "cell_type": "markdown",
  74 |    "metadata": {},
  75 |    "source": [
  76 |     "### Explore and understand the  data"
  77 |    ]
  78 |   },
  79 |   {
  80 |    "cell_type": "code",
  81 |    "execution_count": 4,
  82 |    "metadata": {},
  83 |    "outputs": [
  84 |     {
  85 |      "data": {
  86 |       "text/html": [
  87 |        "<div>\n",
  88 |        "<style scoped>\n",
  89 |        "    .dataframe tbody tr th:only-of-type {\n",
  90 |        "        vertical-align: middle;\n",
  91 |        "    }\n",
  92 |        "\n",
  93 |        "    .dataframe tbody tr th {\n",
  94 |        "        vertical-align: top;\n",
  95 |        "    }\n",
  96 |        "\n",
  97 |        "    .dataframe thead th {\n",
  98 |        "        text-align: right;\n",
  99 |        "    }\n",
 100 |        "</style>\n",
 101 |        "<table border=\"1\" class=\"dataframe\">\n",
 102 |        "  <thead>\n",
 103 |        "    <tr style=\"text-align: right;\">\n",
 104 |        "      <th></th>\n",
 105 |        "      <th>Timestamp</th>\n",
 106 |        "      <th>Choose your gender</th>\n",
 107 |        "      <th>Age</th>\n",
 108 |        "      <th>What is your course?</th>\n",
 109 |        "      <th>Your current year of Study</th>\n",
 110 |        "      <th>What is your CGPA?</th>\n",
 111 |        "      <th>Marital status</th>\n",
 112 |        "      <th>Do you have Depression?</th>\n",
 113 |        "      <th>Do you have Anxiety?</th>\n",
 114 |        "      <th>Do you have Panic attack?</th>\n",
 115 |        "      <th>Did you seek any specialist for a treatment?</th>\n",
 116 |        "    </tr>\n",
 117 |        "  </thead>\n",
 118 |        "  <tbody>\n",
 119 |        "    <tr>\n",
 120 |        "      <th>0</th>\n",
 121 |        "      <td>8/7/2020 12:02</td>\n",
 122 |        "      <td>Female</td>\n",
 123 |        "      <td>18.0</td>\n",
 124 |        "      <td>Engineering</td>\n",
 125 |        "      <td>year 1</td>\n",
 126 |        "      <td>3.00 - 3.49</td>\n",
 127 |        "      <td>No</td>\n",
 128 |        "      <td>Yes</td>\n",
 129 |        "      <td>No</td>\n",
 130 |        "      <td>Yes</td>\n",
 131 |        "      <td>No</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>1</th>\n",
 135 |        "      <td>8/7/2020 12:04</td>\n",
 136 |        "      <td>Male</td>\n",
 137 |        "      <td>21.0</td>\n",
 138 |        "      <td>Islamic education</td>\n",
 139 |        "      <td>year 2</td>\n",
 140 |        "      <td>3.00 - 3.49</td>\n",
 141 |        "      <td>No</td>\n",
 142 |        "      <td>No</td>\n",
 143 |        "      <td>Yes</td>\n",
 144 |        "      <td>No</td>\n",
 145 |        "      <td>No</td>\n",
 146 |        "    </tr>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>2</th>\n",
 149 |        "      <td>8/7/2020 12:05</td>\n",
 150 |        "      <td>Male</td>\n",
 151 |        "      <td>19.0</td>\n",
 152 |        "      <td>BIT</td>\n",
 153 |        "      <td>Year 1</td>\n",
 154 |        "      <td>3.00 - 3.49</td>\n",
 155 |        "      <td>No</td>\n",
 156 |        "      <td>Yes</td>\n",
 157 |        "      <td>Yes</td>\n",
 158 |        "      <td>Yes</td>\n",
 159 |        "      <td>No</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>3</th>\n",
 163 |        "      <td>8/7/2020 12:06</td>\n",
 164 |        "      <td>Female</td>\n",
 165 |        "      <td>22.0</td>\n",
 166 |        "      <td>Laws</td>\n",
 167 |        "      <td>year 3</td>\n",
 168 |        "      <td>3.00 - 3.49</td>\n",
 169 |        "      <td>Yes</td>\n",
 170 |        "      <td>Yes</td>\n",
 171 |        "      <td>No</td>\n",
 172 |        "      <td>No</td>\n",
 173 |        "      <td>No</td>\n",
 174 |        "    </tr>\n",
 175 |        "    <tr>\n",
 176 |        "      <th>4</th>\n",
 177 |        "      <td>8/7/2020 12:13</td>\n",
 178 |        "      <td>Male</td>\n",
 179 |        "      <td>23.0</td>\n",
 180 |        "      <td>Mathemathics</td>\n",
 181 |        "      <td>year 4</td>\n",
 182 |        "      <td>3.00 - 3.49</td>\n",
 183 |        "      <td>No</td>\n",
 184 |        "      <td>No</td>\n",
 185 |        "      <td>No</td>\n",
 186 |        "      <td>No</td>\n",
 187 |        "      <td>No</td>\n",
 188 |        "    </tr>\n",
 189 |        "    <tr>\n",
 190 |        "      <th>...</th>\n",
 191 |        "      <td>...</td>\n",
 192 |        "      <td>...</td>\n",
 193 |        "      <td>...</td>\n",
 194 |        "      <td>...</td>\n",
 195 |        "      <td>...</td>\n",
 196 |        "      <td>...</td>\n",
 197 |        "      <td>...</td>\n",
 198 |        "      <td>...</td>\n",
 199 |        "      <td>...</td>\n",
 200 |        "      <td>...</td>\n",
 201 |        "      <td>...</td>\n",
 202 |        "    </tr>\n",
 203 |        "    <tr>\n",
 204 |        "      <th>96</th>\n",
 205 |        "      <td>13/07/2020 19:56:49</td>\n",
 206 |        "      <td>Female</td>\n",
 207 |        "      <td>21.0</td>\n",
 208 |        "      <td>BCS</td>\n",
 209 |        "      <td>year 1</td>\n",
 210 |        "      <td>3.50 - 4.00</td>\n",
 211 |        "      <td>No</td>\n",
 212 |        "      <td>No</td>\n",
 213 |        "      <td>Yes</td>\n",
 214 |        "      <td>No</td>\n",
 215 |        "      <td>No</td>\n",
 216 |        "    </tr>\n",
 217 |        "    <tr>\n",
 218 |        "      <th>97</th>\n",
 219 |        "      <td>13/07/2020 21:21:42</td>\n",
 220 |        "      <td>Male</td>\n",
 221 |        "      <td>18.0</td>\n",
 222 |        "      <td>Engineering</td>\n",
 223 |        "      <td>Year 2</td>\n",
 224 |        "      <td>3.00 - 3.49</td>\n",
 225 |        "      <td>No</td>\n",
 226 |        "      <td>Yes</td>\n",
 227 |        "      <td>Yes</td>\n",
 228 |        "      <td>No</td>\n",
 229 |        "      <td>No</td>\n",
 230 |        "    </tr>\n",
 231 |        "    <tr>\n",
 232 |        "      <th>98</th>\n",
 233 |        "      <td>13/07/2020 21:22:56</td>\n",
 234 |        "      <td>Female</td>\n",
 235 |        "      <td>19.0</td>\n",
 236 |        "      <td>Nursing</td>\n",
 237 |        "      <td>Year 3</td>\n",
 238 |        "      <td>3.50 - 4.00</td>\n",
 239 |        "      <td>Yes</td>\n",
 240 |        "      <td>Yes</td>\n",
 241 |        "      <td>No</td>\n",
 242 |        "      <td>Yes</td>\n",
 243 |        "      <td>No</td>\n",
 244 |        "    </tr>\n",
 245 |        "    <tr>\n",
 246 |        "      <th>99</th>\n",
 247 |        "      <td>13/07/2020 21:23:57</td>\n",
 248 |        "      <td>Female</td>\n",
 249 |        "      <td>23.0</td>\n",
 250 |        "      <td>Pendidikan Islam</td>\n",
 251 |        "      <td>year 4</td>\n",
 252 |        "      <td>3.50 - 4.00</td>\n",
 253 |        "      <td>No</td>\n",
 254 |        "      <td>No</td>\n",
 255 |        "      <td>No</td>\n",
 256 |        "      <td>No</td>\n",
 257 |        "      <td>No</td>\n",
 258 |        "    </tr>\n",
 259 |        "    <tr>\n",
 260 |        "      <th>100</th>\n",
 261 |        "      <td>18/07/2020 20:16:21</td>\n",
 262 |        "      <td>Male</td>\n",
 263 |        "      <td>20.0</td>\n",
 264 |        "      <td>Biomedical science</td>\n",
 265 |        "      <td>Year 2</td>\n",
 266 |        "      <td>3.00 - 3.49</td>\n",
 267 |        "      <td>No</td>\n",
 268 |        "      <td>No</td>\n",
 269 |        "      <td>No</td>\n",
 270 |        "      <td>No</td>\n",
 271 |        "      <td>No</td>\n",
 272 |        "    </tr>\n",
 273 |        "  </tbody>\n",
 274 |        "</table>\n",
 275 |        "<p>101 rows × 11 columns</p>\n",
 276 |        "</div>"
 277 |       ],
 278 |       "text/plain": [
 279 |        "               Timestamp Choose your gender   Age What is your course?  \\\n",
 280 |        "0         8/7/2020 12:02             Female  18.0          Engineering   \n",
 281 |        "1         8/7/2020 12:04               Male  21.0    Islamic education   \n",
 282 |        "2         8/7/2020 12:05               Male  19.0                  BIT   \n",
 283 |        "3         8/7/2020 12:06             Female  22.0                 Laws   \n",
 284 |        "4         8/7/2020 12:13               Male  23.0         Mathemathics   \n",
 285 |        "..                   ...                ...   ...                  ...   \n",
 286 |        "96   13/07/2020 19:56:49             Female  21.0                  BCS   \n",
 287 |        "97   13/07/2020 21:21:42               Male  18.0          Engineering   \n",
 288 |        "98   13/07/2020 21:22:56             Female  19.0             Nursing    \n",
 289 |        "99   13/07/2020 21:23:57             Female  23.0     Pendidikan Islam   \n",
 290 |        "100  18/07/2020 20:16:21               Male  20.0   Biomedical science   \n",
 291 |        "\n",
 292 |        "    Your current year of Study What is your CGPA? Marital status  \\\n",
 293 |        "0                       year 1        3.00 - 3.49             No   \n",
 294 |        "1                       year 2        3.00 - 3.49             No   \n",
 295 |        "2                       Year 1        3.00 - 3.49             No   \n",
 296 |        "3                       year 3        3.00 - 3.49            Yes   \n",
 297 |        "4                       year 4        3.00 - 3.49             No   \n",
 298 |        "..                         ...                ...            ...   \n",
 299 |        "96                      year 1        3.50 - 4.00             No   \n",
 300 |        "97                      Year 2        3.00 - 3.49             No   \n",
 301 |        "98                      Year 3        3.50 - 4.00            Yes   \n",
 302 |        "99                      year 4        3.50 - 4.00             No   \n",
 303 |        "100                     Year 2        3.00 - 3.49             No   \n",
 304 |        "\n",
 305 |        "    Do you have Depression? Do you have Anxiety? Do you have Panic attack?  \\\n",
 306 |        "0                       Yes                   No                       Yes   \n",
 307 |        "1                        No                  Yes                        No   \n",
 308 |        "2                       Yes                  Yes                       Yes   \n",
 309 |        "3                       Yes                   No                        No   \n",
 310 |        "4                        No                   No                        No   \n",
 311 |        "..                      ...                  ...                       ...   \n",
 312 |        "96                       No                  Yes                        No   \n",
 313 |        "97                      Yes                  Yes                        No   \n",
 314 |        "98                      Yes                   No                       Yes   \n",
 315 |        "99                       No                   No                        No   \n",
 316 |        "100                      No                   No                        No   \n",
 317 |        "\n",
 318 |        "    Did you seek any specialist for a treatment?  \n",
 319 |        "0                                             No  \n",
 320 |        "1                                             No  \n",
 321 |        "2                                             No  \n",
 322 |        "3                                             No  \n",
 323 |        "4                                             No  \n",
 324 |        "..                                           ...  \n",
 325 |        "96                                            No  \n",
 326 |        "97                                            No  \n",
 327 |        "98                                            No  \n",
 328 |        "99                                            No  \n",
 329 |        "100                                           No  \n",
 330 |        "\n",
 331 |        "[101 rows x 11 columns]"
 332 |       ]
 333 |      },
 334 |      "execution_count": 4,
 335 |      "metadata": {},
 336 |      "output_type": "execute_result"
 337 |     }
 338 |    ],
 339 |    "source": [
 340 |     "df = pd.read_csv(\"Student Mental health.csv\", index_col= None)\n",
 341 |     "df"
 342 |    ]
 343 |   },
 344 |   {
 345 |    "cell_type": "code",
 346 |    "execution_count": 9,
 347 |    "metadata": {},
 348 |    "outputs": [
 349 |     {
 350 |      "data": {
 351 |       "text/plain": [
 352 |        "Timestamp          object\n",
 353 |        "Gender             object\n",
 354 |        "Age               float64\n",
 355 |        "Course name        object\n",
 356 |        "Current Year       object\n",
 357 |        "CGPA               object\n",
 358 |        "Marital status     object\n",
 359 |        "Depression         object\n",
 360 |        "Anxiety            object\n",
 361 |        "attack             object\n",
 362 |        "treatment          object\n",
 363 |        "dtype: object"
 364 |       ]
 365 |      },
 366 |      "execution_count": 9,
 367 |      "metadata": {},
 368 |      "output_type": "execute_result"
 369 |     }
 370 |    ],
 371 |    "source": [
 372 |     "df.dtypes"
 373 |    ]
 374 |   },
 375 |   {
 376 |    "cell_type": "markdown",
 377 |    "metadata": {},
 378 |    "source": [
 379 |     "### Rename the columns"
 380 |    ]
 381 |   },
 382 |   {
 383 |    "cell_type": "code",
 384 |    "execution_count": 8,
 385 |    "metadata": {},
 386 |    "outputs": [],
 387 |    "source": [
 388 |     "df.rename(columns={'Choose your gender' : 'Gender',\n",
 389 |     "                   'What is your course?' : 'Course name',\n",
 390 |     "                   'Your current year of Study' : 'Current Year',\n",
 391 |     "                   'What is your CGPA?' : 'CGPA',\n",
 392 |     "                   'Do you have Depression?' : 'Depression',\n",
 393 |     "                   'Do you have Anxiety?' : 'Anxiety',\n",
 394 |     "                   'Do you have Panic attack?' : 'attack',\n",
 395 |     "                   'Did you seek any specialist for a treatment?' : 'treatment'\n",
 396 |     "}, inplace= True)"
 397 |    ]
 398 |   },
 399 |   {
 400 |    "cell_type": "markdown",
 401 |    "metadata": {},
 402 |    "source": [
 403 |     "### data cleaning\n",
 404 |     "1. handling null values\n",
 405 |     "2. delete unwamted columns\n",
 406 |     "3. modifing the columns\n"
 407 |    ]
 408 |   },
 409 |   {
 410 |    "cell_type": "code",
 411 |    "execution_count": 11,
 412 |    "metadata": {},
 413 |    "outputs": [
 414 |     {
 415 |      "data": {
 416 |       "text/plain": [
 417 |        "Timestamp         0\n",
 418 |        "Gender            0\n",
 419 |        "Age               1\n",
 420 |        "Course name       0\n",
 421 |        "Current Year      0\n",
 422 |        "CGPA              0\n",
 423 |        "Marital status    0\n",
 424 |        "Depression        0\n",
 425 |        "Anxiety           0\n",
 426 |        "attack            0\n",
 427 |        "treatment         0\n",
 428 |        "dtype: int64"
 429 |       ]
 430 |      },
 431 |      "execution_count": 11,
 432 |      "metadata": {},
 433 |      "output_type": "execute_result"
 434 |     }
 435 |    ],
 436 |    "source": [
 437 |     "df.isnull().sum()"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 12,
 443 |    "metadata": {},
 444 |    "outputs": [
 445 |     {
 446 |      "data": {
 447 |       "text/html": [
 448 |        "<div>\n",
 449 |        "<style scoped>\n",
 450 |        "    .dataframe tbody tr th:only-of-type {\n",
 451 |        "        vertical-align: middle;\n",
 452 |        "    }\n",
 453 |        "\n",
 454 |        "    .dataframe tbody tr th {\n",
 455 |        "        vertical-align: top;\n",
 456 |        "    }\n",
 457 |        "\n",
 458 |        "    .dataframe thead th {\n",
 459 |        "        text-align: right;\n",
 460 |        "    }\n",
 461 |        "</style>\n",
 462 |        "<table border=\"1\" class=\"dataframe\">\n",
 463 |        "  <thead>\n",
 464 |        "    <tr style=\"text-align: right;\">\n",
 465 |        "      <th></th>\n",
 466 |        "      <th>Timestamp</th>\n",
 467 |        "      <th>Gender</th>\n",
 468 |        "      <th>Age</th>\n",
 469 |        "      <th>Course name</th>\n",
 470 |        "      <th>Current Year</th>\n",
 471 |        "      <th>CGPA</th>\n",
 472 |        "      <th>Marital status</th>\n",
 473 |        "      <th>Depression</th>\n",
 474 |        "      <th>Anxiety</th>\n",
 475 |        "      <th>attack</th>\n",
 476 |        "      <th>treatment</th>\n",
 477 |        "    </tr>\n",
 478 |        "  </thead>\n",
 479 |        "  <tbody>\n",
 480 |        "    <tr>\n",
 481 |        "      <th>0</th>\n",
 482 |        "      <td>8/7/2020 12:02</td>\n",
 483 |        "      <td>Female</td>\n",
 484 |        "      <td>18.0</td>\n",
 485 |        "      <td>Engineering</td>\n",
 486 |        "      <td>year 1</td>\n",
 487 |        "      <td>3.00 - 3.49</td>\n",
 488 |        "      <td>No</td>\n",
 489 |        "      <td>Yes</td>\n",
 490 |        "      <td>No</td>\n",
 491 |        "      <td>Yes</td>\n",
 492 |        "      <td>No</td>\n",
 493 |        "    </tr>\n",
 494 |        "    <tr>\n",
 495 |        "      <th>1</th>\n",
 496 |        "      <td>8/7/2020 12:04</td>\n",
 497 |        "      <td>Male</td>\n",
 498 |        "      <td>21.0</td>\n",
 499 |        "      <td>Islamic education</td>\n",
 500 |        "      <td>year 2</td>\n",
 501 |        "      <td>3.00 - 3.49</td>\n",
 502 |        "      <td>No</td>\n",
 503 |        "      <td>No</td>\n",
 504 |        "      <td>Yes</td>\n",
 505 |        "      <td>No</td>\n",
 506 |        "      <td>No</td>\n",
 507 |        "    </tr>\n",
 508 |        "    <tr>\n",
 509 |        "      <th>2</th>\n",
 510 |        "      <td>8/7/2020 12:05</td>\n",
 511 |        "      <td>Male</td>\n",
 512 |        "      <td>19.0</td>\n",
 513 |        "      <td>BIT</td>\n",
 514 |        "      <td>Year 1</td>\n",
 515 |        "      <td>3.00 - 3.49</td>\n",
 516 |        "      <td>No</td>\n",
 517 |        "      <td>Yes</td>\n",
 518 |        "      <td>Yes</td>\n",
 519 |        "      <td>Yes</td>\n",
 520 |        "      <td>No</td>\n",
 521 |        "    </tr>\n",
 522 |        "    <tr>\n",
 523 |        "      <th>3</th>\n",
 524 |        "      <td>8/7/2020 12:06</td>\n",
 525 |        "      <td>Female</td>\n",
 526 |        "      <td>22.0</td>\n",
 527 |        "      <td>Laws</td>\n",
 528 |        "      <td>year 3</td>\n",
 529 |        "      <td>3.00 - 3.49</td>\n",
 530 |        "      <td>Yes</td>\n",
 531 |        "      <td>Yes</td>\n",
 532 |        "      <td>No</td>\n",
 533 |        "      <td>No</td>\n",
 534 |        "      <td>No</td>\n",
 535 |        "    </tr>\n",
 536 |        "    <tr>\n",
 537 |        "      <th>4</th>\n",
 538 |        "      <td>8/7/2020 12:13</td>\n",
 539 |        "      <td>Male</td>\n",
 540 |        "      <td>23.0</td>\n",
 541 |        "      <td>Mathemathics</td>\n",
 542 |        "      <td>year 4</td>\n",
 543 |        "      <td>3.00 - 3.49</td>\n",
 544 |        "      <td>No</td>\n",
 545 |        "      <td>No</td>\n",
 546 |        "      <td>No</td>\n",
 547 |        "      <td>No</td>\n",
 548 |        "      <td>No</td>\n",
 549 |        "    </tr>\n",
 550 |        "    <tr>\n",
 551 |        "      <th>...</th>\n",
 552 |        "      <td>...</td>\n",
 553 |        "      <td>...</td>\n",
 554 |        "      <td>...</td>\n",
 555 |        "      <td>...</td>\n",
 556 |        "      <td>...</td>\n",
 557 |        "      <td>...</td>\n",
 558 |        "      <td>...</td>\n",
 559 |        "      <td>...</td>\n",
 560 |        "      <td>...</td>\n",
 561 |        "      <td>...</td>\n",
 562 |        "      <td>...</td>\n",
 563 |        "    </tr>\n",
 564 |        "    <tr>\n",
 565 |        "      <th>96</th>\n",
 566 |        "      <td>13/07/2020 19:56:49</td>\n",
 567 |        "      <td>Female</td>\n",
 568 |        "      <td>21.0</td>\n",
 569 |        "      <td>BCS</td>\n",
 570 |        "      <td>year 1</td>\n",
 571 |        "      <td>3.50 - 4.00</td>\n",
 572 |        "      <td>No</td>\n",
 573 |        "      <td>No</td>\n",
 574 |        "      <td>Yes</td>\n",
 575 |        "      <td>No</td>\n",
 576 |        "      <td>No</td>\n",
 577 |        "    </tr>\n",
 578 |        "    <tr>\n",
 579 |        "      <th>97</th>\n",
 580 |        "      <td>13/07/2020 21:21:42</td>\n",
 581 |        "      <td>Male</td>\n",
 582 |        "      <td>18.0</td>\n",
 583 |        "      <td>Engineering</td>\n",
 584 |        "      <td>Year 2</td>\n",
 585 |        "      <td>3.00 - 3.49</td>\n",
 586 |        "      <td>No</td>\n",
 587 |        "      <td>Yes</td>\n",
 588 |        "      <td>Yes</td>\n",
 589 |        "      <td>No</td>\n",
 590 |        "      <td>No</td>\n",
 591 |        "    </tr>\n",
 592 |        "    <tr>\n",
 593 |        "      <th>98</th>\n",
 594 |        "      <td>13/07/2020 21:22:56</td>\n",
 595 |        "      <td>Female</td>\n",
 596 |        "      <td>19.0</td>\n",
 597 |        "      <td>Nursing</td>\n",
 598 |        "      <td>Year 3</td>\n",
 599 |        "      <td>3.50 - 4.00</td>\n",
 600 |        "      <td>Yes</td>\n",
 601 |        "      <td>Yes</td>\n",
 602 |        "      <td>No</td>\n",
 603 |        "      <td>Yes</td>\n",
 604 |        "      <td>No</td>\n",
 605 |        "    </tr>\n",
 606 |        "    <tr>\n",
 607 |        "      <th>99</th>\n",
 608 |        "      <td>13/07/2020 21:23:57</td>\n",
 609 |        "      <td>Female</td>\n",
 610 |        "      <td>23.0</td>\n",
 611 |        "      <td>Pendidikan Islam</td>\n",
 612 |        "      <td>year 4</td>\n",
 613 |        "      <td>3.50 - 4.00</td>\n",
 614 |        "      <td>No</td>\n",
 615 |        "      <td>No</td>\n",
 616 |        "      <td>No</td>\n",
 617 |        "      <td>No</td>\n",
 618 |        "      <td>No</td>\n",
 619 |        "    </tr>\n",
 620 |        "    <tr>\n",
 621 |        "      <th>100</th>\n",
 622 |        "      <td>18/07/2020 20:16:21</td>\n",
 623 |        "      <td>Male</td>\n",
 624 |        "      <td>20.0</td>\n",
 625 |        "      <td>Biomedical science</td>\n",
 626 |        "      <td>Year 2</td>\n",
 627 |        "      <td>3.00 - 3.49</td>\n",
 628 |        "      <td>No</td>\n",
 629 |        "      <td>No</td>\n",
 630 |        "      <td>No</td>\n",
 631 |        "      <td>No</td>\n",
 632 |        "      <td>No</td>\n",
 633 |        "    </tr>\n",
 634 |        "  </tbody>\n",
 635 |        "</table>\n",
 636 |        "<p>101 rows × 11 columns</p>\n",
 637 |        "</div>"
 638 |       ],
 639 |       "text/plain": [
 640 |        "               Timestamp  Gender   Age         Course name Current Year  \\\n",
 641 |        "0         8/7/2020 12:02  Female  18.0         Engineering       year 1   \n",
 642 |        "1         8/7/2020 12:04    Male  21.0   Islamic education       year 2   \n",
 643 |        "2         8/7/2020 12:05    Male  19.0                 BIT       Year 1   \n",
 644 |        "3         8/7/2020 12:06  Female  22.0                Laws       year 3   \n",
 645 |        "4         8/7/2020 12:13    Male  23.0        Mathemathics       year 4   \n",
 646 |        "..                   ...     ...   ...                 ...          ...   \n",
 647 |        "96   13/07/2020 19:56:49  Female  21.0                 BCS       year 1   \n",
 648 |        "97   13/07/2020 21:21:42    Male  18.0         Engineering       Year 2   \n",
 649 |        "98   13/07/2020 21:22:56  Female  19.0            Nursing        Year 3   \n",
 650 |        "99   13/07/2020 21:23:57  Female  23.0    Pendidikan Islam       year 4   \n",
 651 |        "100  18/07/2020 20:16:21    Male  20.0  Biomedical science       Year 2   \n",
 652 |        "\n",
 653 |        "            CGPA Marital status Depression Anxiety attack treatment  \n",
 654 |        "0    3.00 - 3.49             No        Yes      No    Yes        No  \n",
 655 |        "1    3.00 - 3.49             No         No     Yes     No        No  \n",
 656 |        "2    3.00 - 3.49             No        Yes     Yes    Yes        No  \n",
 657 |        "3    3.00 - 3.49            Yes        Yes      No     No        No  \n",
 658 |        "4    3.00 - 3.49             No         No      No     No        No  \n",
 659 |        "..           ...            ...        ...     ...    ...       ...  \n",
 660 |        "96   3.50 - 4.00             No         No     Yes     No        No  \n",
 661 |        "97   3.00 - 3.49             No        Yes     Yes     No        No  \n",
 662 |        "98   3.50 - 4.00            Yes        Yes      No    Yes        No  \n",
 663 |        "99   3.50 - 4.00             No         No      No     No        No  \n",
 664 |        "100  3.00 - 3.49             No         No      No     No        No  \n",
 665 |        "\n",
 666 |        "[101 rows x 11 columns]"
 667 |       ]
 668 |      },
 669 |      "execution_count": 12,
 670 |      "metadata": {},
 671 |      "output_type": "execute_result"
 672 |     }
 673 |    ],
 674 |    "source": [
 675 |     "df"
 676 |    ]
 677 |   },
 678 |   {
 679 |    "cell_type": "code",
 680 |    "execution_count": 18,
 681 |    "metadata": {},
 682 |    "outputs": [],
 683 |    "source": [
 684 |     "df['Age'] = df['Age'].fillna(0).astype(int)\n"
 685 |    ]
 686 |   },
 687 |   {
 688 |    "cell_type": "code",
 689 |    "execution_count": 29,
 690 |    "metadata": {},
 691 |    "outputs": [],
 692 |    "source": [
 693 |     "df.drop(columns='Timestamp', inplace=True)"
 694 |    ]
 695 |   },
 696 |   {
 697 |    "cell_type": "code",
 698 |    "execution_count": 41,
 699 |    "metadata": {},
 700 |    "outputs": [
 701 |     {
 702 |      "name": "stdout",
 703 |      "output_type": "stream",
 704 |      "text": [
 705 |       "['Female' 'Male']\n",
 706 |       "[18 21 19 22 23 20 24]\n",
 707 |       "['Engineering' 'Islamic education' 'BIT' 'Laws' 'Mathemathics'\n",
 708 |       " 'Pendidikan islam' 'BCS' 'Human Resources' 'Irkhs' 'Psychology' 'KENMS'\n",
 709 |       " 'Accounting ' 'ENM' 'Marine science' 'KOE' 'Banking Studies'\n",
 710 |       " 'Business Administration' 'Law' 'KIRKHS' 'Usuluddin ' 'TAASL' 'Engine'\n",
 711 |       " 'ALA' 'Biomedical science' 'koe' 'Kirkhs' 'BENL' 'Benl' 'IT' 'CTS'\n",
 712 |       " 'engin' 'Econs' 'MHSC' 'Malcom' 'Kop' 'Human Sciences ' 'Biotechnology'\n",
 713 |       " 'Communication ' 'Diploma Nursing' 'Pendidikan Islam ' 'Radiography'\n",
 714 |       " 'psychology' 'Fiqh fatwa ' 'DIPLOMA TESL' 'Koe' 'Fiqh'\n",
 715 |       " 'Islamic Education' 'Nursing ' 'Pendidikan Islam']\n",
 716 |       "['year 1' 'year 2' 'year 3' 'year 4']\n",
 717 |       "['Grade B' 'Grade A' 'Grade C' 'Grade D' 'Fail']\n",
 718 |       "['No' 'Yes']\n",
 719 |       "['Yes' 'No']\n",
 720 |       "['No' 'Yes']\n",
 721 |       "['Yes' 'No']\n",
 722 |       "['No' 'Yes']\n"
 723 |      ]
 724 |     }
 725 |    ],
 726 |    "source": [
 727 |     "for col in df:\n",
 728 |     "    uniq = df[col].unique()\n",
 729 |     "    print(uniq)"
 730 |    ]
 731 |   },
 732 |   {
 733 |    "cell_type": "code",
 734 |    "execution_count": 32,
 735 |    "metadata": {},
 736 |    "outputs": [],
 737 |    "source": [
 738 |     " df = df[df['Age'] != 0]"
 739 |    ]
 740 |   },
 741 |   {
 742 |    "cell_type": "code",
 743 |    "execution_count": 35,
 744 |    "metadata": {},
 745 |    "outputs": [],
 746 |    "source": [
 747 |     "df['CGPA'] = df['CGPA'].str.strip()"
 748 |    ]
 749 |   },
 750 |   {
 751 |    "cell_type": "code",
 752 |    "execution_count": 38,
 753 |    "metadata": {},
 754 |    "outputs": [],
 755 |    "source": [
 756 |     "df['CGPA'].replace(\n",
 757 |     "    {\n",
 758 |     "        '3.50 - 4.00' : 'Grade A',\n",
 759 |     "        '3.00 - 3.49' : 'Grade B',\n",
 760 |     "        '2.50 - 2.99' : 'Grade C',\n",
 761 |     "        '2.00 - 2.49' : 'Grade D',\n",
 762 |     "        '0 - 1.99' : 'Fail'\n",
 763 |     "    },\n",
 764 |     "inplace= True\n",
 765 |     ")"
 766 |    ]
 767 |   },
 768 |   {
 769 |    "cell_type": "code",
 770 |    "execution_count": 40,
 771 |    "metadata": {},
 772 |    "outputs": [],
 773 |    "source": [
 774 |     "df['Current Year'] = df['Current Year'].str.lower()"
 775 |    ]
 776 |   },
 777 |   {
 778 |    "cell_type": "markdown",
 779 |    "metadata": {},
 780 |    "source": [
 781 |     "## Data Visualization\n"
 782 |    ]
 783 |   },
 784 |   {
 785 |    "cell_type": "markdown",
 786 |    "metadata": {},
 787 |    "source": [
 788 |     "### Distribution of gender"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 55,
 794 |    "metadata": {},
 795 |    "outputs": [
 796 |     {
 797 |      "data": {
 798 |       "image/png": "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",
 799 |       "text/plain": [
 800 |        "<Figure size 640x480 with 1 Axes>"
 801 |       ]
 802 |      },
 803 |      "metadata": {},
 804 |      "output_type": "display_data"
 805 |     }
 806 |    ],
 807 |    "source": [
 808 |     "male = df.loc[df['Gender'] == 'Male'].count()[0]\n",
 809 |     "female = df.loc[df['Gender'] == 'Female'].count()[0]\n",
 810 |     "\n",
 811 |     "plt.pie([male, female], labels= ['Male', 'Female'], autopct= '%.f%%')\n",
 812 |     "plt.show()"
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "code",
 817 |    "execution_count": 51,
 818 |    "metadata": {},
 819 |    "outputs": [
 820 |     {
 821 |      "data": {
 822 |       "text/plain": [
 823 |        "25"
 824 |       ]
 825 |      },
 826 |      "execution_count": 51,
 827 |      "metadata": {},
 828 |      "output_type": "execute_result"
 829 |     }
 830 |    ],
 831 |    "source": [
 832 |     "df.loc[df['Gender'] == 'Male'].count()[0]"
 833 |    ]
 834 |   },
 835 |   {
 836 |    "cell_type": "markdown",
 837 |    "metadata": {},
 838 |    "source": [
 839 |     "### Distribution of depression by Age"
 840 |    ]
 841 |   },
 842 |   {
 843 |    "cell_type": "code",
 844 |    "execution_count": 60,
 845 |    "metadata": {},
 846 |    "outputs": [
 847 |     {
 848 |      "data": {
 849 |       "text/plain": [
 850 |        "<Axes: xlabel='Age', ylabel='count'>"
 851 |       ]
 852 |      },
 853 |      "execution_count": 60,
 854 |      "metadata": {},
 855 |      "output_type": "execute_result"
 856 |     },
 857 |     {
 858 |      "data": {
 859 |       "image/png": "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",
 860 |       "text/plain": [
 861 |        "<Figure size 640x480 with 1 Axes>"
 862 |       ]
 863 |      },
 864 |      "metadata": {},
 865 |      "output_type": "display_data"
 866 |     }
 867 |    ],
 868 |    "source": [
 869 |     "sns.countplot(data = df, x = 'Age' , hue = 'Depression')"
 870 |    ]
 871 |   },
 872 |   {
 873 |    "cell_type": "markdown",
 874 |    "metadata": {},
 875 |    "source": [
 876 |     "### Distribution of depression by gender"
 877 |    ]
 878 |   },
 879 |   {
 880 |    "cell_type": "code",
 881 |    "execution_count": 61,
 882 |    "metadata": {},
 883 |    "outputs": [
 884 |     {
 885 |      "data": {
 886 |       "text/plain": [
 887 |        "<Axes: xlabel='Gender', ylabel='count'>"
 888 |       ]
 889 |      },
 890 |      "execution_count": 61,
 891 |      "metadata": {},
 892 |      "output_type": "execute_result"
 893 |     },
 894 |     {
 895 |      "data": {
 896 |       "image/png": "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",
 897 |       "text/plain": [
 898 |        "<Figure size 640x480 with 1 Axes>"
 899 |       ]
 900 |      },
 901 |      "metadata": {},
 902 |      "output_type": "display_data"
 903 |     }
 904 |    ],
 905 |    "source": [
 906 |     "sns.countplot(data = df, x = 'Gender' , hue = 'Depression')"
 907 |    ]
 908 |   },
 909 |   {
 910 |    "cell_type": "markdown",
 911 |    "metadata": {},
 912 |    "source": [
 913 |     "### Distribution of anziety by Gender"
 914 |    ]
 915 |   },
 916 |   {
 917 |    "cell_type": "code",
 918 |    "execution_count": 63,
 919 |    "metadata": {},
 920 |    "outputs": [
 921 |     {
 922 |      "data": {
 923 |       "text/plain": [
 924 |        "<Axes: xlabel='Gender', ylabel='count'>"
 925 |       ]
 926 |      },
 927 |      "execution_count": 63,
 928 |      "metadata": {},
 929 |      "output_type": "execute_result"
 930 |     },
 931 |     {
 932 |      "data": {
 933 |       "image/png": "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",
 934 |       "text/plain": [
 935 |        "<Figure size 640x480 with 1 Axes>"
 936 |       ]
 937 |      },
 938 |      "metadata": {},
 939 |      "output_type": "display_data"
 940 |     }
 941 |    ],
 942 |    "source": [
 943 |     "sns.countplot(data = df, x = 'Gender' , hue = 'Anxiety')"
 944 |    ]
 945 |   },
 946 |   {
 947 |    "cell_type": "code",
 948 |    "execution_count": 64,
 949 |    "metadata": {},
 950 |    "outputs": [],
 951 |    "source": [
 952 |     "df.rename(columns={'attack' : 'Panic attack'}, inplace= True)"
 953 |    ]
 954 |   },
 955 |   {
 956 |    "cell_type": "markdown",
 957 |    "metadata": {},
 958 |    "source": [
 959 |     "### Distribution of panic attack by gender"
 960 |    ]
 961 |   },
 962 |   {
 963 |    "cell_type": "code",
 964 |    "execution_count": 65,
 965 |    "metadata": {},
 966 |    "outputs": [
 967 |     {
 968 |      "data": {
 969 |       "text/plain": [
 970 |        "<Axes: xlabel='Gender', ylabel='count'>"
 971 |       ]
 972 |      },
 973 |      "execution_count": 65,
 974 |      "metadata": {},
 975 |      "output_type": "execute_result"
 976 |     },
 977 |     {
 978 |      "data": {
 979 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAunElEQVR4nO3de1hU9aLG8Xe4DQgCScglQbzkJcVrpqR5DCm1cnshS/PsvOXZFXpSMt0c81pJ1vaSpmXu1OrkodrZxUtqUVrbUJPUraVsIw3bCpgGCMpFWOePdnPObMUQgTVLvp/nWc/D/NaaNe9Mzzhva/1mls0wDEMAAAAW5GZ2AAAAgOqiyAAAAMuiyAAAAMuiyAAAAMuiyAAAAMuiyAAAAMuiyAAAAMvyMDtAbauoqNCJEyfUsGFD2Ww2s+MAAIAqMAxDZ8+eVXh4uNzcKj/ucs0XmRMnTigiIsLsGAAAoBqOHz+uJk2aVLr+mi8yDRs2lPTLC+Hv729yGgAAUBUFBQWKiIhwfI5X5povMr+eTvL396fIAABgMb81LYTJvgAAwLIoMgAAwLIoMgAAwLKu+TkyAABURXl5ucrKysyOUW94enrK3d39qvdDkQEA1GuGYSg7O1t5eXlmR6l3AgMDFRoaelW/80aRAQDUa7+WmMaNG6tBgwb8eGodMAxD586dU25uriQpLCys2vuiyAAA6q3y8nJHiQkKCjI7Tr3i4+MjScrNzVXjxo2rfZqJyb4AgHrr1zkxDRo0MDlJ/fTr6341c5MoMgCAeo/TSeaoidedIgMAACyLIgMAACzL1CIze/Zs2Ww2p6VNmzaO9cXFxUpISFBQUJD8/PwUHx+vnJwcExMDAFBzRo8ercGDB5sdo8ZERUVp8eLFdfqYph+RadeunU6ePOlY/vrXvzrWTZ48WevXr9c777yj7du368SJExo6dKiJaQEA9cXo0aMd/5Pt5eWlli1bau7cubpw4UKNPcYLL7ygNWvW1Nj+/tWxY8dks9m0b98+p/FrqUCZ/vVrDw8PhYaGXjSen5+vV199VWvXrlVsbKwkafXq1Wrbtq127typHj161HVUAEA9079/f61evVolJSXatGmTEhIS5OnpqaSkpBrZf0BAQI3spz4z/YjMkSNHFB4erubNm2vkyJHKysqSJKWnp6usrExxcXGObdu0aaPIyEilpaVVur+SkhIVFBQ4LQAAVIfdbldoaKiaNm2qRx55RHFxcfrwww8lSQsXLlR0dLR8fX0VERGhRx99VIWFhY77rlmzRoGBgdqyZYvatm0rPz8/9e/fXydPnnRs869HRioqKvTcc8+pZcuWstvtioyM1DPPPFNpvs2bN6tXr14KDAxUUFCQ7rnnHmVmZjrWN2vWTJLUuXNn2Ww29enTR7Nnz9Zrr72mDz74wHHEadu2bZKkadOmqVWrVmrQoIGaN2+uGTNmXPTV6PXr16tbt27y9vbW9ddfryFDhlSa789//rMCAwOVmpr62y92NZl6RKZ79+5as2aNWrdurZMnT2rOnDm67bbbdPDgQWVnZ8vLy0uBgYFO9wkJCVF2dnal+0xOTtacOXNqOTlcWdbcaLMj4J8iZx4wOwJQo3x8fHT69GlJkpubm5YsWaJmzZrp+++/16OPPqqpU6dq+fLlju3PnTunP/3pT3rjjTfk5uamf//3f9eUKVP05ptvXnL/SUlJWrlypRYtWqRevXrp5MmTOnz4cKV5ioqKlJiYqA4dOqiwsFAzZ87UkCFDtG/fPrm5uWn37t265ZZb9Mknn6hdu3by8vKSl5eXDh06pIKCAq1evVqS1KhRI0lSw4YNtWbNGoWHh+vAgQMaP368GjZsqKlTp0qSNm7cqCFDhmj69Ol6/fXXVVpaqk2bNl0y23PPPafnnntOW7du1S233HLlL3YVmVpkBgwY4Pi7Q4cO6t69u5o2baq3337b8Yt/VyopKUmJiYmO2wUFBYqIiLjqrACA+sswDKWmpmrLli2aOHGiJGnSpEmO9VFRUXr66af18MMPOxWZsrIyvfzyy2rRooUkacKECZo7d+4lH+Ps2bN64YUX9OKLL2rUqFGSpBYtWqhXr16V5oqPj3e6vWrVKgUHB+vbb79V+/btFRwcLEkKCgpymsbh4+OjkpKSi6Z2PPnkk07PacqUKUpJSXEUmWeeeUbDhw93OmDQsWPHi3JNmzZNb7zxhrZv36527dpVmr8mmD5H5v8LDAxUq1at9N133+mOO+5QaWmp8vLynI7K5OTkXHJOza/sdrvsdnsdpAUAXOs2bNggPz8/lZWVqaKiQg888IBmz54tSfrkk0+UnJysw4cPq6CgQBcuXFBxcbHOnTvn+MXaBg0aOEqM9Ms1hX69vtC/OnTokEpKStS3b98q5zty5IhmzpypXbt26aefflJFRYUkKSsrS+3bt7/i5/vWW29pyZIlyszMVGFhoS5cuCB/f3/H+n379mn8+PGX3ceCBQtUVFSkPXv2qHnz5lec4UqZPkfm/yssLFRmZqbCwsLUtWtXeXp6Op1Xy8jIUFZWlmJiYkxMCQCoL26//Xbt27dPR44c0fnz5/Xaa6/J19dXx44d0z333KMOHTro3XffVXp6upYtWyZJKi0tddzf09PTaX82m02GYVzysapzJmLgwIE6c+aMVq5cqV27dmnXrl0XZaiqtLQ0jRw5UnfddZc2bNigvXv3avr06U77qkrG2267TeXl5Xr77bevOEN1mHpEZsqUKRo4cKCaNm2qEydOaNasWXJ3d9eIESMUEBCgcePGKTExUY0aNZK/v78mTpyomJgYvrEEAKgTvr6+atmy5UXj6enpqqio0IIFC+Tm9ssxgav94L7xxhvl4+Oj1NRUPfTQQ7+5/enTp5WRkaGVK1fqtttukySnnzCRJC8vL0m/XBzzX8f/dezLL79U06ZNNX36dMfYDz/84LRNhw4dlJqaqjFjxlSa65ZbbtGECRPUv39/eXh4aMqUKb/5XK6GqUXmxx9/1IgRI3T69GkFBwerV69e2rlzp+Oc3qJFi+Tm5qb4+HiVlJSoX79+TuceAQAwQ8uWLVVWVqalS5dq4MCB2rFjh15++eWr2qe3t7emTZumqVOnysvLSz179tSpU6f0zTffaNy4cRdtf9111ykoKEivvPKKwsLClJWVpT/+8Y9O2zRu3Fg+Pj7avHmzmjRpIm9vbwUEBCgqKkpbtmxRRkaGgoKCFBAQoBtvvFFZWVlKSUlRt27dtHHjRr333ntO+5s1a5b69u2rFi1aaPjw4bpw4YI2bdqkadOmOW136623atOmTRowYIA8PDyc5hPVNFNPLaWkpOjEiRMqKSnRjz/+qJSUFKdzid7e3lq2bJnOnDmjoqIirVu37rLzYwAAqAsdO3bUwoULNX/+fLVv315vvvmmkpOTr3q/M2bM0OOPP66ZM2eqbdu2uv/++yudU+Pm5qaUlBSlp6erffv2mjx5sp5//nmnbTw8PLRkyRKtWLFC4eHhGjRokCRp/Pjxat26tW6++WYFBwdrx44d+t3vfqfJkydrwoQJ6tSpk7788kvNmDHDaX99+vTRO++8ow8//FCdOnVSbGysdu/efcl8vXr10saNG/Xkk09q6dKlV/3aVMZmVHay7hpRUFCggIAA5efnO01YwrWLr1+7Dr5+DVdXXFyso0ePqlmzZvL29jY7Tr1zude/qp/fLjXZFwAA4EpQZAAAgGVRZAAAgGVRZAAAgGVRZAAAgGVRZAAAgGVRZAAAgGVRZAAAgGVRZAAAgGWZeq0lAACuBV2feL1OHy/9+QervK1hGLrjjjvk7u6uLVu2OK1bvny5/uu//ksHDx5UkyZNajpmneCIDAAA1zCbzabVq1dr165dWrFihWP86NGjmjp1qpYuXWrZEiNRZAAAuOZFRETohRde0JQpU3T06FEZhqFx48bpzjvvVOfOnTVgwAD5+fkpJCREv//97/XTTz857vuXv/xF0dHR8vHxUVBQkOLi4lRUVGTis3FGkQEAoB4YNWqU+vbtq7Fjx+rFF1/UwYMHtWLFCsXGxqpz587as2ePNm/erJycHN13332SpJMnT2rEiBEaO3asDh06pG3btmno0KFypetNM0cGAIB64pVXXlG7du30+eef691339WKFSvUuXNnzZs3z7HNqlWrFBERob///e8qLCzUhQsXNHToUDVt2lSSFB0dbVb8S+KIDAAA9UTjxo31hz/8QW3bttXgwYO1f/9+ffbZZ/Lz83Msbdq0kSRlZmaqY8eO6tu3r6KjozVs2DCtXLlSP//8s8nPwhlFBgCAesTDw0MeHr+ckCksLNTAgQO1b98+p+XIkSPq3bu33N3d9fHHH+ujjz7STTfdpKVLl6p169Y6evSoyc/i/1BkAACop7p06aJvvvlGUVFRatmypdPi6+sr6ZdvPfXs2VNz5szR3r175eXlpffee8/k5P+HIgMAQD2VkJCgM2fOaMSIEfrqq6+UmZmpLVu2aMyYMSovL9euXbs0b9487dmzR1lZWVq3bp1OnTqltm3bmh3dgcm+AADUU+Hh4dqxY4emTZumO++8UyUlJWratKn69+8vNzc3+fv76/PPP9fixYtVUFCgpk2basGCBRowYIDZ0R1shit9h6oWFBQUKCAgQPn5+fL39zc7DupA1lzXmlFfn0XOPGB2BOCyiouLdfToUTVr1kze3t5mx6l3Lvf6V/Xzm1NLAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsrjWEgAAV6muL41Snct/jB49Wq+99pqSk5P1xz/+0TH+/vvva8iQIbLqFYs4IgMAQD3h7e2t+fPn6+effzY7So2hyAAAUE/ExcUpNDRUycnJlW7z7rvvql27drLb7YqKitKCBQvqMOGVo8gAAFBPuLu7a968eVq6dKl+/PHHi9anp6frvvvu0/Dhw3XgwAHNnj1bM2bM0Jo1a+o+bBVRZAAAqEeGDBmiTp06adasWRetW7hwofr27asZM2aoVatWGj16tCZMmKDnn3/ehKRVQ5EBAKCemT9/vl577TUdOnTIafzQoUPq2bOn01jPnj115MgRlZeX12XEKqPIAABQz/Tu3Vv9+vVTUlKS2VGuGl+/BgCgHnr22WfVqVMntW7d2jHWtm1b7dixw2m7HTt2qFWrVnJ3d6/riFVCkQEAoB6Kjo7WyJEjtWTJEsfY448/rm7duumpp57S/fffr7S0NL344otavny5iUkvj1NLAADUU3PnzlVFRYXjdpcuXfT2228rJSVF7du318yZMzV37lyNHj3avJC/gSMyAABcper80m5du9RXqKOiolRSUuI0Fh8fr/j4+DpKdfU4IgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAqPcMwzA7Qr1UE687RQYAUG95enpKks6dO2dykvrp19f91/8O1cHXrwEA9Za7u7sCAwOVm5srSWrQoIFsNpvJqa59hmHo3Llzys3NVWBg4FX9ajBFBgBQr4WGhkqSo8yg7gQGBjpe/+qiyAAA6jWbzaawsDA1btxYZWVlZsepNzw9PWvk+k0UGQAA9MtpJle9MCIqx2RfAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWS5TZJ599lnZbDZNmjTJMVZcXKyEhAQFBQXJz89P8fHxysnJMS8kAABwKS5RZL766iutWLFCHTp0cBqfPHmy1q9fr3feeUfbt2/XiRMnNHToUJNSAgAAV2N6kSksLNTIkSO1cuVKXXfddY7x/Px8vfrqq1q4cKFiY2PVtWtXrV69Wl9++aV27txZ6f5KSkpUUFDgtAAAgGuT6UUmISFBd999t+Li4pzG09PTVVZW5jTepk0bRUZGKi0trdL9JScnKyAgwLFERETUWnYAAGAuU4tMSkqKvv76ayUnJ1+0Ljs7W15eXgoMDHQaDwkJUXZ2dqX7TEpKUn5+vmM5fvx4TccGAAAuwrSrXx8/flyPPfaYPv74Y3l7e9fYfu12u+x2e43tDwAAuC7Tjsikp6crNzdXXbp0kYeHhzw8PLR9+3YtWbJEHh4eCgkJUWlpqfLy8pzul5OTo9DQUHNCAwAAl2LaEZm+ffvqwIEDTmNjxoxRmzZtNG3aNEVERMjT01OpqamKj4+XJGVkZCgrK0sxMTFmRAYAAC7GtCLTsGFDtW/f3mnM19dXQUFBjvFx48YpMTFRjRo1kr+/vyZOnKiYmBj16NHDjMgAAMDFmFZkqmLRokVyc3NTfHy8SkpK1K9fPy1fvtzsWAAAwEXYDMMwzA5RmwoKChQQEKD8/Hz5+/ubHQd1IGtutNkR8E+RMw/89kYAcAlV/fw2/XdkAAAAqosiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALMvUIvPSSy+pQ4cO8vf3l7+/v2JiYvTRRx851hcXFyshIUFBQUHy8/NTfHy8cnJyTEwMAABcialFpkmTJnr22WeVnp6uPXv2KDY2VoMGDdI333wjSZo8ebLWr1+vd955R9u3b9eJEyc0dOhQMyMDAAAXYjMMwzA7xP/XqFEjPf/887r33nsVHBystWvX6t5775UkHT58WG3btlVaWpp69OhRpf0VFBQoICBA+fn58vf3r83ocBFZc6PNjoB/ipx5wOwIACyqqp/fLjNHpry8XCkpKSoqKlJMTIzS09NVVlamuLg4xzZt2rRRZGSk0tLSKt1PSUmJCgoKnBYAAHBtMr3IHDhwQH5+frLb7Xr44Yf13nvv6aabblJ2dra8vLwUGBjotH1ISIiys7Mr3V9ycrICAgIcS0RERC0/AwAAYBbTi0zr1q21b98+7dq1S4888ohGjRqlb7/9ttr7S0pKUn5+vmM5fvx4DaYFAACuxMPsAF5eXmrZsqUkqWvXrvrqq6/0wgsv6P7771dpaany8vKcjsrk5OQoNDS00v3Z7XbZ7fbajg0AAFyA6Udk/lVFRYVKSkrUtWtXeXp6KjU11bEuIyNDWVlZiomJMTEhAABwFaYekUlKStKAAQMUGRmps2fPau3atdq2bZu2bNmigIAAjRs3TomJiWrUqJH8/f01ceJExcTEVPkbSwAA4NpmapHJzc3Vgw8+qJMnTyogIEAdOnTQli1bdMcdd0iSFi1aJDc3N8XHx6ukpET9+vXT8uXLzYwMAABciMv9jkxN43dk6h9+R8Z18DsyAKrLcr8jAwAAcKUoMgAAwLKqVWRiY2OVl5d30XhBQYFiY2OvNhMAAECVVKvIbNu2TaWlpReNFxcX64svvrjqUAAAAFVxRd9a+tvf/ub4+9tvv3W6VEB5ebk2b96sG264oebSAQAAXMYVFZlOnTrJZrPJZrNd8hSSj4+Pli5dWmPhAAAALueKiszRo0dlGIaaN2+u3bt3Kzg42LHOy8tLjRs3lru7e42HBAAAuJQrKjJNmzaV9MtlBAAAAMxW7V/2PXLkiD777DPl5uZeVGxmzpx51cEAAAB+S7WKzMqVK/XII4/o+uuvV2hoqGw2m2OdzWajyAAAgDpRrSLz9NNP65lnntG0adNqOg8AAECVVet3ZH7++WcNGzasprMAAABckWoVmWHDhmnr1q01nQUAAOCKVOvUUsuWLTVjxgzt3LlT0dHR8vT0dFr/n//5nzUSDgAA4HJshmEYV3qnZs2aVb5Dm03ff//9VYWqSVW9DDiuHVlzo82OgH+KnHnA7AgALKqqn9/VOiJz9OjRagcDAACoKdWaIwMAAOAKqnVEZuzYsZddv2rVqmqFAQAAuBLVKjI///yz0+2ysjIdPHhQeXl5l7yYJAAAQG2oVpF57733LhqrqKjQI488ohYtWlx1KAAAgKqosTkybm5uSkxM1KJFi2pqlwAAAJdVo5N9MzMzdeHChZrcJQAAQKWqdWopMTHR6bZhGDp58qQ2btyoUaNG1UgwAACA31KtIrN3716n225ubgoODtaCBQt+8xtNAAAANaVaReazzz6r6RwAAABXrFpF5lenTp1SRkaGJKl169YKDg6ukVAAAABVUa3JvkVFRRo7dqzCwsLUu3dv9e7dW+Hh4Ro3bpzOnTtX0xkBAAAuqVpFJjExUdu3b9f69euVl5envLw8ffDBB9q+fbsef/zxms4IAABwSdU6tfTuu+/qL3/5i/r06eMYu+uuu+Tj46P77rtPL730Uk3ls4yuT7xudgT803sNzU4AAKgr1Toic+7cOYWEhFw03rhxY04tAQCAOlOtIhMTE6NZs2apuLjYMXb+/HnNmTNHMTExNRYOAADgcqp1amnx4sXq37+/mjRpoo4dO0qS9u/fL7vdrq1bt9ZoQAAAgMpUq8hER0fryJEjevPNN3X48GFJ0ogRIzRy5Ej5+PjUaEAAAIDKVKvIJCcnKyQkROPHj3caX7VqlU6dOqVp06bVSDgAAIDLqdYcmRUrVqhNmzYXjbdr104vv/zyVYcCAACoimoVmezsbIWFhV00HhwcrJMnT151KAAAgKqoVpGJiIjQjh07LhrfsWOHwsPDrzoUAABAVVRrjsz48eM1adIklZWVKTY2VpKUmpqqqVOn8su+AACgzlSryDzxxBM6ffq0Hn30UZWWlkqSvL29NW3aNCUlJdVoQAAAgMpUq8jYbDbNnz9fM2bM0KFDh+Tj46Mbb7xRdru9pvMBAABUqlpF5ld+fn7q1q1bTWUBAAC4ItWa7AsAAOAKKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyTC0yycnJ6tatmxo2bKjGjRtr8ODBysjIcNqmuLhYCQkJCgoKkp+fn+Lj45WTk2NSYgAA4EpMLTLbt29XQkKCdu7cqY8//lhlZWW68847VVRU5Nhm8uTJWr9+vd555x1t375dJ06c0NChQ01MDQAAXIWHmQ++efNmp9tr1qxR48aNlZ6ert69eys/P1+vvvqq1q5dq9jYWEnS6tWr1bZtW+3cuVM9evQwIzYAAHARLjVHJj8/X5LUqFEjSVJ6errKysoUFxfn2KZNmzaKjIxUWlraJfdRUlKigoICpwUAAFybXKbIVFRUaNKkSerZs6fat28vScrOzpaXl5cCAwOdtg0JCVF2dvYl95OcnKyAgADHEhERUdvRAQCASVymyCQkJOjgwYNKSUm5qv0kJSUpPz/fsRw/fryGEgIAAFdj6hyZX02YMEEbNmzQ559/riZNmjjGQ0NDVVpaqry8PKejMjk5OQoNDb3kvux2u+x2e21HBgAALsDUIzKGYWjChAl677339Omnn6pZs2ZO67t27SpPT0+lpqY6xjIyMpSVlaWYmJi6jgsAAFyMqUdkEhIStHbtWn3wwQdq2LChY95LQECAfHx8FBAQoHHjxikxMVGNGjWSv7+/Jk6cqJiYGL6xBAAAzC0yL730kiSpT58+TuOrV6/W6NGjJUmLFi2Sm5ub4uPjVVJSon79+mn58uV1nBQAALgiU4uMYRi/uY23t7eWLVumZcuW1UEiAABgJS7zrSUAAIArRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACW5WF2AAAAqiprbrTZEfBPkTMPmB1BEkdkAACAhVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZVFkAACAZZlaZD7//HMNHDhQ4eHhstlsev/9953WG4ahmTNnKiwsTD4+PoqLi9ORI0fMCQsAAFyOqUWmqKhIHTt21LJlyy65/rnnntOSJUv08ssva9euXfL19VW/fv1UXFxcx0kBAIAr8jDzwQcMGKABAwZccp1hGFq8eLGefPJJDRo0SJL0+uuvKyQkRO+//76GDx9+yfuVlJSopKTEcbugoKDmgwMAAJfgsnNkjh49quzsbMXFxTnGAgIC1L17d6WlpVV6v+TkZAUEBDiWiIiIuogLAABM4LJFJjs7W5IUEhLiNB4SEuJYdylJSUnKz893LMePH6/VnAAAwDymnlqqDXa7XXa73ewYAACgDrjsEZnQ0FBJUk5OjtN4Tk6OYx0AAKjfXLbINGvWTKGhoUpNTXWMFRQUaNeuXYqJiTExGQAAcBWmnloqLCzUd99957h99OhR7du3T40aNVJkZKQmTZqkp59+WjfeeKOaNWumGTNmKDw8XIMHDzYvNAAAcBmmFpk9e/bo9ttvd9xOTEyUJI0aNUpr1qzR1KlTVVRUpP/4j/9QXl6eevXqpc2bN8vb29usyAAAwIWYWmT69OkjwzAqXW+z2TR37lzNnTu3DlMBAACrcNk5MgAAAL+FIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACzrmrvWEgDUtK5PvG52BPzTew3NTgBXwxEZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWRQZAABgWZYoMsuWLVNUVJS8vb3VvXt37d692+xIAADABbh8kXnrrbeUmJioWbNm6euvv1bHjh3Vr18/5ebmmh0NAACYzOWLzMKFCzV+/HiNGTNGN910k15++WU1aNBAq1atMjsaAAAwmYfZAS6ntLRU6enpSkpKcoy5ubkpLi5OaWlpl7xPSUmJSkpKHLfz8/MlSQUFBbWatbzkfK3uH1V31rPc7Aj4p9p+39UV3t+ug/e366jt9/ev+zcM47LbuXSR+emnn1ReXq6QkBCn8ZCQEB0+fPiS90lOTtacOXMuGo+IiKiVjHA97c0OgP+THGB2AlxjeH+7kDp6f589e1YBAZU/lksXmepISkpSYmKi43ZFRYXOnDmjoKAg2Ww2E5OhLhQUFCgiIkLHjx+Xv7+/2XEA1CDe3/WLYRg6e/aswsPDL7udSxeZ66+/Xu7u7srJyXEaz8nJUWho6CXvY7fbZbfbncYCAwNrKyJclL+/P//QAdco3t/1x+WOxPzKpSf7enl5qWvXrkpNTXWMVVRUKDU1VTExMSYmAwAArsClj8hIUmJiokaNGqWbb75Zt9xyixYvXqyioiKNGTPG7GgAAMBkLl9k7r//fp06dUozZ85Udna2OnXqpM2bN180ARiQfjm1OGvWrItOLwKwPt7fuBSb8VvfawIAAHBRLj1HBgAA4HIoMgAAwLIoMgAAwLIoMoCkqKgoLV682OwYAK7QsWPHZLPZtG/fPrOjwCQUGdS50aNHy2azXbR89913ZkcDUAd+/Tfg4YcfvmhdQkKCbDabRo8eXffBYEkUGZiif//+OnnypNPSrFkzs2MBqCMRERFKSUnR+fP/d0HO4uJirV27VpGRkSYmg9VQZGAKu92u0NBQp8Xd3V0ffPCBunTpIm9vbzVv3lxz5szRhQsXHPez2WxasWKF7rnnHjVo0EBt27ZVWlqavvvuO/Xp00e+vr669dZblZmZ6bhPZmamBg0apJCQEPn5+albt2765JNPLpsvLy9PDz30kIKDg+Xv76/Y2Fjt37+/1l4PoL7p0qWLIiIitG7dOsfYunXrFBkZqc6dOzvGNm/erF69eikwMFBBQUG65557nN7fl3Lw4EENGDBAfn5+CgkJ0e9//3v99NNPtfZcYC6KDFzGF198oQcffFCPPfaYvv32W61YsUJr1qzRM88847TdU089pQcffFD79u1TmzZt9MADD+gPf/iDkpKStGfPHhmGoQkTJji2Lyws1F133aXU1FTt3btX/fv318CBA5WVlVVplmHDhik3N1cfffSR0tPT1aVLF/Xt21dnzpyptecP1Ddjx47V6tWrHbdXrVp10a+2FxUVKTExUXv27FFqaqrc3Nw0ZMgQVVRUXHKfeXl5io2NVefOnbVnzx5t3rxZOTk5uu+++2r1ucBEBlDHRo0aZbi7uxu+vr6O5d577zX69u1rzJs3z2nbN954wwgLC3PclmQ8+eSTjttpaWmGJOPVV191jP3P//yP4e3tfdkM7dq1M5YuXeq43bRpU2PRokWGYRjGF198Yfj7+xvFxcVO92nRooWxYsWKK36+AJyNGjXKGDRokJGbm2vY7Xbj2LFjxrFjxwxvb2/j1KlTxqBBg4xRo0Zd8r6nTp0yJBkHDhwwDMMwjh49akgy9u7daxiGYTz11FPGnXfe6XSf48ePG5KMjIyM2nxaMInLX6IA16bbb79dL730kuO2r6+vOnTooB07djgdgSkvL1dxcbHOnTunBg0aSJI6dOjgWP/rpSqio6OdxoqLi1VQUCB/f38VFhZq9uzZ2rhxo06ePKkLFy7o/PnzlR6R2b9/vwoLCxUUFOQ0fv78+d88pA2g6oKDg3X33XdrzZo1MgxDd999t66//nqnbY4cOaKZM2dq165d+umnnxxHYrKystS+ffuL9rl//3599tln8vPzu2hdZmamWrVqVTtPBqahyMAUvr6+atmypdNYYWGh5syZo6FDh160vbe3t+NvT09Px982m63SsV//wZsyZYo+/vhj/elPf1LLli3l4+Oje++9V6WlpZfMVlhYqLCwMG3btu2idYGBgVV7ggCqZOzYsY5TwcuWLbto/cCBA9W0aVOtXLlS4eHhqqioUPv27S/7/h04cKDmz59/0bqwsLCaDQ+XQJGBy+jSpYsyMjIuKjhXa8eOHRo9erSGDBki6Zd/6I4dO3bZHNnZ2fLw8FBUVFSNZgHgrH///iotLZXNZlO/fv2c1p0+fVoZGRlauXKlbrvtNknSX//618vur0uXLnr33XcVFRUlDw8+4uoDJvvCZcycOVOvv/665syZo2+++UaHDh1SSkqKnnzyyava74033qh169Zp37592r9/vx544IFKJwpKUlxcnGJiYjR48GBt3bpVx44d05dffqnp06drz549V5UFgDN3d3cdOnRI3377rdzd3Z3WXXfddQoKCtIrr7yi7777Tp9++qkSExMvu7+EhASdOXNGI0aM0FdffaXMzExt2bJFY8aMUXl5eW0+FZiEIgOX0a9fP23YsEFbt25Vt27d1KNHDy1atEhNmza9qv0uXLhQ1113nW699VYNHDhQ/fr1U5cuXSrd3mazadOmTerdu7fGjBmjVq1aafjw4frhhx8cc3IA1Bx/f3/5+/tfNO7m5qaUlBSlp6erffv2mjx5sp5//vnL7is8PFw7duxQeXm57rzzTkVHR2vSpEkKDAyUmxsfedcim2EYhtkhAAAAqoN6CgAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiA+Ca1qdPH02aNMnsGABqCUUGQK3Lzs7WY489ppYtW8rb21shISHq2bOnXnrpJZ07d87seAAsjEuDAqhV33//vXr27KnAwEDNmzdP0dHRstvtOnDggF555RXdcMMN+t3vfmd2zEqVl5fLZrNxnR7ARfHOBFCrHn30UXl4eGjPnj2677771LZtWzVv3lyDBg3Sxo0bNXDgQElSXl6eHnroIQUHB8vf31+xsbHav3+/Yz+zZ89Wp06d9MYbbygqKkoBAQEaPny4zp4969imqKhIDz74oPz8/BQWFqYFCxZclKekpERTpkzRDTfcIF9fX3Xv3l3btm1zrF+zZo0CAwP14Ycf6qabbpLdbldWVlbtvUAArgpFBkCtOX36tLZu3aqEhAT5+vpechubzSZJGjZsmHJzc/XRRx8pPT1dXbp0Ud++fXXmzBnHtpmZmXr//fe1YcMGbdiwQdu3b9ezzz7rWP/EE09o+/bt+uCDD7R161Zt27ZNX3/9tdPjTZgwQWlpaUpJSdHf/vY3DRs2TP3799eRI0cc25w7d07z58/Xn//8Z33zzTdq3LhxTb4sAGqSAQC1ZOfOnYYkY926dU7jQUFBhq+vr+Hr62tMnTrV+OKLLwx/f3+juLjYabsWLVoYK1asMAzDMGbNmmU0aNDAKCgocKx/4oknjO7duxuGYRhnz541vLy8jLffftux/vTp04aPj4/x2GOPGYZhGD/88IPh7u5u/OMf/3B6nL59+xpJSUmGYRjG6tWrDUnGvn37auZFAFCrmCMDoM7t3r1bFRUVGjlypEpKSrR//34VFhYqKCjIabvz588rMzPTcTsqKkoNGzZ03A4LC1Nubq6kX47WlJaWqnv37o71jRo1UuvWrR23Dxw4oPLycrVq1crpcUpKSpwe28vLSx06dKiZJwugVlFkANSali1bymazKSMjw2m8efPmkiQfHx9JUmFhocLCwpzmqvwqMDDQ8benp6fTOpvNpoqKiirnKSwslLu7u9LT0+Xu7u60zs/Pz/G3j4+P45QXANdGkQFQa4KCgnTHHXfoxRdf1MSJEyudJ9OlSxdlZ2fLw8NDUVFR1XqsFi1ayNPTU7t27VJkZKQk6eeff9bf//53/du//ZskqXPnziovL1dubq5uu+22aj0OANfCZF8AtWr58uW6cOGCbr75Zr311ls6dOiQMjIy9N///d86fPiw3N3dFRcXp5iYGA0ePFhbt27VsWPH9OWXX2r69Onas2dPlR7Hz89P48aN0xNPPKFPP/1UBw8e1OjRo52+Nt2qVSuNHDlSDz74oNatW6ejR49q9+7dSk5O1saNG2vrJQBQizgiA6BWtWjRQnv37tW8efOUlJSkH3/8UXa7XTfddJOmTJmiRx99VDabTZs2bdL06dM1ZswYnTp1SqGhoerdu7dCQkKq/FjPP/+8CgsLNXDgQDVs2FCPP/648vPznbZZvXq1nn76aT3++OP6xz/+oeuvv149evTQPffcU9NPHUAdsBmGYZgdAgAAoDo4tQQAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACyLIgMAACzrfwHSElTPrf2IRgAAAABJRU5ErkJggg==",
 980 |       "text/plain": [
 981 |        "<Figure size 640x480 with 1 Axes>"
 982 |       ]
 983 |      },
 984 |      "metadata": {},
 985 |      "output_type": "display_data"
 986 |     }
 987 |    ],
 988 |    "source": [
 989 |     "sns.countplot(data = df, x = 'Gender' , hue = 'Panic attack')"
 990 |    ]
 991 |   },
 992 |   {
 993 |    "cell_type": "markdown",
 994 |    "metadata": {},
 995 |    "source": [
 996 |     "### Distribution of mental health treatment by gender"
 997 |    ]
 998 |   },
 999 |   {
1000 |    "cell_type": "code",
1001 |    "execution_count": 72,
1002 |    "metadata": {},
1003 |    "outputs": [
1004 |     {
1005 |      "data": {
1006 |       "text/plain": [
1007 |        "<Axes: xlabel='Gender', ylabel='count'>"
1008 |       ]
1009 |      },
1010 |      "execution_count": 72,
1011 |      "metadata": {},
1012 |      "output_type": "execute_result"
1013 |     },
1014 |     {
1015 |      "data": {
1016 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxKUlEQVR4nO3de1hU9b7H8c+AMCA4kIRcEgXzghc0UY+SpaUkWnk02Vrm3l6zG12U7MIpNa2kMtMuamaKXbbHnWW1q51alFSGJqRuTSM1DEsBKwHRGAzW+aPj7D1bNERgzar363nW87B+v9/81nfN84zzca3fzNgMwzAEAABgQV5mFwAAAFBXBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZTcwuoKFVV1fr4MGDatasmWw2m9nlAACAWjAMQ0ePHlVkZKS8vE5/3eV3H2QOHjyoqKgos8sAAAB1cODAAbVs2fK0/b/7INOsWTNJvz4RDofD5GoAAEBtlJWVKSoqyvU+fjq/+yBz8naSw+EgyAAAYDG/tSyExb4AAMCyCDIAAMCyCDIAAMCyfvdrZAAAaGhVVVU6ceKE2WVYio+Pj7y9vc95HoIMAAB1ZBiGCgsLVVJSYnYplhQcHKzw8PBz+p43ggwAAHV0MsS0aNFCTZs25YtXa8kwDB0/flzFxcWSpIiIiDrPRZABAKAOqqqqXCEmJCTE7HIsx9/fX5JUXFysFi1a1Pk2E4t9AQCog5NrYpo2bWpyJdZ18rk7l/VFBBkAAM4Bt5Pqrj6eO4IMAACwLIIMAACwLFODTHR0tGw22ylbSkqKJKmiokIpKSkKCQlRYGCgkpOTVVRUZGbJAADAg5gaZLZs2aJDhw65tvfff1+SNHLkSEnS1KlT9fbbb2v16tXKysrSwYMHNWLECDNLBgCgVi677DJNmTKlUY41fvx4DR8+vFGOVRsrVqxQcHBwoxzL1I9fh4aGuu0/+uijuvDCC9W/f3+VlpZq2bJlWrlypQYMGCBJysjIUMeOHbVp0yb16dPHjJIBAKgXhmGoqqpKTZrwTSjnwmPWyFRWVuqVV17RxIkTZbPZlJubqxMnTigxMdE1JjY2Vq1atVJ2dvZp53E6nSorK3PbAABoTOPHj1dWVpaeeuop17KJFStWyGaz6b333lOPHj1kt9v16aefqrq6Wunp6YqJiZG/v7+6deum1157zTVXVVWVJk2a5Orv0KGDnnrqKVf/gw8+qBdffFFvvfWW61gbNmzQ/v37ZbPZ9Oqrr+rSSy+Vv7+/evXqpa+//lpbtmxRz549FRgYqCFDhujw4cNu9b/wwgvq2LGj/Pz8FBsbq0WLFrn6Ts67Zs0aXX755WratKm6devmem/esGGDJkyYoNLSUlc9Dz74YIM91x4TA998802VlJRo/Pjxkn79tkRfX99TLk2FhYWpsLDwtPOkp6dr1qxZDVhpzXrc/VKjHxM1y5071uwSAPzBPfXUU/r666/VpUsXzZ49W5L05ZdfSpLuu+8+PfHEE2rTpo3OO+88paen65VXXtFzzz2ndu3a6eOPP9af//xnhYaGqn///qqurlbLli21evVqhYSE6LPPPtONN96oiIgIjRo1StOmTdPu3btVVlamjIwMSVLz5s118OBBSdLMmTO1YMECtWrVShMnTtT111+vZs2a6amnnlLTpk01atQozZgxQ4sXL5Yk/fWvf9WMGTP07LPPqnv37tq6dasmT56sgIAAjRs3znWO999/v5544gm1a9dO999/v0aPHq29e/fq4osv1oIFCzRjxgzl5eVJkgIDAxvsufaYILNs2TINGTJEkZGR5zRPWlqaUlNTXftlZWWKioo61/IAAKi1oKAg+fr6qmnTpgoPD5ckffXVV5Kk2bNn64orrpD0612EOXPm6IMPPlBCQoIkqU2bNvr000+1ZMkS9e/fXz4+Pm7/QY+JiVF2drZeffVVjRo1SoGBgfL395fT6XQd699NmzZNSUlJkqQ777xTo0ePVmZmpvr27StJmjRpklasWOEaP3PmTM2bN8+1JjUmJka7du3SkiVL3ILMtGnTdNVVV0mSZs2apc6dO2vv3r2KjY1VUFCQbDZbjfXUN48IMt9++60++OADrVmzxtUWHh6uyspKlZSUuF2VKSoqOuMTY7fbZbfbG7JcAADqrGfPnq6/9+7dq+PHj7uCzUmVlZXq3r27a3/hwoVavny5CgoK9PPPP6uyslIXXXRRrY7XtWtX199hYWGSpLi4OLe2k795dOzYMe3bt0+TJk3S5MmTXWN++eUXBQUFnXbek7+VVFxcrNjY2FrVVV88IshkZGSoRYsWrmQnST169JCPj48yMzOVnJwsScrLy1NBQYErtQIAYDUBAQGuv8vLyyVJ7777ri644AK3cSf/U75q1SpNmzZN8+bNU0JCgpo1a6a5c+dq8+bNtTqej4+P6++T36T7n23V1dVu9SxdulS9e/d2m+c/fwuppnlPztOYTA8y1dXVysjI0Lhx49xWbgcFBWnSpElKTU1V8+bN5XA4dPvttyshIYFPLAEAPJ6vr6+qqqrOOKZTp06y2+0qKChQ//79axyzceNGXXzxxbr11ltdbfv27TvrY9VGWFiYIiMj9c0332jMmDF1nqe+6qkN04PMBx98oIKCAk2cOPGUvvnz58vLy0vJyclyOp1KSkpyWzkNAICnio6O1ubNm7V//34FBgbWeLWiWbNmmjZtmqZOnarq6mpdcsklKi0t1caNG+VwODRu3Di1a9dOL730ktatW6eYmBi9/PLL2rJli2JiYtyOtW7dOuXl5SkkJOSU20BnY9asWbrjjjsUFBSkwYMHy+l0KicnR0eOHHFbg/pb515eXq7MzEx169ZNTZs2bbAf1zT949eDBg2SYRhq3779KX1+fn5auHChfvrpJx07dkxr1qxplIVDAACcq2nTpsnb21udOnVSaGioCgoKahz30EMPafr06UpPT1fHjh01ePBgvfvuu66gctNNN2nEiBG69tpr1bt3b/34449uV2ckafLkyerQoYN69uyp0NBQbdy4sc5133DDDXrhhReUkZGhuLg49e/fXytWrHALTr/l4osv1s0336xrr71WoaGhevzxx+tcz2+xGYZhNNjsHqCsrExBQUEqLS2Vw+FosOPw8WvPwcevATSGiooK5efnKyYmRn5+fmaXY0lneg5r+/5t+hUZAACAuiLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyzL9t5YAAPi9a+xvfz/bbzgfP368XnzxRaWnp+u+++5ztb/55pu65ppr5Mk/AsAVGQAAID8/Pz322GM6cuSI2aWcFYIMAABQYmKiwsPDlZ6eftoxr7/+ujp37iy73a7o6GjNmzevESusGUEGAADI29tbc+bM0TPPPKPvvvvulP7c3FyNGjVK1113nXbs2KEHH3xQ06dP14oVKxq/2H9DkAEAAJKka665RhdddJFmzpx5St+TTz6pgQMHavr06Wrfvr3Gjx+v2267TXPnzjWh0n8hyAAAAJfHHntML774onbv3u3Wvnv3bvXt29etrW/fvtqzZ4+qqqoas0Q3BBkAAODSr18/JSUlKS0tzexSaoWPXwMAADePPvqoLrroInXo0MHV1rFjR23cuNFt3MaNG9W+fXt5e3s3dokuBBkAAOAmLi5OY8aM0dNPP+1qu+uuu9SrVy899NBDuvbaa5Wdna1nn31WixYtMrFSbi0BAIAazJ49W9XV1a79+Ph4vfrqq1q1apW6dOmiGTNmaPbs2Ro/frx5RYorMgAANLiz/abdxlbTR6ijo6PldDrd2pKTk5WcnNxIVdUOV2QAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBl8RMFAAA0sILZcY16vFYzdtR6rGEYuuKKK+Tt7a1169a59S1atEj/8z//o507d6ply5b1XWa94IoMAAB/YDabTRkZGdq8ebOWLFnias/Pz9c999yjZ555xmNDjESQAQDgDy8qKkpPPfWUpk2bpvz8fBmGoUmTJmnQoEHq3r27hgwZosDAQIWFhekvf/mLfvjhB9djX3vtNcXFxcnf318hISFKTEzUsWPHGq12ggwAANC4ceM0cOBATZw4Uc8++6x27typJUuWaMCAAerevbtycnK0du1aFRUVadSoUZKkQ4cOafTo0Zo4caJ2796tDRs2aMSIETIMo9HqZo0MAACQJD3//PPq3LmzPv74Y73++utasmSJunfvrjlz5rjGLF++XFFRUfr6669VXl6uX375RSNGjFDr1q0lSXFxjbseiCsyAABAktSiRQvddNNN6tixo4YPH67t27fro48+UmBgoGuLjY2VJO3bt0/dunXTwIEDFRcXp5EjR2rp0qU6cuRIo9ZMkAEAAC5NmjRRkya/3rApLy/X0KFDtW3bNrdtz5496tevn7y9vfX+++/rvffeU6dOnfTMM8+oQ4cOys/Pb7R6CTIAAKBG8fHx+vLLLxUdHa22bdu6bQEBAZJ+/dRT3759NWvWLG3dulW+vr564403Gq1G04PM999/rz//+c8KCQmRv7+/4uLilJOT4+o3DEMzZsxQRESE/P39lZiYqD179phYMQAAfwwpKSn66aefNHr0aG3ZskX79u3TunXrNGHCBFVVVWnz5s2aM2eOcnJyVFBQoDVr1ujw4cPq2LFjo9VoapA5cuSI+vbtKx8fH7333nvatWuX5s2bp/POO8815vHHH9fTTz+t5557Tps3b1ZAQICSkpJUUVFhYuUAAPz+RUZGauPGjaqqqtKgQYMUFxenKVOmKDg4WF5eXnI4HPr444915ZVXqn379nrggQc0b948DRkypNFqtBmN+Rmp/3Dfffdp48aN+uSTT2rsNwxDkZGRuuuuuzRt2jRJUmlpqcLCwrRixQpdd911v3mMsrIyBQUFqbS0VA6Ho17r/3c97n6pwebG2cmdO9bsEgD8AVRUVCg/P18xMTHy8/MzuxxLOtNzWNv3b1OvyPz9739Xz549NXLkSLVo0ULdu3fX0qVLXf35+fkqLCxUYmKiqy0oKEi9e/dWdnZ2jXM6nU6VlZW5bQAA4PfJ1CDzzTffaPHixWrXrp3WrVunW265RXfccYdefPFFSVJhYaEkKSwszO1xYWFhrr7/lJ6erqCgINcWFRXVsCcBAABMY2qQqa6uVnx8vObMmaPu3bvrxhtv1OTJk/Xcc8/Vec60tDSVlpa6tgMHDtRjxQAAwJOYGmQiIiLUqVMnt7aOHTuqoKBAkhQeHi5JKioqchtTVFTk6vtPdrtdDofDbQMAAL9PpgaZvn37Ki8vz63t66+/dn3NcUxMjMLDw5WZmenqLysr0+bNm5WQkNCotQIAUBMTPzNjefXx3Jn6W0tTp07VxRdfrDlz5mjUqFH6/PPP9fzzz+v555+X9OuX7EyZMkUPP/yw2rVrp5iYGE2fPl2RkZEaPny4maUDAP7gfHx8JEnHjx+Xv7+/ydVY0/HjxyX967msC1ODTK9evfTGG28oLS1Ns2fPVkxMjBYsWKAxY8a4xtxzzz06duyYbrzxRpWUlOiSSy7R2rVr+agbAMBU3t7eCg4OVnFxsSSpadOmstlsJldlDYZh6Pjx4youLlZwcLC8vb3rPJep3yPTGPgemT8evkcGQGMxDEOFhYUqKSkxuxRLCg4OVnh4eI0BsLbv36ZekQEAwMpsNpsiIiLUokULnThxwuxyLMXHx+ecrsScRJABAOAceXt718ubMs6e6T8aCQAAUFcEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFmmBpkHH3xQNpvNbYuNjXX1V1RUKCUlRSEhIQoMDFRycrKKiopMrBgAAHgS06/IdO7cWYcOHXJtn376qatv6tSpevvtt7V69WplZWXp4MGDGjFihInVAgAAT9LE9AKaNFF4ePgp7aWlpVq2bJlWrlypAQMGSJIyMjLUsWNHbdq0SX369KlxPqfTKafT6dovKytrmMIBAIDpTL8is2fPHkVGRqpNmzYaM2aMCgoKJEm5ubk6ceKEEhMTXWNjY2PVqlUrZWdnn3a+9PR0BQUFubaoqKgGPwcAAGAOU4NM7969tWLFCq1du1aLFy9Wfn6+Lr30Uh09elSFhYXy9fVVcHCw22PCwsJUWFh42jnT0tJUWlrq2g4cONDAZwEAAMxi6q2lIUOGuP7u2rWrevfurdatW+vVV1+Vv79/nea02+2y2+31VSIAAPBgpt9a+nfBwcFq37699u7dq/DwcFVWVqqkpMRtTFFRUY1ragAAwB+PRwWZ8vJy7du3TxEREerRo4d8fHyUmZnp6s/Ly1NBQYESEhJMrBIAAHgKU28tTZs2TUOHDlXr1q118OBBzZw5U97e3ho9erSCgoI0adIkpaamqnnz5nI4HLr99tuVkJBw2k8sAQCAPxZTg8x3332n0aNH68cff1RoaKguueQSbdq0SaGhoZKk+fPny8vLS8nJyXI6nUpKStKiRYvMLBkAAHgQm2EYhtlFNKSysjIFBQWptLRUDoejwY7T4+6XGmxunJ3cuWPNLgEAcI5q+/7tUWtkAAAAzgZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWJbHBJlHH31UNptNU6ZMcbVVVFQoJSVFISEhCgwMVHJysoqKiswrEgAAeBSPCDJbtmzRkiVL1LVrV7f2qVOn6u2339bq1auVlZWlgwcPasSIESZVCQAAPI3pQaa8vFxjxozR0qVLdd5557naS0tLtWzZMj355JMaMGCAevTooYyMDH322WfatGmTiRUDAABPUacgM2DAAJWUlJzSXlZWpgEDBpzVXCkpKbrqqquUmJjo1p6bm6sTJ064tcfGxqpVq1bKzs4+7XxOp1NlZWVuGwAA+H1qUpcHbdiwQZWVlae0V1RU6JNPPqn1PKtWrdIXX3yhLVu2nNJXWFgoX19fBQcHu7WHhYWpsLDwtHOmp6dr1qxZta4BAABY11kFmX/+85+uv3ft2uUWKKqqqrR27VpdcMEFtZrrwIEDuvPOO/X+++/Lz8/vbMo4o7S0NKWmprr2y8rKFBUVVW/zAwAAz3FWQeaiiy6SzWaTzWar8RaSv7+/nnnmmVrNlZubq+LiYsXHx7vaqqqq9PHHH+vZZ5/VunXrVFlZqZKSErerMkVFRQoPDz/tvHa7XXa7vfYnBQAALOusgkx+fr4Mw1CbNm30+eefKzQ01NXn6+urFi1ayNvbu1ZzDRw4UDt27HBrmzBhgmJjY3XvvfcqKipKPj4+yszMVHJysiQpLy9PBQUFSkhIOJuyAQDA79RZBZnWrVtLkqqrq8/5wM2aNVOXLl3c2gICAhQSEuJqnzRpklJTU9W8eXM5HA7dfvvtSkhIUJ8+fc75+AAAwPrqtNhXkvbs2aOPPvpIxcXFpwSbGTNmnHNhkjR//nx5eXkpOTlZTqdTSUlJWrRoUb3MDQAArM9mGIZxtg9aunSpbrnlFp1//vkKDw+XzWb714Q2m7744ot6LfJclJWVKSgoSKWlpXI4HA12nB53v9Rgc+Ps5M4da3YJAIBzVNv37zpdkXn44Yf1yCOP6N57761zgQAAAOeqTl+Id+TIEY0cObK+awEAADgrdQoyI0eO1Pr16+u7FgAAgLNSp1tLbdu21fTp07Vp0ybFxcXJx8fHrf+OO+6ol+IAAADOpE5B5vnnn1dgYKCysrKUlZXl1mez2QgyAACgUdQpyOTn59d3HQAAAGetTmtkAAAAPEGdrshMnDjxjP3Lly+vUzEAAABno05B5siRI277J06c0M6dO1VSUlLjj0kCAAA0hDoFmTfeeOOUturqat1yyy268MILz7koAACA2qi3NTJeXl5KTU3V/Pnz62tKAACAM6rXxb779u3TL7/8Up9TAgAAnFadbi2lpqa67RuGoUOHDundd9/VuHHj6qUwAACA31KnILN161a3fS8vL4WGhmrevHm/+YkmAACA+lKnIPPRRx/Vdx0AAABnrU5B5qTDhw8rLy9PktShQweFhobWS1EAAAC1UafFvseOHdPEiRMVERGhfv36qV+/foqMjNSkSZN0/Pjx+q4RAACgRnUKMqmpqcrKytLbb7+tkpISlZSU6K233lJWVpbuuuuu+q4RAACgRnW6tfT666/rtdde02WXXeZqu/LKK+Xv769Ro0Zp8eLF9VUfAADAadXpiszx48cVFhZ2SnuLFi24tQQAABpNnYJMQkKCZs6cqYqKClfbzz//rFmzZikhIaHeigMAADiTOt1aWrBggQYPHqyWLVuqW7dukqTt27fLbrdr/fr19VogAADA6dQpyMTFxWnPnj3661//qq+++kqSNHr0aI0ZM0b+/v71WiAAAMDp1CnIpKenKywsTJMnT3ZrX758uQ4fPqx77723XooDAAA4kzqtkVmyZIliY2NPae/cubOee+65cy4KAACgNuoUZAoLCxUREXFKe2hoqA4dOnTORQEAANRGnYJMVFSUNm7ceEr7xo0bFRkZec5FAQAA1Ead1shMnjxZU6ZM0YkTJzRgwABJUmZmpu655x6+2RcAADSaOgWZu+++Wz/++KNuvfVWVVZWSpL8/Px07733Ki0trV4LBAAAOJ06BRmbzabHHntM06dP1+7du+Xv76927drJbrfXd30AAACnVacgc1JgYKB69epVX7UAAACclTot9gUAAPAEBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZpgaZxYsXq2vXrnI4HHI4HEpISNB7773n6q+oqFBKSopCQkIUGBio5ORkFRUVmVgxAADwJKYGmZYtW+rRRx9Vbm6ucnJyNGDAAA0bNkxffvmlJGnq1Kl6++23tXr1amVlZengwYMaMWKEmSUDAAAPck4/UXCuhg4d6rb/yCOPaPHixdq0aZNatmypZcuWaeXKla5f2M7IyFDHjh21adMm9enTx4ySAQCAB/GYNTJVVVVatWqVjh07poSEBOXm5urEiRNKTEx0jYmNjVWrVq2UnZ192nmcTqfKysrcNgAA8PtkepDZsWOHAgMDZbfbdfPNN+uNN95Qp06dVFhYKF9fXwUHB7uNDwsLU2Fh4WnnS09PV1BQkGuLiopq4DMAAABmMT3IdOjQQdu2bdPmzZt1yy23aNy4cdq1a1ed50tLS1NpaalrO3DgQD1WCwAAPImpa2QkydfXV23btpUk9ejRQ1u2bNFTTz2la6+9VpWVlSopKXG7KlNUVKTw8PDTzme322W32xu6bAAA4AFMvyLzn6qrq+V0OtWjRw/5+PgoMzPT1ZeXl6eCggIlJCSYWCEAAPAUpl6RSUtL05AhQ9SqVSsdPXpUK1eu1IYNG7Ru3ToFBQVp0qRJSk1NVfPmzeVwOHT77bcrISGBTywBAABJJgeZ4uJijR07VocOHVJQUJC6du2qdevW6YorrpAkzZ8/X15eXkpOTpbT6VRSUpIWLVpkZskAAMCD2AzDMMwuoiGVlZUpKChIpaWlcjgcDXacHne/1GBz4+zkzh1rdgkAgHNU2/dvj1sjAwAAUFsEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFlNzC4AADxdj7tfMrsE/L/cuWPNLgEehisyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAskwNMunp6erVq5eaNWumFi1aaPjw4crLy3MbU1FRoZSUFIWEhCgwMFDJyckqKioyqWIAAOBJTA0yWVlZSklJ0aZNm/T+++/rxIkTGjRokI4dO+YaM3XqVL399ttavXq1srKydPDgQY0YMcLEqgEAgKdoYubB165d67a/YsUKtWjRQrm5uerXr59KS0u1bNkyrVy5UgMGDJAkZWRkqGPHjtq0aZP69OlzypxOp1NOp9O1X1ZW1rAnAQAATONRa2RKS0slSc2bN5ck5ebm6sSJE0pMTHSNiY2NVatWrZSdnV3jHOnp6QoKCnJtUVFRDV84AAAwhccEmerqak2ZMkV9+/ZVly5dJEmFhYXy9fVVcHCw29iwsDAVFhbWOE9aWppKS0td24EDBxq6dAAAYBJTby39u5SUFO3cuVOffvrpOc1jt9tlt9vrqSoAAODJPOKKzG233aZ33nlHH330kVq2bOlqDw8PV2VlpUpKStzGFxUVKTw8vJGrBAAAnsbUIGMYhm677Ta98cYb+vDDDxUTE+PW36NHD/n4+CgzM9PVlpeXp4KCAiUkJDR2uQAAwMOYemspJSVFK1eu1FtvvaVmzZq51r0EBQXJ399fQUFBmjRpklJTU9W8eXM5HA7dfvvtSkhIqPETSwAA4I/F1CCzePFiSdJll13m1p6RkaHx48dLkubPny8vLy8lJyfL6XQqKSlJixYtauRKAQCAJzI1yBiG8Ztj/Pz8tHDhQi1cuLARKgIAAFbiEYt9AQAA6oIgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALMvUIPPxxx9r6NChioyMlM1m05tvvunWbxiGZsyYoYiICPn7+ysxMVF79uwxp1gAAOBxTA0yx44dU7du3bRw4cIa+x9//HE9/fTTeu6557R582YFBAQoKSlJFRUVjVwpAADwRE3MPPiQIUM0ZMiQGvsMw9CCBQv0wAMPaNiwYZKkl156SWFhYXrzzTd13XXX1fg4p9Mpp9Pp2i8rK6v/wgEAgEfw2DUy+fn5KiwsVGJioqstKChIvXv3VnZ29mkfl56erqCgINcWFRXVGOUCAAATeGyQKSwslCSFhYW5tYeFhbn6apKWlqbS0lLXduDAgQatEwAAmMfUW0sNwW63y263m10GAABoBB57RSY8PFySVFRU5NZeVFTk6gMAAH9sHhtkYmJiFB4erszMTFdbWVmZNm/erISEBBMrAwAAnsLUW0vl5eXau3evaz8/P1/btm1T8+bN1apVK02ZMkUPP/yw2rVrp5iYGE2fPl2RkZEaPny4eUUDAACPYWqQycnJ0eWXX+7aT01NlSSNGzdOK1as0D333KNjx47pxhtvVElJiS655BKtXbtWfn5+ZpUMAAA8iKlB5rLLLpNhGKftt9lsmj17tmbPnt2IVQEAAKvw2DUyAAAAv4UgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALKuJ2QUA9a1gdpzZJeD/tZqxw+wSAPzOcUUGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYVhOzCwAAoLYKZseZXQL+X6sZO8wuQZJFrsgsXLhQ0dHR8vPzU+/evfX555+bXRIAAPAAHh9k/va3vyk1NVUzZ87UF198oW7duikpKUnFxcVmlwYAAEzm8UHmySef1OTJkzVhwgR16tRJzz33nJo2barly5ebXRoAADCZR6+RqaysVG5urtLS0lxtXl5eSkxMVHZ2do2PcTqdcjqdrv3S0lJJUllZWYPWWuX8uUHnR+0d9akyuwT8v4Z+3TUWXt+eg9e352jo1/fJ+Q3DOOM4jw4yP/zwg6qqqhQWFubWHhYWpq+++qrGx6Snp2vWrFmntEdFRTVIjfA8XcwuAP+SHmR2Bfid4fXtQRrp9X306FEFBZ3+WB4dZOoiLS1Nqamprv3q6mr99NNPCgkJkc1mM7EyNIaysjJFRUXpwIEDcjgcZpcDoB7x+v5jMQxDR48eVWRk5BnHeXSQOf/88+Xt7a2ioiK39qKiIoWHh9f4GLvdLrvd7tYWHBzcUCXCQzkcDv6hA36neH3/cZzpSsxJHr3Y19fXVz169FBmZqarrbq6WpmZmUpISDCxMgAA4Ak8+oqMJKWmpmrcuHHq2bOn/uu//ksLFizQsWPHNGHCBLNLAwAAJvP4IHPttdfq8OHDmjFjhgoLC3XRRRdp7dq1pywABqRfby3OnDnzlNuLAKyP1zdqYjN+63NNAAAAHsqj18gAAACcCUEGAABYFkEGAABYFkEGkBQdHa0FCxaYXQaAs7R//37ZbDZt27bN7FJgEoIMGt348eNls9lO2fbu3Wt2aQAawcl/A26++eZT+lJSUmSz2TR+/PjGLwyWRJCBKQYPHqxDhw65bTExMWaXBaCRREVFadWqVfr553/9IGdFRYVWrlypVq1amVgZrIYgA1PY7XaFh4e7bd7e3nrrrbcUHx8vPz8/tWnTRrNmzdIvv/ziepzNZtOSJUt09dVXq2nTpurYsaOys7O1d+9eXXbZZQoICNDFF1+sffv2uR6zb98+DRs2TGFhYQoMDFSvXr30wQcfnLG+kpIS3XDDDQoNDZXD4dCAAQO0ffv2Bns+gD+a+Ph4RUVFac2aNa62NWvWqFWrVurevburbe3atbrkkksUHByskJAQXX311W6v75rs3LlTQ4YMUWBgoMLCwvSXv/xFP/zwQ4OdC8xFkIHH+OSTTzR27Fjdeeed2rVrl5YsWaIVK1bokUcecRv30EMPaezYsdq2bZtiY2N1/fXX66abblJaWppycnJkGIZuu+021/jy8nJdeeWVyszM1NatWzV48GANHTpUBQUFp61l5MiRKi4u1nvvvafc3FzFx8dr4MCB+umnnxrs/IE/mokTJyojI8O1v3z58lO+tf3YsWNKTU1VTk6OMjMz5eXlpWuuuUbV1dU1zllSUqIBAwaoe/fuysnJ0dq1a1VUVKRRo0Y16LnARAbQyMaNG2d4e3sbAQEBru1Pf/qTMXDgQGPOnDluY19++WUjIiLCtS/JeOCBB1z72dnZhiRj2bJlrrb//d//Nfz8/M5YQ+fOnY1nnnnGtd+6dWtj/vz5hmEYxieffGI4HA6joqLC7TEXXnihsWTJkrM+XwDuxo0bZwwbNswoLi427Ha7sX//fmP//v2Gn5+fcfjwYWPYsGHGuHHjanzs4cOHDUnGjh07DMMwjPz8fEOSsXXrVsMwDOOhhx4yBg0a5PaYAwcOGJKMvLy8hjwtmMTjf6IAv0+XX365Fi9e7NoPCAhQ165dtXHjRrcrMFVVVaqoqNDx48fVtGlTSVLXrl1d/Sd/qiIuLs6traKiQmVlZXI4HCovL9eDDz6od999V4cOHdIvv/yin3/++bRXZLZv367y8nKFhIS4tf/888+/eUkbQO2Fhobqqquu0ooVK2QYhq666iqdf/75bmP27NmjGTNmaPPmzfrhhx9cV2IKCgrUpUuXU+bcvn27PvroIwUGBp7St2/fPrVv375hTgamIcjAFAEBAWrbtq1bW3l5uWbNmqURI0acMt7Pz8/1t4+Pj+tvm8122raT/+BNmzZN77//vp544gm1bdtW/v7++tOf/qTKysoaaysvL1dERIQ2bNhwSl9wcHDtThBArUycONF1K3jhwoWn9A8dOlStW7fW0qVLFRkZqerqanXp0uWMr9+hQ4fqscceO6UvIiKifouHRyDIwGPEx8crLy/vlIBzrjZu3Kjx48frmmuukfTrP3T79+8/Yx2FhYVq0qSJoqOj67UWAO4GDx6syspK2Ww2JSUlufX9+OOPysvL09KlS3XppZdKkj799NMzzhcfH6/XX39d0dHRatKEt7g/Ahb7wmPMmDFDL730kmbNmqUvv/xSu3fv1qpVq/TAAw+c07zt2rXTmjVrtG3bNm3fvl3XX3/9aRcKSlJiYqISEhI0fPhwrV+/Xvv379dnn32m+++/Xzk5OedUCwB33t7e2r17t3bt2iVvb2+3vvPOO08hISF6/vnntXfvXn344YdKTU0943wpKSn66aefNHr0aG3ZskX79u3TunXrNGHCBFVVVTXkqcAkBBl4jKSkJL3zzjtav369evXqpT59+mj+/Plq3br1Oc375JNP6rzzztPFF1+soUOHKikpSfHx8acdb7PZ9I9//EP9+vXThAkT1L59e1133XX69ttvXWtyANQfh8Mhh8NxSruXl5dWrVql3NxcdenSRVOnTtXcuXPPOFdkZKQ2btyoqqoqDRo0SHFxcZoyZYqCg4Pl5cVb3u+RzTAMw+wiAAAA6oJ4CgAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgA+B37bLLLtOUKVPMLgNAAyHIAGhwhYWFuvPOO9W2bVv5+fkpLCxMffv21eLFi3X8+HGzywNgYfw0KIAG9c0336hv374KDg7WnDlzFBcXJ7vdrh07duj555/XBRdcoP/+7/82u8zTqqqqks1m43d6AA/FKxNAg7r11lvVpEkT5eTkaNSoUerYsaPatGmjYcOG6d1339XQoUMlSSUlJbrhhhsUGhoqh8OhAQMGaPv27a55HnzwQV100UV6+eWXFR0draCgIF133XU6evSoa8yxY8c0duxYBQYGKiIiQvPmzTulHqfTqWnTpumCCy5QQECAevfurQ0bNrj6V6xYoeDgYP39739Xp06dZLfbVVBQ0HBPEIBzQpAB0GB+/PFHrV+/XikpKQoICKhxjM1mkySNHDlSxcXFeu+995Sbm6v4+HgNHDhQP/30k2vsvn379Oabb+qdd97RO++8o6ysLD366KOu/rvvvltZWVl66623tH79em3YsEFffPGF2/Fuu+02ZWdna9WqVfrnP/+pkSNHavDgwdqzZ49rzPHjx/XYY4/phRde0JdffqkWLVrU59MCoD4ZANBANm3aZEgy1qxZ49YeEhJiBAQEGAEBAcY999xjfPLJJ4bD4TAqKircxl144YXGkiVLDMMwjJkzZxpNmzY1ysrKXP1333230bt3b8MwDOPo0aOGr6+v8eqrr7r6f/zxR8Pf39+48847DcMwjG+//dbw9vY2vv/+e7fjDBw40EhLSzMMwzAyMjIMSca2bdvq50kA0KBYIwOg0X3++eeqrq7WmDFj5HQ6tX37dpWXlyskJMRt3M8//6x9+/a59qOjo9WsWTPXfkREhIqLiyX9erWmsrJSvXv3dvU3b95cHTp0cO3v2LFDVVVVat++vdtxnE6n27F9fX3VtWvX+jlZAA2KIAOgwbRt21Y2m015eXlu7W3atJEk+fv7S5LKy8sVERHhtlblpODgYNffPj4+bn02m03V1dW1rqe8vFze3t7Kzc2Vt7e3W19gYKDrb39/f9ctLwCejSADoMGEhIToiiuu0LPPPqvbb7/9tOtk4uPjVVhYqCZNmig6OrpOx7rwwgvl4+OjzZs3q1WrVpKkI0eO6Ouvv1b//v0lSd27d1dVVZWKi4t16aWX1uk4ADwLi30BNKhFixbpl19+Uc+ePfW3v/1Nu3fvVl5enl555RV99dVX8vb2VmJiohISEjR8+HCtX79e+/fv12effab7779fOTk5tTpOYGCgJk2apLvvvlsffvihdu7cqfHjx7t9bLp9+/YaM2aMxo4dqzVr1ig/P1+ff/650tPT9e677zbUUwCgAXFFBkCDuvDCC7V161bNmTNHaWlp+u6772S329WpUydNmzZNt956q2w2m/7xj3/o/vvv14QJE3T48GGFh4erX79+CgsLq/Wx5s6dq/Lycg0dOlTNmjXTXXfdpdLSUrcxGRkZevjhh3XXXXfp+++/1/nnn68+ffro6quvru9TB9AIbIZhGGYXAQAAUBfcWgIAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJb1fxMxOhdVDwQXAAAAAElFTkSuQmCC",
1017 |       "text/plain": [
1018 |        "<Figure size 640x480 with 1 Axes>"
1019 |       ]
1020 |      },
1021 |      "metadata": {},
1022 |      "output_type": "display_data"
1023 |     }
1024 |    ],
1025 |    "source": [
1026 |     "sns.countplot(data = df, x = 'Gender' , hue = 'treatment')"
1027 |    ]
1028 |   },
1029 |   {
1030 |    "cell_type": "markdown",
1031 |    "metadata": {},
1032 |    "source": [
1033 |     "### Distribution of depression by course name"
1034 |    ]
1035 |   },
1036 |   {
1037 |    "cell_type": "code",
1038 |    "execution_count": 85,
1039 |    "metadata": {},
1040 |    "outputs": [
1041 |     {
1042 |      "data": {
1043 |       "text/plain": [
1044 |        "<Axes: xlabel='count', ylabel='Course name'>"
1045 |       ]
1046 |      },
1047 |      "execution_count": 85,
1048 |      "metadata": {},
1049 |      "output_type": "execute_result"
1050 |     },
1051 |     {
1052 |      "data": {
1053 |       "image/png": "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",
1054 |       "text/plain": [
1055 |        "<Figure size 500x1000 with 1 Axes>"
1056 |       ]
1057 |      },
1058 |      "metadata": {},
1059 |      "output_type": "display_data"
1060 |     }
1061 |    ],
1062 |    "source": [
1063 |     "plt.figure(figsize=(5,10))\n",
1064 |     "sns.countplot(data = df, y = 'Course name', hue = 'Depression' )"
1065 |    ]
1066 |   },
1067 |   {
1068 |    "cell_type": "code",
1069 |    "execution_count": null,
1070 |    "metadata": {},
1071 |    "outputs": [],
1072 |    "source": []
1073 |   }
1074 |  ],
1075 |  "metadata": {
1076 |   "kernelspec": {
1077 |    "display_name": "Python 3",
1078 |    "language": "python",
1079 |    "name": "python3"
1080 |   },
1081 |   "language_info": {
1082 |    "codemirror_mode": {
1083 |     "name": "ipython",
1084 |     "version": 3
1085 |    },
1086 |    "file_extension": ".py",
1087 |    "mimetype": "text/x-python",
1088 |    "name": "python",
1089 |    "nbconvert_exporter": "python",
1090 |    "pygments_lexer": "ipython3",
1091 |    "version": "3.12.3"
1092 |   }
1093 |  },
1094 |  "nbformat": 4,
1095 |  "nbformat_minor": 2
1096 | }
1097 | 


--------------------------------------------------------------------------------