Student performance prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c6534c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1af4bfd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['assessments.csv',\n",
       " 'courses.csv',\n",
       " 'studentAssessment.csv',\n",
       " 'studentInfo.csv',\n",
       " 'studentRegistration.csv',\n",
       " 'studentVle.csv',\n",
       " 'vle.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder = 'Open University Dataset/'\n",
    "os.listdir(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f7a79c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = []\n",
    "for file in os.listdir(folder):\n",
    "    table.append(pd.read_csv(folder+file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8af57879",
   "metadata": {},
   "outputs": [],
   "source": [
    "student_assessment = table[2]\n",
    "assessment = table[0]\n",
    "student_info  = table[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "693feec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping 'assessment_submission_date' as it not required \n",
    "assessment = assessment.drop(['assessment_submission_date'],axis = 1)\n",
    "\n",
    "#Dropping 'is_banked' , 'date_submitted' as it is not required\n",
    "student_assessment = student_assessment.drop(['is_banked','date_submitted'],axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fe1dd93",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting internal assessments and final assessments\n",
    "internals = assessment[assessment['assessment_type'] != 'Exam']\n",
    "finals = assessment[assessment['assessment_type'] == 'Exam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b712741",
   "metadata": {},
   "outputs": [],
   "source": [
    "assessment_per_presentation = internals.groupby(['code_module','code_presentation']).agg(assessment_per_presentation = ('id_assessment','count')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8e91f88",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_assessment</th>\n",
       "      <th>assessment_type</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1757</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2014J</td>\n",
       "      <td>1763</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013B</td>\n",
       "      <td>14990</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>15002</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014B</td>\n",
       "      <td>15014</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014J</td>\n",
       "      <td>15025</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "      <td>24290</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "      <td>40087</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014J</td>\n",
       "      <td>24299</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014J</td>\n",
       "      <td>40088</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013B</td>\n",
       "      <td>25340</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013J</td>\n",
       "      <td>25354</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014B</td>\n",
       "      <td>25361</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "      <td>25368</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2013J</td>\n",
       "      <td>30713</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014B</td>\n",
       "      <td>30718</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30723</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013B</td>\n",
       "      <td>34872</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013J</td>\n",
       "      <td>34885</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014B</td>\n",
       "      <td>34898</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014J</td>\n",
       "      <td>34911</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2013J</td>\n",
       "      <td>37424</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014B</td>\n",
       "      <td>37434</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37444</td>\n",
       "      <td>Exam</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code_module code_presentation  id_assessment assessment_type  weight\n",
       "5           AAA             2013J           1757            Exam   100.0\n",
       "11          AAA             2014J           1763            Exam   100.0\n",
       "23          BBB             2013B          14990            Exam   100.0\n",
       "35          BBB             2013J          15002            Exam   100.0\n",
       "47          BBB             2014B          15014            Exam   100.0\n",
       "53          BBB             2014J          15025            Exam   100.0\n",
       "62          CCC             2014B          24290            Exam   100.0\n",
       "63          CCC             2014B          40087            Exam   100.0\n",
       "72          CCC             2014J          24299            Exam   100.0\n",
       "73          CCC             2014J          40088            Exam   100.0\n",
       "87          DDD             2013B          25340            Exam   100.0\n",
       "94          DDD             2013J          25354            Exam   100.0\n",
       "101         DDD             2014B          25361            Exam   100.0\n",
       "108         DDD             2014J          25368            Exam   100.0\n",
       "113         EEE             2013J          30713            Exam   100.0\n",
       "118         EEE             2014B          30718            Exam   100.0\n",
       "123         EEE             2014J          30723            Exam   100.0\n",
       "136         FFF             2013B          34872            Exam   100.0\n",
       "149         FFF             2013J          34885            Exam   100.0\n",
       "162         FFF             2014B          34898            Exam   100.0\n",
       "175         FFF             2014J          34911            Exam   100.0\n",
       "185         GGG             2013J          37424            Exam   100.0\n",
       "195         GGG             2014B          37434            Exam   100.0\n",
       "205         GGG             2014J          37444            Exam   100.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f299d669",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_assessment</th>\n",
       "      <th>assessment_type</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1753</td>\n",
       "      <td>TMA</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1754</td>\n",
       "      <td>TMA</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1755</td>\n",
       "      <td>TMA</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1756</td>\n",
       "      <td>TMA</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37442</td>\n",
       "      <td>CMA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37443</td>\n",
       "      <td>CMA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37435</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37436</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code_module code_presentation  id_assessment assessment_type  weight\n",
       "0           AAA             2013J           1752             TMA    10.0\n",
       "1           AAA             2013J           1753             TMA    20.0\n",
       "2           AAA             2013J           1754             TMA    20.0\n",
       "3           AAA             2013J           1755             TMA    20.0\n",
       "4           AAA             2013J           1756             TMA    30.0\n",
       "..          ...               ...            ...             ...     ...\n",
       "200         GGG             2014J          37442             CMA     0.0\n",
       "201         GGG             2014J          37443             CMA     0.0\n",
       "202         GGG             2014J          37435             TMA     0.0\n",
       "203         GGG             2014J          37436             TMA     0.0\n",
       "204         GGG             2014J          37437             TMA     0.0\n",
       "\n",
       "[182 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "301c07b7",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>assessment_per_presentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2014J</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013B</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014B</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014J</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014J</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013B</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013J</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2013J</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013B</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013J</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014B</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014J</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2013J</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014B</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   code_module code_presentation  assessment_per_presentation\n",
       "0          AAA             2013J                            5\n",
       "1          AAA             2014J                            5\n",
       "2          BBB             2013B                           11\n",
       "3          BBB             2013J                           11\n",
       "4          BBB             2014B                           11\n",
       "5          BBB             2014J                            5\n",
       "6          CCC             2014B                            8\n",
       "7          CCC             2014J                            8\n",
       "8          DDD             2013B                           13\n",
       "9          DDD             2013J                            6\n",
       "10         DDD             2014B                            6\n",
       "11         DDD             2014J                            6\n",
       "12         EEE             2013J                            4\n",
       "13         EEE             2014B                            4\n",
       "14         EEE             2014J                            4\n",
       "15         FFF             2013B                           12\n",
       "16         FFF             2013J                           12\n",
       "17         FFF             2014B                           12\n",
       "18         FFF             2014J                           12\n",
       "19         GGG             2013J                            9\n",
       "20         GGG             2014B                            9\n",
       "21         GGG             2014J                            9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assessment_per_presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8b18746",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_assessment</th>\n",
       "      <th>assessment_type</th>\n",
       "      <th>weight</th>\n",
       "      <th>id_student</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11391</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28400</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31604</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32885</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>TMA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38053</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168948</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>652462</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168949</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>652539</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168950</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653157</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168951</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653252</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168952</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37437</td>\n",
       "      <td>TMA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653374</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168953 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code_module code_presentation  id_assessment assessment_type  weight  \\\n",
       "0              AAA             2013J           1752             TMA    10.0   \n",
       "1              AAA             2013J           1752             TMA    10.0   \n",
       "2              AAA             2013J           1752             TMA    10.0   \n",
       "3              AAA             2013J           1752             TMA    10.0   \n",
       "4              AAA             2013J           1752             TMA    10.0   \n",
       "...            ...               ...            ...             ...     ...   \n",
       "168948         GGG             2014J          37437             TMA     0.0   \n",
       "168949         GGG             2014J          37437             TMA     0.0   \n",
       "168950         GGG             2014J          37437             TMA     0.0   \n",
       "168951         GGG             2014J          37437             TMA     0.0   \n",
       "168952         GGG             2014J          37437             TMA     0.0   \n",
       "\n",
       "        id_student  score  \n",
       "0            11391   78.0  \n",
       "1            28400   70.0  \n",
       "2            31604   72.0  \n",
       "3            32885   69.0  \n",
       "4            38053   79.0  \n",
       "...            ...    ...  \n",
       "168948      652462   60.0  \n",
       "168949      652539   75.0  \n",
       "168950      653157   70.0  \n",
       "168951      653252   70.0  \n",
       "168952      653374   50.0  \n",
       "\n",
       "[168953 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#misa is the merge of tables 'internals' and 'student_assessment'\n",
    "misa_internals = pd.merge(internals,student_assessment,on=['id_assessment'],how = 'inner')\n",
    "misa_internals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07c1c269",
   "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>id_student</th>\n",
       "      <th>score</th>\n",
       "      <th>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>558914</td>\n",
       "      <td>32.0</td>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>559706</td>\n",
       "      <td>78.0</td>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>559770</td>\n",
       "      <td>54.0</td>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>560114</td>\n",
       "      <td>64.0</td>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>560311</td>\n",
       "      <td>100.0</td>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4954</th>\n",
       "      <td>652680</td>\n",
       "      <td>91.0</td>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4955</th>\n",
       "      <td>652732</td>\n",
       "      <td>47.0</td>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4956</th>\n",
       "      <td>652965</td>\n",
       "      <td>87.0</td>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4957</th>\n",
       "      <td>653051</td>\n",
       "      <td>27.0</td>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4958</th>\n",
       "      <td>653319</td>\n",
       "      <td>64.0</td>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4959 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_student  score code_module code_presentation\n",
       "0         558914   32.0         CCC             2014B\n",
       "1         559706   78.0         CCC             2014B\n",
       "2         559770   54.0         CCC             2014B\n",
       "3         560114   64.0         CCC             2014B\n",
       "4         560311  100.0         CCC             2014B\n",
       "...          ...    ...         ...               ...\n",
       "4954      652680   91.0         DDD             2014J\n",
       "4955      652732   47.0         DDD             2014J\n",
       "4956      652965   87.0         DDD             2014J\n",
       "4957      653051   27.0         DDD             2014J\n",
       "4958      653319   64.0         DDD             2014J\n",
       "\n",
       "[4959 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misa_finals = pd.merge(student_assessment,finals,on=['id_assessment'],how='inner')\n",
    "misa_finals = misa_finals.drop(['id_assessment','assessment_type','weight'],axis=1)\n",
    "misa_finals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "398fe4ee",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>pass_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>82.4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>65.4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>38053</td>\n",
       "      <td>66.9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25834</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2620947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25835</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2645731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25836</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2648187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25837</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2679821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25838</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2684003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25839 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module code_presentation  id_student  weighted_grade  pass_rate\n",
       "0             AAA             2013J       11391            82.4   1.000000\n",
       "1             AAA             2013J       28400            65.4   1.000000\n",
       "2             AAA             2013J       31604            76.3   1.000000\n",
       "3             AAA             2013J       32885            55.0   0.600000\n",
       "4             AAA             2013J       38053            66.9   1.000000\n",
       "...           ...               ...         ...             ...        ...\n",
       "25834         GGG             2014J     2620947             0.0   1.000000\n",
       "25835         GGG             2014J     2645731             0.0   1.000000\n",
       "25836         GGG             2014J     2648187             0.0   1.000000\n",
       "25837         GGG             2014J     2679821             0.0   0.222222\n",
       "25838         GGG             2014J     2684003             0.0   0.777778\n",
       "\n",
       "[25839 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating 'weighted_grade' attribute to combine weights and their respective scores\n",
    "misa_internals['partial_weighted_grade'] = (misa_internals['weight']/100)*(misa_internals['score'])\n",
    "\n",
    "\n",
    "misa_internals['has_passed'] = (misa_internals['score']>40).map({True:1,False:0})\n",
    "\n",
    "\n",
    "misa_internals = misa_internals.groupby(['code_module','code_presentation','id_student']).agg(weighted_grade=('partial_weighted_grade',sum),\n",
    "                                                                          pass_count = ('has_passed',sum)).reset_index()\n",
    "\n",
    "\n",
    "\n",
    "misa_internals = pd.merge(misa_internals,assessment_per_presentation,on=['code_module','code_presentation'],how = 'inner')\n",
    "misa_internals['pass_rate'] = misa_internals['pass_count']/misa_internals['assessment_per_presentation']\n",
    "misa_internals =misa_internals.drop(['pass_count','assessment_per_presentation'],axis=1)\n",
    "misa_internals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc173405",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>pass_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>82.4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>65.4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>38053</td>\n",
       "      <td>66.9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25834</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2620947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25835</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2645731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25836</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2648187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25837</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2679821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25838</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2684003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25839 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module code_presentation  id_student  weighted_grade  pass_rate\n",
       "0             AAA             2013J       11391            82.4   1.000000\n",
       "1             AAA             2013J       28400            65.4   1.000000\n",
       "2             AAA             2013J       31604            76.3   1.000000\n",
       "3             AAA             2013J       32885            55.0   0.600000\n",
       "4             AAA             2013J       38053            66.9   1.000000\n",
       "...           ...               ...         ...             ...        ...\n",
       "25834         GGG             2014J     2620947             0.0   1.000000\n",
       "25835         GGG             2014J     2645731             0.0   1.000000\n",
       "25836         GGG             2014J     2648187             0.0   1.000000\n",
       "25837         GGG             2014J     2679821             0.0   0.222222\n",
       "25838         GGG             2014J     2684003             0.0   0.777778\n",
       "\n",
       "[25839 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# misa = pd.merge(misa_internals,misa_finals,on=['code_module','code_presentation','id_student'])\n",
    "# misa['exam_score'] = misa['score']\n",
    "# misa = misa.drop(['score'],axis = 1)\n",
    "misa = misa_internals\n",
    "misa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01f8012b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#student_info contains 'studentInfo.csv' \n",
    "#diregarding students who withdrew the course\n",
    "student_info = student_info[student_info['final_result']!='Withdrawn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e0aca1b",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>highest_education</th>\n",
       "      <th>imd_band</th>\n",
       "      <th>age_band</th>\n",
       "      <th>num_of_prev_attempts</th>\n",
       "      <th>studied_credits</th>\n",
       "      <th>disability</th>\n",
       "      <th>final_result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>M</td>\n",
       "      <td>East Anglian Region</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>90-100%</td>\n",
       "      <td>55&lt;=</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>F</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>20-30%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>F</td>\n",
       "      <td>South East Region</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>50-60%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>F</td>\n",
       "      <td>West Midlands Region</td>\n",
       "      <td>Lower Than A Level</td>\n",
       "      <td>50-60%</td>\n",
       "      <td>0-35</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>38053</td>\n",
       "      <td>M</td>\n",
       "      <td>Wales</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>80-90%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32587</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2620947</td>\n",
       "      <td>F</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>80-90%</td>\n",
       "      <td>0-35</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Y</td>\n",
       "      <td>Distinction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32588</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2640965</td>\n",
       "      <td>F</td>\n",
       "      <td>Wales</td>\n",
       "      <td>Lower Than A Level</td>\n",
       "      <td>10-20</td>\n",
       "      <td>0-35</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>N</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32589</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2645731</td>\n",
       "      <td>F</td>\n",
       "      <td>East Anglian Region</td>\n",
       "      <td>Lower Than A Level</td>\n",
       "      <td>40-50%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>N</td>\n",
       "      <td>Distinction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32590</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2648187</td>\n",
       "      <td>F</td>\n",
       "      <td>South Region</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>20-30%</td>\n",
       "      <td>0-35</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Y</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32592</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>2684003</td>\n",
       "      <td>F</td>\n",
       "      <td>Yorkshire Region</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>50-60%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>N</td>\n",
       "      <td>Distinction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22437 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module code_presentation  id_student gender                region  \\\n",
       "0             AAA             2013J       11391      M   East Anglian Region   \n",
       "1             AAA             2013J       28400      F              Scotland   \n",
       "3             AAA             2013J       31604      F     South East Region   \n",
       "4             AAA             2013J       32885      F  West Midlands Region   \n",
       "5             AAA             2013J       38053      M                 Wales   \n",
       "...           ...               ...         ...    ...                   ...   \n",
       "32587         GGG             2014J     2620947      F              Scotland   \n",
       "32588         GGG             2014J     2640965      F                 Wales   \n",
       "32589         GGG             2014J     2645731      F   East Anglian Region   \n",
       "32590         GGG             2014J     2648187      F          South Region   \n",
       "32592         GGG             2014J     2684003      F      Yorkshire Region   \n",
       "\n",
       "           highest_education imd_band age_band  num_of_prev_attempts  \\\n",
       "0           HE Qualification  90-100%     55<=                     0   \n",
       "1           HE Qualification   20-30%    35-55                     0   \n",
       "3      A Level or Equivalent   50-60%    35-55                     0   \n",
       "4         Lower Than A Level   50-60%     0-35                     0   \n",
       "5      A Level or Equivalent   80-90%    35-55                     0   \n",
       "...                      ...      ...      ...                   ...   \n",
       "32587  A Level or Equivalent   80-90%     0-35                     0   \n",
       "32588     Lower Than A Level    10-20     0-35                     0   \n",
       "32589     Lower Than A Level   40-50%    35-55                     0   \n",
       "32590  A Level or Equivalent   20-30%     0-35                     0   \n",
       "32592       HE Qualification   50-60%    35-55                     0   \n",
       "\n",
       "       studied_credits disability final_result  \n",
       "0                  240          N         Pass  \n",
       "1                   60          N         Pass  \n",
       "3                   60          N         Pass  \n",
       "4                   60          N         Pass  \n",
       "5                   60          N         Pass  \n",
       "...                ...        ...          ...  \n",
       "32587               30          Y  Distinction  \n",
       "32588               30          N         Fail  \n",
       "32589               30          N  Distinction  \n",
       "32590               30          Y         Pass  \n",
       "32592               30          N  Distinction  \n",
       "\n",
       "[22437 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc0876e8",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>4.765306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>3.337209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>30268</td>\n",
       "      <td>3.697368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>3.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>2.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4132</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2680344</td>\n",
       "      <td>2.802667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4133</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2680885</td>\n",
       "      <td>2.098039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4134</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2691100</td>\n",
       "      <td>2.706294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4135</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2691566</td>\n",
       "      <td>2.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2693772</td>\n",
       "      <td>1.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4137 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     code_module code_presentation  id_student  avg_click_per_site\n",
       "0            AAA             2013J       11391            4.765306\n",
       "1            AAA             2013J       28400            3.337209\n",
       "2            AAA             2013J       30268            3.697368\n",
       "3            AAA             2013J       31604            3.254902\n",
       "4            AAA             2013J       32885            2.937500\n",
       "...          ...               ...         ...                 ...\n",
       "4132         BBB             2013J     2680344            2.802667\n",
       "4133         BBB             2013J     2680885            2.098039\n",
       "4134         BBB             2013J     2691100            2.706294\n",
       "4135         BBB             2013J     2691566            2.588235\n",
       "4136         BBB             2013J     2693772            1.818182\n",
       "\n",
       "[4137 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#student_vle contains 'studentVle.csv'.\n",
    "student_vle = table[5]\n",
    "#Dropping 'student_interaction_date' as it is not required.\n",
    "student_vle = student_vle.drop(['student_interaction_date'],axis = 1)\n",
    "#Creating new attributes 'sites_visited' and 'tot_clicks' by adding their corresponding values.\n",
    "student_vle = student_vle.groupby(['code_module','code_presentation','id_student']).agg({'id_site':'count','sum_click':sum}).reset_index()\n",
    "student_vle.rename(columns = {'id_site':'sites_visited', 'sum_click':'tot_clicks'}, inplace = True)\n",
    "student_vle['avg_click_per_site'] = student_vle['tot_clicks']/ student_vle['sites_visited']\n",
    "student_vle = student_vle.drop(['sites_visited','tot_clicks'],axis=1)\n",
    "student_vle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9246e7c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3379 entries, 0 to 3378\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   code_module           3379 non-null   object \n",
      " 1   code_presentation     3379 non-null   object \n",
      " 2   id_student            3379 non-null   int64  \n",
      " 3   gender                3379 non-null   object \n",
      " 4   region                3379 non-null   object \n",
      " 5   highest_education     3379 non-null   object \n",
      " 6   imd_band              3329 non-null   object \n",
      " 7   age_band              3379 non-null   object \n",
      " 8   num_of_prev_attempts  3379 non-null   int64  \n",
      " 9   studied_credits       3379 non-null   int64  \n",
      " 10  disability            3379 non-null   object \n",
      " 11  final_result          3379 non-null   object \n",
      " 12  avg_click_per_site    3379 non-null   float64\n",
      "dtypes: float64(1), int64(3), object(9)\n",
      "memory usage: 369.6+ KB\n",
      "\n",
      "\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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>highest_education</th>\n",
       "      <th>imd_band</th>\n",
       "      <th>age_band</th>\n",
       "      <th>num_of_prev_attempts</th>\n",
       "      <th>studied_credits</th>\n",
       "      <th>disability</th>\n",
       "      <th>final_result</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>M</td>\n",
       "      <td>East Anglian Region</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>90-100%</td>\n",
       "      <td>55&lt;=</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "      <td>4.765306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>F</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>20-30%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "      <td>3.337209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>F</td>\n",
       "      <td>South East Region</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>50-60%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "      <td>3.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>F</td>\n",
       "      <td>West Midlands Region</td>\n",
       "      <td>Lower Than A Level</td>\n",
       "      <td>50-60%</td>\n",
       "      <td>0-35</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "      <td>2.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>38053</td>\n",
       "      <td>M</td>\n",
       "      <td>Wales</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>80-90%</td>\n",
       "      <td>35-55</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>Pass</td>\n",
       "      <td>3.381743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code_module code_presentation  id_student gender                region  \\\n",
       "0         AAA             2013J       11391      M   East Anglian Region   \n",
       "1         AAA             2013J       28400      F              Scotland   \n",
       "2         AAA             2013J       31604      F     South East Region   \n",
       "3         AAA             2013J       32885      F  West Midlands Region   \n",
       "4         AAA             2013J       38053      M                 Wales   \n",
       "\n",
       "       highest_education imd_band age_band  num_of_prev_attempts  \\\n",
       "0       HE Qualification  90-100%     55<=                     0   \n",
       "1       HE Qualification   20-30%    35-55                     0   \n",
       "2  A Level or Equivalent   50-60%    35-55                     0   \n",
       "3     Lower Than A Level   50-60%     0-35                     0   \n",
       "4  A Level or Equivalent   80-90%    35-55                     0   \n",
       "\n",
       "   studied_credits disability final_result  avg_click_per_site  \n",
       "0              240          N         Pass            4.765306  \n",
       "1               60          N         Pass            3.337209  \n",
       "2               60          N         Pass            3.254902  \n",
       "3               60          N         Pass            2.937500  \n",
       "4               60          N         Pass            3.381743  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sisv a merge of tables 'student_info' and 'student_vle'\n",
    "sisv = pd.merge(student_info,student_vle,on=['code_module','code_presentation','id_student'],how='inner')\n",
    "sisv.info()\n",
    "print('\\n')\n",
    "sisv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8885334",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3257 entries, 0 to 3256\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   code_module         3257 non-null   object \n",
      " 1   code_presentation   3257 non-null   object \n",
      " 2   avg_click_per_site  3257 non-null   float64\n",
      " 3   weighted_grade      3257 non-null   float64\n",
      " 4   final_result        3257 non-null   object \n",
      " 5   pass_rate           3257 non-null   float64\n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 178.1+ KB\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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>final_result</th>\n",
       "      <th>pass_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>4.765306</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>3.337209</td>\n",
       "      <td>65.4</td>\n",
       "      <td>Pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>3.254902</td>\n",
       "      <td>76.3</td>\n",
       "      <td>Pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>3.381743</td>\n",
       "      <td>66.9</td>\n",
       "      <td>Pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code_module code_presentation  avg_click_per_site  weighted_grade  \\\n",
       "0         AAA             2013J            4.765306            82.4   \n",
       "1         AAA             2013J            3.337209            65.4   \n",
       "2         AAA             2013J            3.254902            76.3   \n",
       "3         AAA             2013J            2.937500            55.0   \n",
       "4         AAA             2013J            3.381743            66.9   \n",
       "\n",
       "  final_result  pass_rate  \n",
       "0         Pass        1.0  \n",
       "1         Pass        1.0  \n",
       "2         Pass        1.0  \n",
       "3         Pass        0.6  \n",
       "4         Pass        1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging tables 'misa' and 'sisv' and selecting required attributes to make 'misa_sisv_woa'\n",
    "misa_sisv = pd.merge(sisv,misa,on=['code_module','code_presentation','id_student'])\n",
    "misa_sisv\n",
    "misa_sisv_woa = misa_sisv[['code_module','code_presentation','avg_click_per_site','weighted_grade'\n",
    "                           ,'final_result','pass_rate']]\n",
    "misa_sisv_woa.info()\n",
    "misa_sisv_woa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a2868568",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>final_result</th>\n",
       "      <th>pass_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.765306</td>\n",
       "      <td>82.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.337209</td>\n",
       "      <td>65.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.254902</td>\n",
       "      <td>76.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.381743</td>\n",
       "      <td>66.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3252</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3253</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.802667</td>\n",
       "      <td>78.28</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.098039</td>\n",
       "      <td>70.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.706294</td>\n",
       "      <td>62.23</td>\n",
       "      <td>2</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>64.19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3257 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module  code_presentation  avg_click_per_site  weighted_grade  \\\n",
       "0               0                  1            4.765306           82.40   \n",
       "1               0                  1            3.337209           65.40   \n",
       "2               0                  1            3.254902           76.30   \n",
       "3               0                  1            2.937500           55.00   \n",
       "4               0                  1            3.381743           66.90   \n",
       "...           ...                ...                 ...             ...   \n",
       "3252            1                  1            1.500000            2.30   \n",
       "3253            1                  1            2.802667           78.28   \n",
       "3254            1                  1            2.098039           70.31   \n",
       "3255            1                  1            2.706294           62.23   \n",
       "3256            1                  1            2.588235           64.19   \n",
       "\n",
       "      final_result  pass_rate  \n",
       "0                2   1.000000  \n",
       "1                2   1.000000  \n",
       "2                2   1.000000  \n",
       "3                2   0.600000  \n",
       "4                2   1.000000  \n",
       "...            ...        ...  \n",
       "3252             1   0.090909  \n",
       "3253             2   1.000000  \n",
       "3254             0   0.909091  \n",
       "3255             2   0.818182  \n",
       "3256             1   0.909091  \n",
       "\n",
       "[3257 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misa_sisv_woa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d12d03ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def jitter(values,j):\n",
    "    return values + np.random.normal(j,0.1,values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d1f3fc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Scatter(data,ft1,ft2,col,j):\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    x = jitter(data[ft1],j)\n",
    "    y = jitter(data[ft2],j)\n",
    "    plt.scatter(x, y, c=col, marker=\"o\")\n",
    "    plt.xlabel(ft1)\n",
    "    plt.ylabel(ft2)\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ccf4e9",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e676324",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "38df2d2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aadit\\AppData\\Local\\Temp\\ipykernel_6288\\499711599.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  misa_sisv_woa[col] = labelEncoder.fit_transform(misa_sisv_woa[col])\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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>final_result</th>\n",
       "      <th>pass_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.765306</td>\n",
       "      <td>82.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.337209</td>\n",
       "      <td>65.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.254902</td>\n",
       "      <td>76.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.381743</td>\n",
       "      <td>66.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3252</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3253</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.802667</td>\n",
       "      <td>78.28</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.098039</td>\n",
       "      <td>70.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.706294</td>\n",
       "      <td>62.23</td>\n",
       "      <td>2</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>64.19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3257 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module  code_presentation  avg_click_per_site  weighted_grade  \\\n",
       "0               0                  1            4.765306           82.40   \n",
       "1               0                  1            3.337209           65.40   \n",
       "2               0                  1            3.254902           76.30   \n",
       "3               0                  1            2.937500           55.00   \n",
       "4               0                  1            3.381743           66.90   \n",
       "...           ...                ...                 ...             ...   \n",
       "3252            1                  1            1.500000            2.30   \n",
       "3253            1                  1            2.802667           78.28   \n",
       "3254            1                  1            2.098039           70.31   \n",
       "3255            1                  1            2.706294           62.23   \n",
       "3256            1                  1            2.588235           64.19   \n",
       "\n",
       "      final_result  pass_rate  \n",
       "0                2   1.000000  \n",
       "1                2   1.000000  \n",
       "2                2   1.000000  \n",
       "3                2   0.600000  \n",
       "4                2   1.000000  \n",
       "...            ...        ...  \n",
       "3252             1   0.090909  \n",
       "3253             2   1.000000  \n",
       "3254             0   0.909091  \n",
       "3255             2   0.818182  \n",
       "3256             1   0.909091  \n",
       "\n",
       "[3257 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "labelEncoder = LabelEncoder()\n",
    "MinMax_Scaler = MinMaxScaler()\n",
    "#Label Encoding\n",
    "for col in misa_sisv_woa.columns.tolist():\n",
    "    if misa_sisv_woa[col].dtype == 'object':\n",
    "        misa_sisv_woa[col] = labelEncoder.fit_transform(misa_sisv_woa[col])\n",
    "        \n",
    "misa_sisv_woa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "116845b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(misa_sisv_woa.corr(), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4f9b94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "cycol = cycle('bgrcmk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "629e496e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pair plot for each 2 columns\n",
    "cols = list(misa_sisv_woa.columns)\n",
    "for i in range(len(cols)):\n",
    "    for j in range(i+1,len(cols)):\n",
    "        Scatter(misa_sisv_woa,cols[i],cols[j],col =next(cycol),j = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1d3f6952",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_table = dict(zip(labelEncoder.inverse_transform(misa_sisv_woa['final_result']),misa_sisv_woa['final_result'])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1665ff34",
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>avg_click_per_site</th>\n",
       "      <th>weighted_grade</th>\n",
       "      <th>final_result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.204330</td>\n",
       "      <td>0.839702</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.126832</td>\n",
       "      <td>0.666463</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.122366</td>\n",
       "      <td>0.777540</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.105142</td>\n",
       "      <td>0.560481</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.129249</td>\n",
       "      <td>0.681749</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3252</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.027133</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3253</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.097825</td>\n",
       "      <td>0.797717</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.059587</td>\n",
       "      <td>0.716499</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.092595</td>\n",
       "      <td>0.634159</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.086188</td>\n",
       "      <td>0.654132</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3257 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code_module  code_presentation  avg_click_per_site  weighted_grade  \\\n",
       "0             0.0                0.5            0.204330        0.839702   \n",
       "1             0.0                0.5            0.126832        0.666463   \n",
       "2             0.0                0.5            0.122366        0.777540   \n",
       "3             0.0                0.5            0.105142        0.560481   \n",
       "4             0.0                0.5            0.129249        0.681749   \n",
       "...           ...                ...                 ...             ...   \n",
       "3252          1.0                0.5            0.027133        0.023438   \n",
       "3253          1.0                0.5            0.097825        0.797717   \n",
       "3254          1.0                0.5            0.059587        0.716499   \n",
       "3255          1.0                0.5            0.092595        0.634159   \n",
       "3256          1.0                0.5            0.086188        0.654132   \n",
       "\n",
       "      final_result  \n",
       "0              1.0  \n",
       "1              1.0  \n",
       "2              1.0  \n",
       "3              1.0  \n",
       "4              1.0  \n",
       "...            ...  \n",
       "3252           0.5  \n",
       "3253           1.0  \n",
       "3254           0.0  \n",
       "3255           1.0  \n",
       "3256           0.5  \n",
       "\n",
       "[3257 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalisation\n",
    "misa_sisv_woa_norm = MinMax_Scaler.fit_transform(misa_sisv_woa[['code_module','code_presentation','avg_click_per_site','weighted_grade','final_result']])\n",
    "misa_sisv_woa_norm = pd.DataFrame(misa_sisv_woa_norm,columns = ['code_module','code_presentation','avg_click_per_site','weighted_grade','final_result'])\n",
    "misa_sisv_woa_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed66cd5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting TRAIN and TEST data\n",
    "X = misa_sisv_woa_norm.drop(['final_result'],axis=1)\n",
    "y = misa_sisv_woa[['final_result']]\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size =0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f35264d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_CFM_HeatMap(y_test,y_pred,tabel):\n",
    "    print(tabel)\n",
    "    sb.heatmap(confusion_matrix(y_test,y_pred), annot=True)\n",
    "    plt.ylabel('y true')\n",
    "    plt.xlabel('y pred')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "500313b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_table_binary = {'Pass':1,'Fail':0}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb054a3e",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5e2ac4",
   "metadata": {},
   "source": [
    "## Pass or Fail  prediction ( Binary Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b349662f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.551535151715473"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "StudentPredictionModel_lr = LR.fit(X_train, y_train['final_result'].map({2: 1, 1:0, 0 : 1}))\n",
    "StudentPredictionModel_lr.score(X_test,y_test['final_result'].map({2: 1, 1:0, 0 : 1}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83968f0",
   "metadata": {},
   "source": [
    "## Pass or Fail or Distinction prediction ( Multi Class Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94e42d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016011010142234272"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "StudentPredictionModel_lr = LR.fit(X_train, y_train)\n",
    "StudentPredictionModel_lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414660c7",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4eb831",
   "metadata": {},
   "source": [
    "## Pass or Fail or Distinction prediction ( Multi Class Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "edfa09a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7832310838445807"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LOR = LogisticRegression(max_iter=10000,multi_class='multinomial', solver='lbfgs')\n",
    "StudentPredictionModel_lor = LOR.fit(X_train,y_train.values.ravel())\n",
    "StudentPredictionModel_lor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "59195b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.11      0.19       113\n",
      "           1       0.90      0.67      0.77       267\n",
      "           2       0.75      0.96      0.84       598\n",
      "\n",
      "    accuracy                           0.78       978\n",
      "   macro avg       0.82      0.58      0.60       978\n",
      "weighted avg       0.80      0.78      0.75       978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = StudentPredictionModel_lor.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8384ed7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Pass': 2, 'Fail': 1, 'Distinction': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_CFM_HeatMap(y_test,y_pred,classification_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ea451c",
   "metadata": {},
   "source": [
    "## Pass or Fail prediction ( Binary Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "890a74d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8895705521472392"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LOR = LogisticRegression(max_iter=10000)\n",
    "StudentPredictionModel_lor = LOR.fit(X_train,y_train['final_result'].map({2: 1, 1:0, 0 : 1}).values.ravel())\n",
    "StudentPredictionModel_lor.score(X_test,y_test['final_result'].map({2: 1, 1:0, 0 : 1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31967290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.67      0.77       267\n",
      "           1       0.89      0.97      0.93       711\n",
      "\n",
      "    accuracy                           0.89       978\n",
      "   macro avg       0.89      0.82      0.85       978\n",
      "weighted avg       0.89      0.89      0.88       978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = StudentPredictionModel_lor.predict(X_test)\n",
    "print(classification_report(y_test['final_result'].map({2: 1, 1:0, 0 : 1}),y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ff5a1e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Pass': 1, 'Fail': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_CFM_HeatMap(y_test['final_result'].map({2: 1, 1:0, 0 : 1}),y_pred,classification_table_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86133465",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef8d48b",
   "metadata": {},
   "source": [
    "## Pass or Fail Or Distinction Prediction ( Multi Class Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "76f8ffa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7709611451942741"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(n_estimators=1000,max_depth=10)\n",
    "StudentPredictionModel_RFC = RFC.fit(X_train,y_train.values.ravel())\n",
    "StudentPredictionModel_RFC.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "094b2473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.35      0.39       113\n",
      "           1       0.91      0.68      0.78       267\n",
      "           2       0.77      0.89      0.83       598\n",
      "\n",
      "    accuracy                           0.77       978\n",
      "   macro avg       0.71      0.64      0.67       978\n",
      "weighted avg       0.77      0.77      0.76       978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = StudentPredictionModel_RFC.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0484fab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Pass': 2, 'Fail': 1, 'Distinction': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_CFM_HeatMap(y_test,y_pred,classification_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7c498a",
   "metadata": {},
   "source": [
    "## Pass or Fail Prediction ( Binary Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0110f79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8905930470347648"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(n_estimators=2000,max_depth=10)\n",
    "StudentPredictionModel_RFC = RFC.fit(X_train,y_train['final_result'].map({2: 1, 1:0, 0 : 1}).values.ravel())\n",
    "StudentPredictionModel_RFC.score(X_test,y_test['final_result'].map({2: 1, 1:0, 0 : 1}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d739c641",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.67      0.77       267\n",
      "           1       0.89      0.97      0.93       711\n",
      "\n",
      "    accuracy                           0.89       978\n",
      "   macro avg       0.90      0.82      0.85       978\n",
      "weighted avg       0.89      0.89      0.89       978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = StudentPredictionModel_RFC.predict(X_test)\n",
    "print(classification_report(y_test['final_result'].map({2: 1, 1:0, 0 : 1}),y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6f678793",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Pass': 1, 'Fail': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_CFM_HeatMap(y_test['final_result'].map({2: 1, 1:0, 0 : 1}),y_pred,classification_table_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f64f63",
   "metadata": {},
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "09b54860",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import Input\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import KFold\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from keras.utils.vis_utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49d8b2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_History(history,epoch):\n",
    "    loss = history.history['loss']\n",
    "    loss_val = history.history['val_loss']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "    acc = history.history['accuracy']\n",
    "    epochs = range(1,epoch+1)\n",
    "    plt.plot(epochs, loss, c ='g', label='Loss')\n",
    "    plt.plot(epochs, loss_val, c = 'b', label='Validation loss')\n",
    "    plt.plot(epochs, val_acc, c = 'orange', label='Validation accuracy')\n",
    "    plt.plot(epochs, acc, c = 'red', label='Accuracy')\n",
    "    plt.title('Training and Validation loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "794710b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining early stop parameters\n",
    "early_stop=EarlyStopping(monitor=\"val_loss\", mode=\"min\", verbose=1, patience=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37ceca0",
   "metadata": {},
   "source": [
    "## Pass or Fail Prediction ( Binary Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "579c7fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Pass': 2, 'Fail': 1, 'Distinction': 0}\n"
     ]
    }
   ],
   "source": [
    "print(classification_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d2a9024c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pass and Distinction goes to Pass (1) Class\n",
    "# Fail and Withdrawn goes to Fail (0) Class  \n",
    "new_y_test = y_test['final_result'].map({2: 1, 1:0, 0 : 1})\n",
    "new_y_train = y_train['final_result'].map({2: 1, 1:0, 0 : 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "596cb15c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "228/228 [==============================] - 1s 1ms/step - loss: 0.5271 - accuracy: 0.7429 - val_loss: 0.4998 - val_accuracy: 0.7270\n",
      "Epoch 2/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.4074 - accuracy: 0.8109 - val_loss: 0.3552 - val_accuracy: 0.8701\n",
      "Epoch 3/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.3050 - accuracy: 0.8991 - val_loss: 0.2984 - val_accuracy: 0.8865\n",
      "Epoch 4/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2700 - accuracy: 0.9043 - val_loss: 0.2905 - val_accuracy: 0.8814\n",
      "Epoch 5/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2663 - accuracy: 0.9057 - val_loss: 0.2792 - val_accuracy: 0.8906\n",
      "Epoch 6/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2624 - accuracy: 0.9026 - val_loss: 0.2776 - val_accuracy: 0.8916\n",
      "Epoch 7/2000\n",
      "228/228 [==============================] - 0s 822us/step - loss: 0.2603 - accuracy: 0.9030 - val_loss: 0.2770 - val_accuracy: 0.8916\n",
      "Epoch 8/2000\n",
      "228/228 [==============================] - 0s 823us/step - loss: 0.2581 - accuracy: 0.9026 - val_loss: 0.2752 - val_accuracy: 0.8896\n",
      "Epoch 9/2000\n",
      "228/228 [==============================] - 0s 803us/step - loss: 0.2570 - accuracy: 0.9061 - val_loss: 0.2765 - val_accuracy: 0.8896\n",
      "Epoch 10/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2568 - accuracy: 0.9092 - val_loss: 0.2783 - val_accuracy: 0.8885\n",
      "Epoch 11/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2571 - accuracy: 0.9043 - val_loss: 0.2767 - val_accuracy: 0.8906\n",
      "Epoch 12/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2569 - accuracy: 0.9052 - val_loss: 0.2807 - val_accuracy: 0.8947\n",
      "Epoch 13/2000\n",
      "228/228 [==============================] - 0s 834us/step - loss: 0.2559 - accuracy: 0.9057 - val_loss: 0.2769 - val_accuracy: 0.8916\n",
      "Epoch 14/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2558 - accuracy: 0.9096 - val_loss: 0.2734 - val_accuracy: 0.8947\n",
      "Epoch 15/2000\n",
      "228/228 [==============================] - 0s 842us/step - loss: 0.2558 - accuracy: 0.9079 - val_loss: 0.2727 - val_accuracy: 0.8947\n",
      "Epoch 16/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2563 - accuracy: 0.9057 - val_loss: 0.2758 - val_accuracy: 0.8937\n",
      "Epoch 17/2000\n",
      "228/228 [==============================] - 0s 810us/step - loss: 0.2554 - accuracy: 0.9061 - val_loss: 0.2788 - val_accuracy: 0.8947\n",
      "Epoch 18/2000\n",
      "228/228 [==============================] - 0s 824us/step - loss: 0.2561 - accuracy: 0.9043 - val_loss: 0.2759 - val_accuracy: 0.8855\n",
      "Epoch 19/2000\n",
      "228/228 [==============================] - 0s 828us/step - loss: 0.2549 - accuracy: 0.9074 - val_loss: 0.2733 - val_accuracy: 0.8906\n",
      "Epoch 20/2000\n",
      "228/228 [==============================] - 0s 819us/step - loss: 0.2553 - accuracy: 0.9070 - val_loss: 0.2743 - val_accuracy: 0.8906\n",
      "Epoch 21/2000\n",
      "228/228 [==============================] - 0s 803us/step - loss: 0.2551 - accuracy: 0.9030 - val_loss: 0.2746 - val_accuracy: 0.8937\n",
      "Epoch 22/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2537 - accuracy: 0.9092 - val_loss: 0.2752 - val_accuracy: 0.8947\n",
      "Epoch 23/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2532 - accuracy: 0.9070 - val_loss: 0.2721 - val_accuracy: 0.8937\n",
      "Epoch 24/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2544 - accuracy: 0.9074 - val_loss: 0.2721 - val_accuracy: 0.8947\n",
      "Epoch 25/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2552 - accuracy: 0.9070 - val_loss: 0.2843 - val_accuracy: 0.8937\n",
      "Epoch 26/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2533 - accuracy: 0.9083 - val_loss: 0.2721 - val_accuracy: 0.8947\n",
      "Epoch 27/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2548 - accuracy: 0.9061 - val_loss: 0.2790 - val_accuracy: 0.8957\n",
      "Epoch 28/2000\n",
      "228/228 [==============================] - 0s 842us/step - loss: 0.2547 - accuracy: 0.9065 - val_loss: 0.2735 - val_accuracy: 0.8937\n",
      "Epoch 29/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2550 - accuracy: 0.9057 - val_loss: 0.2717 - val_accuracy: 0.8937\n",
      "Epoch 30/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2532 - accuracy: 0.9092 - val_loss: 0.2763 - val_accuracy: 0.8947\n",
      "Epoch 31/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2534 - accuracy: 0.9070 - val_loss: 0.2717 - val_accuracy: 0.8947\n",
      "Epoch 32/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2538 - accuracy: 0.9079 - val_loss: 0.2817 - val_accuracy: 0.8957\n",
      "Epoch 33/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2531 - accuracy: 0.9061 - val_loss: 0.2721 - val_accuracy: 0.8947\n",
      "Epoch 34/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2533 - accuracy: 0.9070 - val_loss: 0.2720 - val_accuracy: 0.8937\n",
      "Epoch 35/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2547 - accuracy: 0.9048 - val_loss: 0.2820 - val_accuracy: 0.8957\n",
      "Epoch 36/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2543 - accuracy: 0.9061 - val_loss: 0.2715 - val_accuracy: 0.8926\n",
      "Epoch 37/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2526 - accuracy: 0.9061 - val_loss: 0.2717 - val_accuracy: 0.8926\n",
      "Epoch 38/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2529 - accuracy: 0.9083 - val_loss: 0.2762 - val_accuracy: 0.8957\n",
      "Epoch 39/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2529 - accuracy: 0.9079 - val_loss: 0.2716 - val_accuracy: 0.8916\n",
      "Epoch 40/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2516 - accuracy: 0.9096 - val_loss: 0.2751 - val_accuracy: 0.8896\n",
      "Epoch 41/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2528 - accuracy: 0.9074 - val_loss: 0.2719 - val_accuracy: 0.8926\n",
      "Epoch 42/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2521 - accuracy: 0.9122 - val_loss: 0.2725 - val_accuracy: 0.8926\n",
      "Epoch 43/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2532 - accuracy: 0.9083 - val_loss: 0.2724 - val_accuracy: 0.8957\n",
      "Epoch 44/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2519 - accuracy: 0.9100 - val_loss: 0.2725 - val_accuracy: 0.8916\n",
      "Epoch 45/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2518 - accuracy: 0.9087 - val_loss: 0.2723 - val_accuracy: 0.8947\n",
      "Epoch 46/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2528 - accuracy: 0.9070 - val_loss: 0.2783 - val_accuracy: 0.8967\n",
      "Epoch 47/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2533 - accuracy: 0.9100 - val_loss: 0.2753 - val_accuracy: 0.8957\n",
      "Epoch 48/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2523 - accuracy: 0.9083 - val_loss: 0.2905 - val_accuracy: 0.8937\n",
      "Epoch 49/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2525 - accuracy: 0.9083 - val_loss: 0.2755 - val_accuracy: 0.8906\n",
      "Epoch 50/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2534 - accuracy: 0.9074 - val_loss: 0.2719 - val_accuracy: 0.8916\n",
      "Epoch 51/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2501 - accuracy: 0.9083 - val_loss: 0.2719 - val_accuracy: 0.8926\n",
      "Epoch 52/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2520 - accuracy: 0.9070 - val_loss: 0.2714 - val_accuracy: 0.8916\n",
      "Epoch 53/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2521 - accuracy: 0.9092 - val_loss: 0.2709 - val_accuracy: 0.8916\n",
      "Epoch 54/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2506 - accuracy: 0.9109 - val_loss: 0.2721 - val_accuracy: 0.8947\n",
      "Epoch 55/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2518 - accuracy: 0.9105 - val_loss: 0.2714 - val_accuracy: 0.8937\n",
      "Epoch 56/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2518 - accuracy: 0.9070 - val_loss: 0.2731 - val_accuracy: 0.8957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2516 - accuracy: 0.9065 - val_loss: 0.2715 - val_accuracy: 0.8937\n",
      "Epoch 58/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2499 - accuracy: 0.9114 - val_loss: 0.2720 - val_accuracy: 0.8967\n",
      "Epoch 59/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2516 - accuracy: 0.9100 - val_loss: 0.2720 - val_accuracy: 0.8926\n",
      "Epoch 60/2000\n",
      "228/228 [==============================] - 0s 822us/step - loss: 0.2515 - accuracy: 0.9109 - val_loss: 0.2715 - val_accuracy: 0.8906\n",
      "Epoch 61/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2503 - accuracy: 0.9105 - val_loss: 0.2799 - val_accuracy: 0.8885\n",
      "Epoch 62/2000\n",
      "228/228 [==============================] - 0s 834us/step - loss: 0.2512 - accuracy: 0.9105 - val_loss: 0.2735 - val_accuracy: 0.8947\n",
      "Epoch 63/2000\n",
      "228/228 [==============================] - 0s 897us/step - loss: 0.2520 - accuracy: 0.9074 - val_loss: 0.2722 - val_accuracy: 0.8926\n",
      "Epoch 64/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2501 - accuracy: 0.9096 - val_loss: 0.2733 - val_accuracy: 0.8947\n",
      "Epoch 65/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2513 - accuracy: 0.9087 - val_loss: 0.2714 - val_accuracy: 0.8957\n",
      "Epoch 66/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2497 - accuracy: 0.9092 - val_loss: 0.2723 - val_accuracy: 0.8937\n",
      "Epoch 67/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2521 - accuracy: 0.9074 - val_loss: 0.2711 - val_accuracy: 0.8937\n",
      "Epoch 68/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2503 - accuracy: 0.9079 - val_loss: 0.2741 - val_accuracy: 0.8947\n",
      "Epoch 69/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2505 - accuracy: 0.9100 - val_loss: 0.2712 - val_accuracy: 0.8937\n",
      "Epoch 70/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2507 - accuracy: 0.9070 - val_loss: 0.2729 - val_accuracy: 0.8967\n",
      "Epoch 71/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2509 - accuracy: 0.9092 - val_loss: 0.2708 - val_accuracy: 0.8926\n",
      "Epoch 72/2000\n",
      "228/228 [==============================] - 0s 819us/step - loss: 0.2500 - accuracy: 0.9092 - val_loss: 0.2709 - val_accuracy: 0.8926\n",
      "Epoch 73/2000\n",
      "228/228 [==============================] - 0s 817us/step - loss: 0.2507 - accuracy: 0.9079 - val_loss: 0.2716 - val_accuracy: 0.8957\n",
      "Epoch 74/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2510 - accuracy: 0.9109 - val_loss: 0.2727 - val_accuracy: 0.8957\n",
      "Epoch 75/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2493 - accuracy: 0.9092 - val_loss: 0.2729 - val_accuracy: 0.8937\n",
      "Epoch 76/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2499 - accuracy: 0.9100 - val_loss: 0.2831 - val_accuracy: 0.8937\n",
      "Epoch 77/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2515 - accuracy: 0.9100 - val_loss: 0.2716 - val_accuracy: 0.8957\n",
      "Epoch 78/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2498 - accuracy: 0.9105 - val_loss: 0.2710 - val_accuracy: 0.8947\n",
      "Epoch 79/2000\n",
      "228/228 [==============================] - 0s 838us/step - loss: 0.2522 - accuracy: 0.9083 - val_loss: 0.2756 - val_accuracy: 0.8967\n",
      "Epoch 80/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2526 - accuracy: 0.9048 - val_loss: 0.2785 - val_accuracy: 0.8957\n",
      "Epoch 81/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2523 - accuracy: 0.9070 - val_loss: 0.2733 - val_accuracy: 0.8978\n",
      "Epoch 82/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2499 - accuracy: 0.9100 - val_loss: 0.2749 - val_accuracy: 0.8978\n",
      "Epoch 83/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2499 - accuracy: 0.9100 - val_loss: 0.2706 - val_accuracy: 0.8947\n",
      "Epoch 84/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2507 - accuracy: 0.9092 - val_loss: 0.2713 - val_accuracy: 0.8947\n",
      "Epoch 85/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2513 - accuracy: 0.9074 - val_loss: 0.2716 - val_accuracy: 0.8957\n",
      "Epoch 86/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2512 - accuracy: 0.9070 - val_loss: 0.2716 - val_accuracy: 0.8947\n",
      "Epoch 87/2000\n",
      "228/228 [==============================] - 0s 838us/step - loss: 0.2504 - accuracy: 0.9105 - val_loss: 0.2717 - val_accuracy: 0.8937\n",
      "Epoch 88/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2495 - accuracy: 0.9136 - val_loss: 0.2722 - val_accuracy: 0.8957\n",
      "Epoch 89/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2495 - accuracy: 0.9100 - val_loss: 0.2740 - val_accuracy: 0.8967\n",
      "Epoch 90/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2501 - accuracy: 0.9105 - val_loss: 0.2739 - val_accuracy: 0.8967\n",
      "Epoch 91/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2503 - accuracy: 0.9114 - val_loss: 0.2725 - val_accuracy: 0.8967\n",
      "Epoch 92/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2506 - accuracy: 0.9100 - val_loss: 0.2705 - val_accuracy: 0.8947\n",
      "Epoch 93/2000\n",
      "228/228 [==============================] - 0s 842us/step - loss: 0.2489 - accuracy: 0.9114 - val_loss: 0.2798 - val_accuracy: 0.8926\n",
      "Epoch 94/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2507 - accuracy: 0.9114 - val_loss: 0.2764 - val_accuracy: 0.8967\n",
      "Epoch 95/2000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.2501 - accuracy: 0.9105 - val_loss: 0.2852 - val_accuracy: 0.8947\n",
      "Epoch 96/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2518 - accuracy: 0.9065 - val_loss: 0.2698 - val_accuracy: 0.8947\n",
      "Epoch 97/2000\n",
      "228/228 [==============================] - 0s 855us/step - loss: 0.2498 - accuracy: 0.9096 - val_loss: 0.2728 - val_accuracy: 0.8947\n",
      "Epoch 98/2000\n",
      "228/228 [==============================] - 0s 855us/step - loss: 0.2510 - accuracy: 0.9096 - val_loss: 0.2716 - val_accuracy: 0.8937\n",
      "Epoch 99/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2495 - accuracy: 0.9087 - val_loss: 0.2701 - val_accuracy: 0.8947\n",
      "Epoch 100/2000\n",
      "228/228 [==============================] - 0s 847us/step - loss: 0.2490 - accuracy: 0.9105 - val_loss: 0.2713 - val_accuracy: 0.8937\n",
      "Epoch 101/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2495 - accuracy: 0.9122 - val_loss: 0.2774 - val_accuracy: 0.8957\n",
      "Epoch 102/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2493 - accuracy: 0.9105 - val_loss: 0.2703 - val_accuracy: 0.8967\n",
      "Epoch 103/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2501 - accuracy: 0.9074 - val_loss: 0.2723 - val_accuracy: 0.8967\n",
      "Epoch 104/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2508 - accuracy: 0.9070 - val_loss: 0.2708 - val_accuracy: 0.8916\n",
      "Epoch 105/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2489 - accuracy: 0.9074 - val_loss: 0.2757 - val_accuracy: 0.8978\n",
      "Epoch 106/2000\n",
      "228/228 [==============================] - 0s 825us/step - loss: 0.2499 - accuracy: 0.9083 - val_loss: 0.2736 - val_accuracy: 0.8978\n",
      "Epoch 107/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2497 - accuracy: 0.9109 - val_loss: 0.2800 - val_accuracy: 0.8896\n",
      "Epoch 108/2000\n",
      "228/228 [==============================] - 0s 864us/step - loss: 0.2499 - accuracy: 0.9100 - val_loss: 0.2717 - val_accuracy: 0.8947\n",
      "Epoch 109/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2497 - accuracy: 0.9114 - val_loss: 0.2703 - val_accuracy: 0.8957\n",
      "Epoch 110/2000\n",
      "228/228 [==============================] - 0s 833us/step - loss: 0.2496 - accuracy: 0.9100 - val_loss: 0.2723 - val_accuracy: 0.8957\n",
      "Epoch 111/2000\n",
      "228/228 [==============================] - 0s 831us/step - loss: 0.2504 - accuracy: 0.9083 - val_loss: 0.2706 - val_accuracy: 0.8957\n",
      "Epoch 112/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "228/228 [==============================] - 0s 803us/step - loss: 0.2513 - accuracy: 0.9074 - val_loss: 0.2831 - val_accuracy: 0.8906\n",
      "Epoch 113/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2509 - accuracy: 0.9092 - val_loss: 0.2719 - val_accuracy: 0.8967\n",
      "Epoch 114/2000\n",
      "228/228 [==============================] - 0s 813us/step - loss: 0.2518 - accuracy: 0.9096 - val_loss: 0.2707 - val_accuracy: 0.8926\n",
      "Epoch 115/2000\n",
      "228/228 [==============================] - 0s 816us/step - loss: 0.2493 - accuracy: 0.9087 - val_loss: 0.2715 - val_accuracy: 0.8978\n",
      "Epoch 116/2000\n",
      "228/228 [==============================] - 0s 829us/step - loss: 0.2491 - accuracy: 0.9079 - val_loss: 0.2722 - val_accuracy: 0.8978\n",
      "Epoch 117/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2505 - accuracy: 0.9100 - val_loss: 0.2743 - val_accuracy: 0.8988\n",
      "Epoch 118/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2505 - accuracy: 0.9100 - val_loss: 0.2716 - val_accuracy: 0.8916\n",
      "Epoch 119/2000\n",
      "228/228 [==============================] - 0s 811us/step - loss: 0.2502 - accuracy: 0.9052 - val_loss: 0.2726 - val_accuracy: 0.8978\n",
      "Epoch 120/2000\n",
      "228/228 [==============================] - 0s 819us/step - loss: 0.2471 - accuracy: 0.9100 - val_loss: 0.2915 - val_accuracy: 0.8916\n",
      "Epoch 121/2000\n",
      "228/228 [==============================] - 0s 820us/step - loss: 0.2510 - accuracy: 0.9105 - val_loss: 0.2716 - val_accuracy: 0.8916\n",
      "Epoch 121: early stopping\n"
     ]
    }
   ],
   "source": [
    "#Artificial neural network ,defining its layers\n",
    "ANN = Sequential()\n",
    "ANN.add(Dense(10, activation = 'relu', input_dim = 4 )  )\n",
    "ANN.add(Dense(7, activation = 'relu'))\n",
    "ANN.add(Dense(4, activation = 'relu'))\n",
    "ANN.add(Dense(1, activation = 'sigmoid'))\n",
    "ANN.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "history1 = ANN.fit(X_train, new_y_train, batch_size = 10, epochs = 2000,callbacks=[early_stop],validation_data=(X_test,new_y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "72559ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAN8AAAHBCAYAAADpUm5zAAAABmJLR0QA/wD/AP+gvaeTAAAfCElEQVR4nO3dT2gc5/kH8O9YUtKUxE5aorTBzR9wleJABCnFsUuc2FFDKL8RtLYsy5ZkCnYY3xwQoYc1Dvi6cnwoVSL5UnxY7cpQ2KX0ZJfokO2hpmtKCfLBZBxdZpzS3dIcGtl5fodkJrP/pNlZaZ4Z7fcDC7szs+888+58d2ZeaXcNEREQUdyWdmhXQNSrGD4iJQwfkRKGj0hJf+OEcrmMS5cuadRCtG0tLS01TWs68n322We4du1aLAXR5rl27RpWV1e1y6AGq6urbfPUdOTztEoqJZdhGHjnnXdw7Ngx7VIooFAoYHx8vOU8XvMRKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9IyZaFz3VdLC4uYnR0dKtWsenOnz+P8+fPa5dBPaLt5/m6deHCBXzwwQdb1fy2VKvV8Pjjj2Orv83RMIyW0+P+FsnG7U1KXXHZsiPf3NzcVjW9ZS5evIiLFy+qrX95eTmW9YgIqtWq/7harars4I3bKyJwHMd/rFVXXHjNlxC1Wg0LCwuxrW/Xrl0t78el3fYODg769zXqitOmha9Wq2FxcRGGYWB0dBS3b99uWsZ1XczOzvrL3Lhxw58evD4slUr+Mnfv3q1rw3v+wsICXNetO1Vp134Yra5Rw9Tlui5KpZK/zMLCAgzDwNmzZ/0+MAzDv3kap2WzWZRKpbp5cUvb9noB9p5//vz5un3Au83OzvrPCc4LblO7/dLb1lqthrNnz27umIA0yOfz0mLyhkzTFMuypFqtiohILpcTAH5bjuOIaZqSy+VEROT69esCQCqVipim6S9bLpdFRMS2bQEglmX568hms2LbtoiIVKtVyWQyodoPW3+w3sZp7ery5geXqVarYlmWAJCVlRVxHKepba+d4LTGx50AIPl8vuPnJHF7w/aDt07HcZrqLJfLTftPcDsdxxGRzvbLSqXSsr31rJOnwqaEr1gs+h3vqVardZ3ohTEIgGQyGf9+q/mNL5bXaSLiv8hh2g8jTA3t6mpcplKpCADJZrNdtdNJ7d2Gr5s6N3N7w/ZDJpOpC0Pj87LZrADw37C9Or2giYTfL72DSqe2PHzeO1CjYGcE30Uab43Ltnp+cD25XK6pMzZqP4zN3Bkbp2/38DVOjyN8Htu2/aAFn+e9IczPz/vTgmdPItH2y05sefiivhgbtdE4bWVlpa6zvHfZMO1H3Y4k7Ixha+/F8M3Pz4tpmrKystLyed4bdrVa9U+PO1nXVoYv9tHOVgMxYQ0NDaFYLKJSqcCyLMzMzNRdTHfb/lawLEu7hFjFsb1nz54FACwuLuLtt9/G7373OwwNDa1bz5///GcsLy/j1KlTLZfT2G82JXzz8/MAgFu3bm24zNWrV1Gr1QB8O8oUlmEYqNVqGB4extzcHCqVCmZmZjat/c3kvZi//OUvVdYft7i2969//Stee+01AMDExAQA4Jlnnmm7/PDwMCzLwsTEBBYWFvDKK6/UzVfdbzo4TLbljTSZpumfT3ujRsDXI07BEbDgzbbtunnetVxwwMYbZAG+vhD21uGd64vIuu2HEXx+cCQsbF0A/At5byTWNE2//eBooMi3o3Fe/4h8e/3hOE7dKXUY6PC0M7gd3rYlYXtbjZR6vDa8EWzv+bZt1512Bgflgs8LXvt5wu6XUW35NZ/I10HwOtwLmzeE63WGbdv+nwcsy/KD0bjh603zXig0XPOt134YndTQblpweHp+fr5uUMi2bX9esVgUEWnqH2+AIJPJNO1AYeoPG75WO1urW9zbG7Yubz2Nz/dGP1u97t51YSth9svgG0snYglfL+v23XGzauh0wKWbdWlvbydaDbTEJVEDLkRxKxQKGBsb0y6jCcPXJdd1W97frtKyvefPn6/7N7LDhw9rl9Rkyz5SlCRh/29QIvwH/VNPPVV3P0obaZKW7fVGQOfn53HmzBnlalrrifBt5Q6S1J1vq6Rle8+cOZPY0Hl42kmkhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8RErafqohiR8+pPW9//77WFpa0i6DAlZXV9vOM6ThMyLlchmXLl3a8qKoO/fu3cMnn3yCgwcPapdCIbR4U1xqCh+lQ6FQwPj4eGo+X0dNlnjNR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekpO1vslNyrK6u4tSpU3jw4IE/7fPPP0d/fz9ef/31umVfeOEFfPjhhzFXSFEwfCmwe/dufPrpp7hz507TvI8++qju8auvvhpXWdQlnnamxPT0NAYGBjZc7vjx4zFUQ5uB4UuJkydPYm1tbd1l9u7dixdffDGmiqhbDF9K7NmzBy+99BIMw2g5f2BgAKdOnYq5KuoGw5ci09PT6Ovraznv/v37OHbsWMwVUTcYvhSZmJjAV1991TTdMAzs27cPzz33XPxFUWQMX4o8/fTTOHDgAHbsqH/Z+vr6MD09rVQVRcXwpczU1FTTNBHBkSNHFKqhbjB8KTM2NlZ35Ovr68PIyAgGBwcVq6IoGL6UeeKJJ/Dmm2/6Ay8igsnJSeWqKAqGL4UmJyf9gZf+/n6Mjo4qV0RRMHwpNDo6iocffti/v3PnTuWKKIrU/m9noVDQLkHVyy+/jI8//hjPP/98T/fFj370I+zfv1+7jEgMERHtIqJo958e1FuOHj2KpaUl7TKiWEr1aWc+n4eI9NQtn88DAL788ku8++676vVo3o4ePaq8B3Yn1eHrZQMDA3jvvfe0y6AuMHwp9sgjj2iXQF1g+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+Rkp4On+u6WFxc5NcwkIrUfpJ9M1y4cAEffPCBdhlbar0PHWezWQwNDeHgwYPYtWtXjFUR0ONHvrm5Oe0StpyIwHEc/3G1WvU/jDoyMoKFhQVMTU3BdV3FKntTT4evVwS/0zN4hBseHsaVK1cAAKdPn0atVou9tl7WU+Gr1WpYXFyEYRgYHR3F7du3m5ZxXRezs7P+Mjdu3PCnB68PS6WSv8zdu3fr2vCev7CwANd160792rWvZXBwEOfOnUOpVMLy8nLdvF7ri9hJSgGQfD7f0XNM0xTLsqRarYqISC6XEwDidYPjOGKapuRyORERuX79ugCQSqUipmn6y5bLZRERsW1bAIhlWf46stms2LYtIiLValUymUyo9sPK5/MS5WULbmejarXatB1p6IujR4/K0aNHO+iFRCn0TPiKxaIAkJWVFX+at9N5O4QXxsb1ZDIZ/36r+cFpAMRxHP+x4zih2w9jK8LXan4a+oLhU9Jp+CzLarnzBXeY4Dt6461x2VbPD64nl8v5R1jPRu2HEVf40tAXDJ+STsPX7oXdaIfaqI3GaSsrK3U7Vjab3bCGTmzlaWfwqJOGvkh7+HpqwCWsVgMxYQ0NDaFYLKJSqcCyLMzMzGB2dnbT2t8KN2/eBAAcOnSoaV6v9UWceiZ88/PzAIBbt25tuMzVq1f9YXdvRC4swzBQq9UwPDyMubk5VCoVzMzMbFr7m811XVy+fBmmaeLw4cP+9F7si9hpH3ujQoennd5onGma/gicN8KGb0bpvAGBxptt23XzvOuX4ICNN7CAb07fvHXYtu2fbq3XflhRTjuDdQavvbyRS9M06wZGNqo1KX2R9tPOngmfyNcvvjcI4IXNG+72dhjbtv0hccuy/J2hcSdZb5rjOJLNZpuuc9ZrP6xOw9dqB/du2WzW/1NBu/5Kcl+kPXyp/qGUfD6PY8eOaZcSq0KhgPHxcaT0ZdtUY2NjAMAfSiGizjB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUpPpXisrlsnYJsfO2uVAoKFeib3V1Fbt379YuI7JUf40E0dGjR1P7NRKpPfKl9D1j0/C7XNKP13xEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REpS+8u0veTevXv44x//WDftb3/7GwBgfn6+bvqjjz6KEydOxFYbRZfa32TvJf/73//w5JNP4osvvkBfXx+Ar38WW0SwY8e3Jy9ra2uYnp7GH/7wB61SKbwlnnamwMMPP4yxsTH09/djbW0Na2truH//Ph48eOA/XltbAwAe9VKE4UuJEydO4Msvv1x3mccffxxvvPFGTBVRtxi+lDh06BCefPLJtvMHBgYwOTmJ/n5exqcFw5cSO3bswIkTJ/DQQw+1nL+2toaJiYmYq6JuMHwpMjEx0fbU84c//CH2798fc0XUDYYvRfbt24dnn322afrAwABOnToFwzAUqqKoGL6UmZqawsDAQN00nnKmE8OXMidPnvT/rODZs2cPXnrpJaWKKCqGL2V+8pOfYO/evf4p5sDAAH7zm98oV0VRMHwpND097f+ny9raGo4dO6ZcEUXB8KXQ8ePH8eDBAwDAT3/6U+zZs0e5IoqC4UuhZ599Fj/72c8AfH0UpHRK3D9WFwoFjI+Pa5dB20zCdnMAWErs/yLl83ntEhLtP//5D37/+9/jt7/9bcv54+PjOHfuXM//4b1cLuPy5cvaZbSU2PBxEGFjr732Gn784x+3nDc+Po79+/ezH4HEho/XfCnWLniUDgwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEp2bbhc10Xi4uLGB0d1S6FqKXEfp6vWxcuXMAHH3ygXUZHarUaPvnkE/zjH/9AqVRCsViMZb3rfdluNpvF0NAQDh48iF27dsVST6/Ytke+ubk57RI6ls1m8ac//Qlvv/02SqVSbOsVETiO4z+uVqv+7/+NjIxgYWEBU1NTcF03tpp6wbYNXxpdvHgRFy9eVFn34OCgfz94hBseHsaVK1cAAKdPn0atVou9tu1q24SvVqthcXERhmFgdHQUt2/fblrGdV3Mzs76y9y4ccOfHrw+LJVK/jJ3796ta8N7/sLCAlzXrTtla9d+2g0ODuLcuXMolUpYXl6um8c+7YIkTD6flyhlmaYplmVJtVoVEZFcLicA/LYcxxHTNCWXy4mIyPXr1wWAVCoVMU3TX7ZcLouIiG3bAkAsy/LXkc1mxbZtERGpVquSyWRCtd+pYN1RAZB8Pr9p661Wq039kYY+jbo/xaCQuKqidFaxWBQAsrKy4k/zdhavLS+MQQAkk8n491vND04DII7j+I8dxwndfieSGL5W89PQpwxfB6J0lmVZLZ8TfKGD78SNt8ZlWz0/uJ5cLucfYT0btd+JtIQvDX3K8HUgSme1e0E22hE2aqNx2srKSt0Okc1mN6whiiSGzzuTCB510tCnSQ7fthlwCavVQExYQ0NDKBaLqFQqsCwLMzMzmJ2d3bT2k+zmzZsAvv5t+Ebs02i2Rfjm5+cBALdu3dpwmatXr/rD5d5IWliGYaBWq2F4eBhzc3OoVCqYmZnZtPaTynVdXL58GaZp4vDhw/509mmXtI+9jaKcJnijaKZp+iNn3sgYvhld8y7kG2+2bdfN8647ggM23oAAvjnt8tZh27Z/mrRe+50IrrfxGqgT6PC0s916vZFL0zTrBkZE1t/mpPRpkk87E1dV1M6ybdu/ePfC5g1Tey+0bdv+ULZlWf6L2PjirjfNcRzJZrNN1yfrtR9Wqx0t6o7TSfjardfbRu9PBa0kvU+THL7E/kpRwspKHcMwkM/ne/63GhK8Py1ti2s+ojRi+IiUbNuPFCXJeh/ZCUrgqRFtIYYvBgwVtcLTTiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUpLYTzWE/RgOtTc+Po7x8XHtMqiNxIXvwIEDyOfz2mUkXrlcxuXLl9lXKZa473ChcBL83SQUDr/DhUgLw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5GSxP0yLTVbW1vDf//737ppX3zxBQDg3//+d910wzDw+OOPx1YbRcfwpcC//vUv7N69Gw8ePGia973vfa/u8euvv46//OUvcZVGXeBpZwr84Ac/wMGDB7Fjx/ovl2EYmJiYiKkq6hbDlxJTU1MwDGPdZXbs2IEjR47EVBF1i+FLiSNHjqCvr6/t/L6+Prz11lv4/ve/H2NV1A2GLyV27tyJt956C/39rS/TRQSTk5MxV0XdYPhSZHJysuWgCwA89NBD+L//+7+YK6JuMHwpYpomvvvd7zZN7+/vx69+9Ss8+uijClVRVAxfinznO9/Br3/9awwMDNRNv3//Pk6ePKlUFUXF8KXMiRMnsLa2Vjdt586d+MUvfqFUEUXF8KXMyMhI3R/WBwYGcPz4cTz00EOKVVEUDF/K9Pf34/jx4/6p59raGk6cOKFcFUXB8KXQxMSEf+r51FNP4dVXX1WuiKJg+FLo5z//OZ5++mkAX//ny0b/dkbJlLh/rC6Xy7h06ZJ2GYn32GOPAQD+/ve/Y2xsTLma5FtaWtIuoUni3jI/++wzXLt2TbuMxHvmmWfw2GOP4Yknnmg5/9q1a1hdXY25quRZXV1N7P6UuCOfJ4nvVElTKBRw7NixlvMMw8A777zTdn6vKBQKGB8f1y6jpcQd+Si8Xg9W2jF8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekZNuGz3VdLC4uYnR0VLsUopa2bfguXLiAiYkJlEol7VJCu3v3Ls6ePQvDMHD27FncuHEjlvUahtH2Njs7i1KphFqtFkstvWTbhm9ubk67hI7UajXcunULc3NzqFareO211/DGG2/E8uYhInAcx39crVYhIhARjIyMYGFhAVNTU3Bdd8tr6SXbNnxps7y8DNM0AQC7du3C8ePHASC20+bBwUH//q5du/z7w8PDuHLlCgDg9OnTPAJuom0TvlqthsXFRRiGgdHRUdy+fbtpGdd1MTs76y/jndY1Xh+WSiV/mbt379a14T1/YWEBruvW/WZeu/bD8ILXyLKs0G1slcHBQZw7dw6lUgnLy8t185Lcp4knCZPP5yVKWaZpimVZUq1WRUQkl8sJAL8tx3HENE3J5XIiInL9+nUBIJVKRUzT9Jctl8siImLbtgAQy7L8dWSzWbFtW0REqtWqZDKZUO1HUa1WBYAUi8VIzwcg+Xy+4+e063uvnmB/pKFPo+5PMSgkrqoonVUsFgWArKys+NO8ncVrywtjEADJZDL+/Vbzg9MAiOM4/mPHcUK336nr16+LaZr+m0mnNjt8reanoU8Zvg5E6SzLslo+J/hCB9+JG2+Ny7Z6fnA9uVyuKRQbtd8p0zT9I0YUcYQvDX3K8HUgSme1e0E22hE2aqNx2srKSt0Okc1mN6whilwuJ/Pz8121sVWnncGjThr6NMnh2zYDLmG1GogJa2hoCMViEZVKBZZlYWZmBrOzs5vWPgDcunUL//znP3HmzJmu2tlsN2/eBAAcOnSoaV7S+zSxtOPfKMo71fz8fMsLcQTeOb1lMpmMf3rjOI7/TosQ79IA6k6NKpVK6PbDaLV8pVKpG6AIC5t45PMGPkzTrJuehj5N8pEvcVVF6SxvFM00TX/kzBsZA74eXfMu5Btvtm3XzfNe5OCAjTcg4O0I3jps2/Z3hPXaD8PbwVu1EWXEs9PwBbe3MQxe8IIDI17NSe5TEYavI1E7y7Zt/+LdC5s3TO290LZt+0PZlmX5L2Lji7veNO+dFw3XJ+u1H4ZXe6tbcBQ3rE7C12693jauN/CT5D4VSXb4DBGR1iekOrzv1k9YWaljGAby+XzPf6V8gvenpZ4bcCFKCoaPSElifyJsOwn+r+J6EnhqRFuI4YsBQ0Wt8LSTSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUJPZTDWNjY9olpN7777+PpaUl7TJUra6uapfQVuK+RqJcLuPSpUvaZSTevXv38Mknn+DgwYPapaRCAt+ElhIXPgonwd9NQuHwO1yItDB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+REoaPSAnDR6SE4SNSwvARKWH4iJQk9jfZ6Vurq6s4deoUHjx44E/7/PPP0d/fj9dff71u2RdeeAEffvhhzBVSFAxfCuzevRuffvop7ty50zTvo48+qnv86quvxlUWdYmnnSkxPT2NgYGBDZc7fvx4DNXQZmD4UuLkyZNYW1tbd5m9e/fixRdfjKki6hbDlxJ79uzBSy+9BMMwWs4fGBjAqVOnYq6KusHwpcj09DT6+vpazrt//z6OHTsWc0XUDYYvRSYmJvDVV181TTcMA/v27cNzzz0Xf1EUGcOXIk8//TQOHDiAHTvqX7a+vj5MT08rVUVRMXwpMzU11TRNRHDkyBGFaqgbDF/KjI2N1R35+vr6MDIygsHBQcWqKAqGL2WeeOIJvPnmm/7Ai4hgcnJSuSqKguFLocnJSX/gpb+/H6Ojo8oVURQMXwqNjo7i4Ycf9u/v3LlTuSKKInH/27m6uoqPP/5Yu4zEe/nll/Hxxx/j+eefR6FQ0C4n8ZL4N1BDRES7iKBCoYDx8XHtMmibSdhuDgBLiT3tFBHe1rl9+eWXePfdd9vOB4B8Pq9ep/Ytn88r78ntJTZ8tL6BgQG899572mVQFxi+FHvkkUe0S6AuMHxEShg+IiUMH5ESho9ICcNHpIThI1LC8BEpYfiIlDB8REoYPiIlDB+REoaPSMm2DZ/rulhcXORXLFBibdvwXbhwARMTEyiVStqlhOa6Ls6fPw/DMGAYBhYXF2NZr7e+VrfZ2VmUSiXUarVYaukl2zZ8c3Nz2iV0xHVd3LlzBxcvXoSIIJfLYWJiArOzs1u+bhGB4zj+42q16n8YdWRkBAsLC5iamoLrulteSy/ZtuFLmzt37uCVV17xH3s/9TUzMxPL+oPf+7lr1y7//vDwMK5cuQIAOH36NI+Am2jbhK9Wq2FxcRGGYWB0dBS3b99uWsZ1XczOzvrL3Lhxw58evD4slUr+Mnfv3q1rw3v+wsICXNet+9Wgdu2HEQyetz0AkMlkQrexVQYHB3Hu3DmUSiUsLy/XzUtynyaeJEw+n5coZZmmKZZlSbVaFRGRXC4nAPy2HMcR0zQll8uJiMj169cFgFQqFTFN01+2XC6LiIht2wJALMvy15HNZsW2bRERqVarkslkQrXfKdu2/bZXVlY6fr6ICADJ5/MdP6dd31er1ab+SEOfRt2fYlBIXFVROqtYLDbtqN7O4rXlhTEIgGQyGf9+q/nBaQDEcRz/seM4odsPy9tBvVs2m+3o+cF1b2b4Ws1PQ58yfB2I0lmWZbV8TvCFDr4TN94al231/OB6crmcf4T1bNR+pyqVin8UmJ+f7/j5cYQvDX3K8HUgSme1e0E22hE2aqNx2srKSt0OETwqdRO0dlZWViK3u1WnncGjThr6NMnh2zYDLmG1GogJa2hoCMViEZVKBZZlYWZmpulPAd2032p9SXHz5k0AwKFDh5rmpalPk2RbhG9+fh4AcOvWrQ2XuXr1qj+S6I2khWUYBmq1GoaHhzE3N4dKpeL/KWAz2m/ktZPL5SK3sRlc18Xly5dhmiYOHz7sT09jnyaK9rG3UZTTBG+QwjRNf+TMGxnDN6Nr3oV848227bp53nVHcMDGGxDAN6dd3jps2/ZPk9ZrPwzTNFuO/HU6YONBh6edwe0NXnt5I5emadYNjIisv81J6FORZJ92Jq6qqJ1l27Z/8e6FzRum9l7o4BC+ZVn+i9j44q43zXEcyWazTdcn67Ufhjdi692y2aw/RB9FJ+FrtYOHrSPJfSqS7PAl9odSElZW6hiGgXw+n8hf54lTgven5P5QCtF2x/ARKUncj2NuR8H/VVxPAk+NaAsxfDFgqKgVnnYSKWH4iJQwfERKGD4iJQwfkRKGj0gJw0ekhOEjUsLwESlh+IiUMHxEShg+IiUMH5GSxH6qoVAoaJeQeuVyWbsEdUnug8R+jQTRZkrYbg4AS4kLH1GP4He4EGlh+IiUMHxEShg+IiX/D+uyEQgjdIlzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(ANN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "54cc949a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_History(history1,121)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4d71b1c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 467us/step\n",
      "0.89\n",
      "[[181  86]\n",
      " [ 20 691]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.68      0.77       267\n",
      "           1       0.89      0.97      0.93       711\n",
      "\n",
      "    accuracy                           0.89       978\n",
      "   macro avg       0.89      0.82      0.85       978\n",
      "weighted avg       0.89      0.89      0.89       978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#ANN accuracy\n",
    "y_pred = ANN.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n",
    "print(round(accuracy_score(new_y_test, y_pred.ravel()),2))\n",
    "print(confusion_matrix(new_y_test,y_pred))\n",
    "print('\\n')\n",
    "print(classification_report(new_y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e8202f",
   "metadata": {},
   "source": [
    "## Pass / Fail / Distinction Prediction ( Multi Class Classification )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d32b69b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting TRAIN and TEST data\n",
    "new_y_M = to_categorical(y)\n",
    "X_train_M,X_test_M,new_y_train_M,new_y_test_M = train_test_split(X,new_y_M,test_size =0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6e57a098",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "228/228 [==============================] - 1s 1ms/step - loss: 0.9308 - accuracy: 0.6226 - val_loss: 0.8639 - val_accuracy: 0.6237\n",
      "Epoch 2/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.8483 - accuracy: 0.6226 - val_loss: 0.8264 - val_accuracy: 0.6237\n",
      "Epoch 3/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.8097 - accuracy: 0.6415 - val_loss: 0.7762 - val_accuracy: 0.6881\n",
      "Epoch 4/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.7290 - accuracy: 0.7565 - val_loss: 0.6915 - val_accuracy: 0.7679\n",
      "Epoch 5/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.6685 - accuracy: 0.7727 - val_loss: 0.6468 - val_accuracy: 0.7771\n",
      "Epoch 6/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.6379 - accuracy: 0.7806 - val_loss: 0.6249 - val_accuracy: 0.7812\n",
      "Epoch 7/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.6191 - accuracy: 0.7819 - val_loss: 0.6096 - val_accuracy: 0.7791\n",
      "Epoch 8/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.6051 - accuracy: 0.7793 - val_loss: 0.5981 - val_accuracy: 0.7751\n",
      "Epoch 9/1000\n",
      "228/228 [==============================] - 0s 794us/step - loss: 0.5934 - accuracy: 0.7815 - val_loss: 0.5862 - val_accuracy: 0.7761\n",
      "Epoch 10/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5823 - accuracy: 0.7828 - val_loss: 0.5792 - val_accuracy: 0.7751\n",
      "Epoch 11/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5730 - accuracy: 0.7824 - val_loss: 0.5664 - val_accuracy: 0.7832\n",
      "Epoch 12/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.5650 - accuracy: 0.7797 - val_loss: 0.5605 - val_accuracy: 0.7740\n",
      "Epoch 13/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5571 - accuracy: 0.7815 - val_loss: 0.5557 - val_accuracy: 0.7751\n",
      "Epoch 14/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.5500 - accuracy: 0.7841 - val_loss: 0.5472 - val_accuracy: 0.7761\n",
      "Epoch 15/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.5453 - accuracy: 0.7824 - val_loss: 0.5410 - val_accuracy: 0.7802\n",
      "Epoch 16/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.5397 - accuracy: 0.7832 - val_loss: 0.5391 - val_accuracy: 0.7771\n",
      "Epoch 17/1000\n",
      "228/228 [==============================] - 0s 803us/step - loss: 0.5338 - accuracy: 0.7828 - val_loss: 0.5329 - val_accuracy: 0.7853\n",
      "Epoch 18/1000\n",
      "228/228 [==============================] - 0s 785us/step - loss: 0.5309 - accuracy: 0.7819 - val_loss: 0.5285 - val_accuracy: 0.7771\n",
      "Epoch 19/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.5276 - accuracy: 0.7846 - val_loss: 0.5267 - val_accuracy: 0.7781\n",
      "Epoch 20/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5247 - accuracy: 0.7854 - val_loss: 0.5205 - val_accuracy: 0.7761\n",
      "Epoch 21/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.5204 - accuracy: 0.7832 - val_loss: 0.5171 - val_accuracy: 0.7822\n",
      "Epoch 22/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5181 - accuracy: 0.7824 - val_loss: 0.5226 - val_accuracy: 0.7761\n",
      "Epoch 23/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.5154 - accuracy: 0.7832 - val_loss: 0.5153 - val_accuracy: 0.7822\n",
      "Epoch 24/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5139 - accuracy: 0.7828 - val_loss: 0.5108 - val_accuracy: 0.7781\n",
      "Epoch 25/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5115 - accuracy: 0.7837 - val_loss: 0.5078 - val_accuracy: 0.7832\n",
      "Epoch 26/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.5110 - accuracy: 0.7841 - val_loss: 0.5077 - val_accuracy: 0.7812\n",
      "Epoch 27/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5091 - accuracy: 0.7824 - val_loss: 0.5053 - val_accuracy: 0.7822\n",
      "Epoch 28/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5083 - accuracy: 0.7832 - val_loss: 0.5043 - val_accuracy: 0.7781\n",
      "Epoch 29/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5066 - accuracy: 0.7828 - val_loss: 0.5057 - val_accuracy: 0.7771\n",
      "Epoch 30/1000\n",
      "228/228 [==============================] - 0s 775us/step - loss: 0.5060 - accuracy: 0.7854 - val_loss: 0.5055 - val_accuracy: 0.7781\n",
      "Epoch 31/1000\n",
      "228/228 [==============================] - 0s 766us/step - loss: 0.5051 - accuracy: 0.7898 - val_loss: 0.5019 - val_accuracy: 0.7812\n",
      "Epoch 32/1000\n",
      "228/228 [==============================] - 0s 783us/step - loss: 0.5050 - accuracy: 0.7846 - val_loss: 0.5017 - val_accuracy: 0.7771\n",
      "Epoch 33/1000\n",
      "228/228 [==============================] - 0s 798us/step - loss: 0.5025 - accuracy: 0.7903 - val_loss: 0.4992 - val_accuracy: 0.7894\n",
      "Epoch 34/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5041 - accuracy: 0.7925 - val_loss: 0.4983 - val_accuracy: 0.7894\n",
      "Epoch 35/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5030 - accuracy: 0.7942 - val_loss: 0.5042 - val_accuracy: 0.7832\n",
      "Epoch 36/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.5020 - accuracy: 0.7946 - val_loss: 0.4985 - val_accuracy: 0.7935\n",
      "Epoch 37/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5014 - accuracy: 0.7951 - val_loss: 0.4979 - val_accuracy: 0.7883\n",
      "Epoch 38/1000\n",
      "228/228 [==============================] - 0s 777us/step - loss: 0.5013 - accuracy: 0.7898 - val_loss: 0.4979 - val_accuracy: 0.7924\n",
      "Epoch 39/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.5006 - accuracy: 0.7968 - val_loss: 0.4976 - val_accuracy: 0.7904\n",
      "Epoch 40/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.5012 - accuracy: 0.7933 - val_loss: 0.4962 - val_accuracy: 0.7894\n",
      "Epoch 41/1000\n",
      "228/228 [==============================] - 0s 768us/step - loss: 0.5000 - accuracy: 0.7938 - val_loss: 0.4986 - val_accuracy: 0.7853\n",
      "Epoch 42/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4992 - accuracy: 0.7951 - val_loss: 0.4996 - val_accuracy: 0.7843\n",
      "Epoch 43/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.5006 - accuracy: 0.7903 - val_loss: 0.4953 - val_accuracy: 0.7894\n",
      "Epoch 44/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4992 - accuracy: 0.7933 - val_loss: 0.4991 - val_accuracy: 0.7904\n",
      "Epoch 45/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4999 - accuracy: 0.7903 - val_loss: 0.4951 - val_accuracy: 0.7914\n",
      "Epoch 46/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4985 - accuracy: 0.7894 - val_loss: 0.4995 - val_accuracy: 0.7843\n",
      "Epoch 47/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4989 - accuracy: 0.7898 - val_loss: 0.4947 - val_accuracy: 0.7863\n",
      "Epoch 48/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4986 - accuracy: 0.7933 - val_loss: 0.4949 - val_accuracy: 0.7883\n",
      "Epoch 49/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4988 - accuracy: 0.7885 - val_loss: 0.4953 - val_accuracy: 0.7853\n",
      "Epoch 50/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4984 - accuracy: 0.7920 - val_loss: 0.4942 - val_accuracy: 0.7914\n",
      "Epoch 51/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4969 - accuracy: 0.7977 - val_loss: 0.4943 - val_accuracy: 0.7924\n",
      "Epoch 52/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4978 - accuracy: 0.7960 - val_loss: 0.4947 - val_accuracy: 0.7904\n",
      "Epoch 53/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4981 - accuracy: 0.7942 - val_loss: 0.4969 - val_accuracy: 0.7812\n",
      "Epoch 54/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4979 - accuracy: 0.7929 - val_loss: 0.4933 - val_accuracy: 0.7924\n",
      "Epoch 55/1000\n",
      "228/228 [==============================] - 0s 785us/step - loss: 0.4970 - accuracy: 0.7889 - val_loss: 0.4944 - val_accuracy: 0.7873\n",
      "Epoch 56/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4960 - accuracy: 0.7968 - val_loss: 0.4933 - val_accuracy: 0.7935\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4959 - accuracy: 0.7907 - val_loss: 0.4943 - val_accuracy: 0.7863\n",
      "Epoch 58/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4968 - accuracy: 0.7933 - val_loss: 0.4930 - val_accuracy: 0.7853\n",
      "Epoch 59/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4962 - accuracy: 0.7938 - val_loss: 0.4938 - val_accuracy: 0.7843\n",
      "Epoch 60/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4965 - accuracy: 0.7942 - val_loss: 0.4965 - val_accuracy: 0.7822\n",
      "Epoch 61/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4960 - accuracy: 0.7916 - val_loss: 0.4941 - val_accuracy: 0.7863\n",
      "Epoch 62/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4959 - accuracy: 0.7951 - val_loss: 0.4991 - val_accuracy: 0.7853\n",
      "Epoch 63/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4957 - accuracy: 0.7942 - val_loss: 0.5022 - val_accuracy: 0.7853\n",
      "Epoch 64/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4960 - accuracy: 0.7903 - val_loss: 0.4970 - val_accuracy: 0.7822\n",
      "Epoch 65/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4960 - accuracy: 0.7929 - val_loss: 0.4925 - val_accuracy: 0.7853\n",
      "Epoch 66/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4957 - accuracy: 0.7925 - val_loss: 0.4920 - val_accuracy: 0.7894\n",
      "Epoch 67/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4960 - accuracy: 0.7916 - val_loss: 0.4926 - val_accuracy: 0.7883\n",
      "Epoch 68/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4957 - accuracy: 0.7894 - val_loss: 0.4920 - val_accuracy: 0.7883\n",
      "Epoch 69/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4954 - accuracy: 0.7942 - val_loss: 0.4927 - val_accuracy: 0.7883\n",
      "Epoch 70/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4953 - accuracy: 0.7942 - val_loss: 0.4963 - val_accuracy: 0.7822\n",
      "Epoch 71/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4950 - accuracy: 0.7951 - val_loss: 0.4912 - val_accuracy: 0.7955\n",
      "Epoch 72/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4949 - accuracy: 0.7938 - val_loss: 0.4922 - val_accuracy: 0.7873\n",
      "Epoch 73/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4949 - accuracy: 0.7925 - val_loss: 0.4930 - val_accuracy: 0.7853\n",
      "Epoch 74/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4955 - accuracy: 0.7951 - val_loss: 0.4944 - val_accuracy: 0.7832\n",
      "Epoch 75/1000\n",
      "228/228 [==============================] - 0s 785us/step - loss: 0.4958 - accuracy: 0.7920 - val_loss: 0.4914 - val_accuracy: 0.7894\n",
      "Epoch 76/1000\n",
      "228/228 [==============================] - 0s 794us/step - loss: 0.4941 - accuracy: 0.7929 - val_loss: 0.4931 - val_accuracy: 0.7863\n",
      "Epoch 77/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4943 - accuracy: 0.7916 - val_loss: 0.4909 - val_accuracy: 0.7965\n",
      "Epoch 78/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4950 - accuracy: 0.7942 - val_loss: 0.4919 - val_accuracy: 0.7883\n",
      "Epoch 79/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4954 - accuracy: 0.7885 - val_loss: 0.4955 - val_accuracy: 0.7832\n",
      "Epoch 80/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4937 - accuracy: 0.7920 - val_loss: 0.4902 - val_accuracy: 0.7955\n",
      "Epoch 81/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4954 - accuracy: 0.7907 - val_loss: 0.4939 - val_accuracy: 0.7883\n",
      "Epoch 82/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4949 - accuracy: 0.7929 - val_loss: 0.4912 - val_accuracy: 0.7873\n",
      "Epoch 83/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4935 - accuracy: 0.7933 - val_loss: 0.4937 - val_accuracy: 0.7914\n",
      "Epoch 84/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4937 - accuracy: 0.7898 - val_loss: 0.4920 - val_accuracy: 0.7904\n",
      "Epoch 85/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4960 - accuracy: 0.7951 - val_loss: 0.4914 - val_accuracy: 0.7873\n",
      "Epoch 86/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4946 - accuracy: 0.7977 - val_loss: 0.5009 - val_accuracy: 0.7812\n",
      "Epoch 87/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4945 - accuracy: 0.7951 - val_loss: 0.4946 - val_accuracy: 0.7853\n",
      "Epoch 88/1000\n",
      "228/228 [==============================] - 0s 771us/step - loss: 0.4953 - accuracy: 0.7925 - val_loss: 0.4929 - val_accuracy: 0.7853\n",
      "Epoch 89/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4940 - accuracy: 0.7938 - val_loss: 0.4905 - val_accuracy: 0.7935\n",
      "Epoch 90/1000\n",
      "228/228 [==============================] - 0s 757us/step - loss: 0.4948 - accuracy: 0.7925 - val_loss: 0.4913 - val_accuracy: 0.7883\n",
      "Epoch 91/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4938 - accuracy: 0.7946 - val_loss: 0.4899 - val_accuracy: 0.7955\n",
      "Epoch 92/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4944 - accuracy: 0.7951 - val_loss: 0.4904 - val_accuracy: 0.7924\n",
      "Epoch 93/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4942 - accuracy: 0.7964 - val_loss: 0.4912 - val_accuracy: 0.7863\n",
      "Epoch 94/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4925 - accuracy: 0.7925 - val_loss: 0.4950 - val_accuracy: 0.7843\n",
      "Epoch 95/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4946 - accuracy: 0.7968 - val_loss: 0.4899 - val_accuracy: 0.7965\n",
      "Epoch 96/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4940 - accuracy: 0.7938 - val_loss: 0.4904 - val_accuracy: 0.7904\n",
      "Epoch 97/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4933 - accuracy: 0.7925 - val_loss: 0.4902 - val_accuracy: 0.7914\n",
      "Epoch 98/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4937 - accuracy: 0.7946 - val_loss: 0.4899 - val_accuracy: 0.7945\n",
      "Epoch 99/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4934 - accuracy: 0.7951 - val_loss: 0.4925 - val_accuracy: 0.7853\n",
      "Epoch 100/1000\n",
      "228/228 [==============================] - 0s 762us/step - loss: 0.4940 - accuracy: 0.7911 - val_loss: 0.4915 - val_accuracy: 0.7853\n",
      "Epoch 101/1000\n",
      "228/228 [==============================] - 0s 782us/step - loss: 0.4934 - accuracy: 0.7942 - val_loss: 0.4901 - val_accuracy: 0.7914\n",
      "Epoch 102/1000\n",
      "228/228 [==============================] - 0s 794us/step - loss: 0.4937 - accuracy: 0.7938 - val_loss: 0.4911 - val_accuracy: 0.7853\n",
      "Epoch 103/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4931 - accuracy: 0.7960 - val_loss: 0.4917 - val_accuracy: 0.7832\n",
      "Epoch 104/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4942 - accuracy: 0.7933 - val_loss: 0.4903 - val_accuracy: 0.7863\n",
      "Epoch 105/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4938 - accuracy: 0.7933 - val_loss: 0.4926 - val_accuracy: 0.7853\n",
      "Epoch 106/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4937 - accuracy: 0.7907 - val_loss: 0.4917 - val_accuracy: 0.7843\n",
      "Epoch 107/1000\n",
      "228/228 [==============================] - 0s 803us/step - loss: 0.4937 - accuracy: 0.7960 - val_loss: 0.4923 - val_accuracy: 0.7853\n",
      "Epoch 108/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4934 - accuracy: 0.7986 - val_loss: 0.4901 - val_accuracy: 0.7873\n",
      "Epoch 109/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4922 - accuracy: 0.7938 - val_loss: 0.4906 - val_accuracy: 0.7873\n",
      "Epoch 110/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4930 - accuracy: 0.7938 - val_loss: 0.4910 - val_accuracy: 0.7863\n",
      "Epoch 111/1000\n",
      "228/228 [==============================] - 0s 794us/step - loss: 0.4940 - accuracy: 0.7946 - val_loss: 0.4901 - val_accuracy: 0.7863\n",
      "Epoch 112/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "228/228 [==============================] - 0s 763us/step - loss: 0.4943 - accuracy: 0.7920 - val_loss: 0.4900 - val_accuracy: 0.7853\n",
      "Epoch 113/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4929 - accuracy: 0.7885 - val_loss: 0.4959 - val_accuracy: 0.7832\n",
      "Epoch 114/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4927 - accuracy: 0.7933 - val_loss: 0.4936 - val_accuracy: 0.7843\n",
      "Epoch 115/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4928 - accuracy: 0.7946 - val_loss: 0.4895 - val_accuracy: 0.7945\n",
      "Epoch 116/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4922 - accuracy: 0.7951 - val_loss: 0.4946 - val_accuracy: 0.7832\n",
      "Epoch 117/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4927 - accuracy: 0.7946 - val_loss: 0.4898 - val_accuracy: 0.7863\n",
      "Epoch 118/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4913 - accuracy: 0.7942 - val_loss: 0.4893 - val_accuracy: 0.7924\n",
      "Epoch 119/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4928 - accuracy: 0.7920 - val_loss: 0.4889 - val_accuracy: 0.7914\n",
      "Epoch 120/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4925 - accuracy: 0.7964 - val_loss: 0.4890 - val_accuracy: 0.7873\n",
      "Epoch 121/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4922 - accuracy: 0.7929 - val_loss: 0.4894 - val_accuracy: 0.7924\n",
      "Epoch 122/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4922 - accuracy: 0.7955 - val_loss: 0.4943 - val_accuracy: 0.7863\n",
      "Epoch 123/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4912 - accuracy: 0.7933 - val_loss: 0.4895 - val_accuracy: 0.7894\n",
      "Epoch 124/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4922 - accuracy: 0.7973 - val_loss: 0.4899 - val_accuracy: 0.7853\n",
      "Epoch 125/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4926 - accuracy: 0.7907 - val_loss: 0.4886 - val_accuracy: 0.7863\n",
      "Epoch 126/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4924 - accuracy: 0.7946 - val_loss: 0.4893 - val_accuracy: 0.7863\n",
      "Epoch 127/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4913 - accuracy: 0.7929 - val_loss: 0.4936 - val_accuracy: 0.7832\n",
      "Epoch 128/1000\n",
      "228/228 [==============================] - 0s 745us/step - loss: 0.4920 - accuracy: 0.7920 - val_loss: 0.4897 - val_accuracy: 0.7863\n",
      "Epoch 129/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4927 - accuracy: 0.7916 - val_loss: 0.4894 - val_accuracy: 0.7843\n",
      "Epoch 130/1000\n",
      "228/228 [==============================] - 0s 785us/step - loss: 0.4922 - accuracy: 0.7960 - val_loss: 0.4902 - val_accuracy: 0.7924\n",
      "Epoch 131/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4920 - accuracy: 0.7920 - val_loss: 0.4891 - val_accuracy: 0.7863\n",
      "Epoch 132/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4912 - accuracy: 0.7977 - val_loss: 0.4882 - val_accuracy: 0.7945\n",
      "Epoch 133/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4931 - accuracy: 0.7903 - val_loss: 0.4881 - val_accuracy: 0.7924\n",
      "Epoch 134/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4920 - accuracy: 0.7951 - val_loss: 0.4882 - val_accuracy: 0.7924\n",
      "Epoch 135/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4924 - accuracy: 0.7933 - val_loss: 0.4880 - val_accuracy: 0.7894\n",
      "Epoch 136/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4912 - accuracy: 0.7938 - val_loss: 0.4903 - val_accuracy: 0.7863\n",
      "Epoch 137/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4911 - accuracy: 0.7938 - val_loss: 0.4875 - val_accuracy: 0.7945\n",
      "Epoch 138/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4918 - accuracy: 0.7925 - val_loss: 0.4909 - val_accuracy: 0.7863\n",
      "Epoch 139/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4918 - accuracy: 0.7973 - val_loss: 0.4884 - val_accuracy: 0.7853\n",
      "Epoch 140/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4897 - accuracy: 0.7911 - val_loss: 0.4958 - val_accuracy: 0.7935\n",
      "Epoch 141/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4926 - accuracy: 0.7929 - val_loss: 0.4935 - val_accuracy: 0.7822\n",
      "Epoch 142/1000\n",
      "228/228 [==============================] - 0s 756us/step - loss: 0.4919 - accuracy: 0.7920 - val_loss: 0.4882 - val_accuracy: 0.7853\n",
      "Epoch 143/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4909 - accuracy: 0.7929 - val_loss: 0.4886 - val_accuracy: 0.7883\n",
      "Epoch 144/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4902 - accuracy: 0.7933 - val_loss: 0.4889 - val_accuracy: 0.7863\n",
      "Epoch 145/1000\n",
      "228/228 [==============================] - 0s 757us/step - loss: 0.4903 - accuracy: 0.7942 - val_loss: 0.4914 - val_accuracy: 0.7863\n",
      "Epoch 146/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4905 - accuracy: 0.7925 - val_loss: 0.4875 - val_accuracy: 0.7863\n",
      "Epoch 147/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4911 - accuracy: 0.7929 - val_loss: 0.4964 - val_accuracy: 0.7812\n",
      "Epoch 148/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4925 - accuracy: 0.7933 - val_loss: 0.4877 - val_accuracy: 0.7924\n",
      "Epoch 149/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4909 - accuracy: 0.7925 - val_loss: 0.4877 - val_accuracy: 0.7853\n",
      "Epoch 150/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4920 - accuracy: 0.7933 - val_loss: 0.4876 - val_accuracy: 0.7853\n",
      "Epoch 151/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4917 - accuracy: 0.7925 - val_loss: 0.4868 - val_accuracy: 0.7904\n",
      "Epoch 152/1000\n",
      "228/228 [==============================] - 0s 762us/step - loss: 0.4899 - accuracy: 0.7973 - val_loss: 0.4894 - val_accuracy: 0.7924\n",
      "Epoch 153/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4906 - accuracy: 0.7968 - val_loss: 0.4888 - val_accuracy: 0.7853\n",
      "Epoch 154/1000\n",
      "228/228 [==============================] - 0s 761us/step - loss: 0.4902 - accuracy: 0.7982 - val_loss: 0.4892 - val_accuracy: 0.7843\n",
      "Epoch 155/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4900 - accuracy: 0.7898 - val_loss: 0.5044 - val_accuracy: 0.7730\n",
      "Epoch 156/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4904 - accuracy: 0.7920 - val_loss: 0.4928 - val_accuracy: 0.7843\n",
      "Epoch 157/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4905 - accuracy: 0.7929 - val_loss: 0.4897 - val_accuracy: 0.7863\n",
      "Epoch 158/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4911 - accuracy: 0.7920 - val_loss: 0.4887 - val_accuracy: 0.7904\n",
      "Epoch 159/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4916 - accuracy: 0.7964 - val_loss: 0.4867 - val_accuracy: 0.7914\n",
      "Epoch 160/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4913 - accuracy: 0.7920 - val_loss: 0.4878 - val_accuracy: 0.7863\n",
      "Epoch 161/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4904 - accuracy: 0.7907 - val_loss: 0.4892 - val_accuracy: 0.7863\n",
      "Epoch 162/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4908 - accuracy: 0.7938 - val_loss: 0.4867 - val_accuracy: 0.7904\n",
      "Epoch 163/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4903 - accuracy: 0.7876 - val_loss: 0.4870 - val_accuracy: 0.7883\n",
      "Epoch 164/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4902 - accuracy: 0.7929 - val_loss: 0.4877 - val_accuracy: 0.7945\n",
      "Epoch 165/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4908 - accuracy: 0.7955 - val_loss: 0.4867 - val_accuracy: 0.7904\n",
      "Epoch 166/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4906 - accuracy: 0.7925 - val_loss: 0.4865 - val_accuracy: 0.7883\n",
      "Epoch 167/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "228/228 [==============================] - 0s 798us/step - loss: 0.4891 - accuracy: 0.7964 - val_loss: 0.4912 - val_accuracy: 0.7832\n",
      "Epoch 168/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4907 - accuracy: 0.7968 - val_loss: 0.4878 - val_accuracy: 0.7832\n",
      "Epoch 169/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4897 - accuracy: 0.7938 - val_loss: 0.4866 - val_accuracy: 0.7873\n",
      "Epoch 170/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4904 - accuracy: 0.7964 - val_loss: 0.4863 - val_accuracy: 0.7986\n",
      "Epoch 171/1000\n",
      "228/228 [==============================] - 0s 807us/step - loss: 0.4909 - accuracy: 0.7986 - val_loss: 0.4865 - val_accuracy: 0.7914\n",
      "Epoch 172/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4897 - accuracy: 0.7925 - val_loss: 0.4888 - val_accuracy: 0.7883\n",
      "Epoch 173/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4897 - accuracy: 0.7982 - val_loss: 0.4892 - val_accuracy: 0.7843\n",
      "Epoch 174/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4905 - accuracy: 0.7946 - val_loss: 0.4869 - val_accuracy: 0.7853\n",
      "Epoch 175/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4904 - accuracy: 0.7916 - val_loss: 0.4872 - val_accuracy: 0.7914\n",
      "Epoch 176/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4906 - accuracy: 0.7925 - val_loss: 0.4916 - val_accuracy: 0.7873\n",
      "Epoch 177/1000\n",
      "228/228 [==============================] - 0s 798us/step - loss: 0.4895 - accuracy: 0.7942 - val_loss: 0.4872 - val_accuracy: 0.7853\n",
      "Epoch 178/1000\n",
      "228/228 [==============================] - 0s 785us/step - loss: 0.4892 - accuracy: 0.7898 - val_loss: 0.4856 - val_accuracy: 0.7945\n",
      "Epoch 179/1000\n",
      "228/228 [==============================] - 0s 789us/step - loss: 0.4898 - accuracy: 0.7925 - val_loss: 0.4867 - val_accuracy: 0.7853\n",
      "Epoch 180/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4895 - accuracy: 0.7960 - val_loss: 0.4863 - val_accuracy: 0.7863\n",
      "Epoch 181/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4889 - accuracy: 0.7920 - val_loss: 0.4903 - val_accuracy: 0.7863\n",
      "Epoch 182/1000\n",
      "228/228 [==============================] - 0s 794us/step - loss: 0.4901 - accuracy: 0.7982 - val_loss: 0.4873 - val_accuracy: 0.7904\n",
      "Epoch 183/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4895 - accuracy: 0.7898 - val_loss: 0.4870 - val_accuracy: 0.7894\n",
      "Epoch 184/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4886 - accuracy: 0.7907 - val_loss: 0.4862 - val_accuracy: 0.7986\n",
      "Epoch 185/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4890 - accuracy: 0.7951 - val_loss: 0.4863 - val_accuracy: 0.7975\n",
      "Epoch 186/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4891 - accuracy: 0.7942 - val_loss: 0.4855 - val_accuracy: 0.7863\n",
      "Epoch 187/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4893 - accuracy: 0.7955 - val_loss: 0.4872 - val_accuracy: 0.7863\n",
      "Epoch 188/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4896 - accuracy: 0.7946 - val_loss: 0.4859 - val_accuracy: 0.7883\n",
      "Epoch 189/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4876 - accuracy: 0.7960 - val_loss: 0.4962 - val_accuracy: 0.7822\n",
      "Epoch 190/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4911 - accuracy: 0.7920 - val_loss: 0.4855 - val_accuracy: 0.7914\n",
      "Epoch 191/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4891 - accuracy: 0.7960 - val_loss: 0.4859 - val_accuracy: 0.7883\n",
      "Epoch 192/1000\n",
      "228/228 [==============================] - 0s 780us/step - loss: 0.4896 - accuracy: 0.7938 - val_loss: 0.4854 - val_accuracy: 0.7914\n",
      "Epoch 193/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4901 - accuracy: 0.7973 - val_loss: 0.4862 - val_accuracy: 0.7894\n",
      "Epoch 194/1000\n",
      "228/228 [==============================] - 0s 776us/step - loss: 0.4900 - accuracy: 0.7933 - val_loss: 0.4883 - val_accuracy: 0.7843\n",
      "Epoch 195/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4897 - accuracy: 0.7933 - val_loss: 0.4855 - val_accuracy: 0.7873\n",
      "Epoch 196/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4890 - accuracy: 0.7907 - val_loss: 0.4864 - val_accuracy: 0.7863\n",
      "Epoch 197/1000\n",
      "228/228 [==============================] - 0s 791us/step - loss: 0.4887 - accuracy: 0.7955 - val_loss: 0.4867 - val_accuracy: 0.7873\n",
      "Epoch 198/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4891 - accuracy: 0.7938 - val_loss: 0.4886 - val_accuracy: 0.7843\n",
      "Epoch 199/1000\n",
      "228/228 [==============================] - 0s 761us/step - loss: 0.4896 - accuracy: 0.7894 - val_loss: 0.4857 - val_accuracy: 0.7863\n",
      "Epoch 200/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4893 - accuracy: 0.7973 - val_loss: 0.4849 - val_accuracy: 0.7883\n",
      "Epoch 201/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4886 - accuracy: 0.7951 - val_loss: 0.4848 - val_accuracy: 0.7883\n",
      "Epoch 202/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4893 - accuracy: 0.7938 - val_loss: 0.4889 - val_accuracy: 0.7863\n",
      "Epoch 203/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4892 - accuracy: 0.7907 - val_loss: 0.4857 - val_accuracy: 0.7894\n",
      "Epoch 204/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4895 - accuracy: 0.7933 - val_loss: 0.4847 - val_accuracy: 0.7914\n",
      "Epoch 205/1000\n",
      "228/228 [==============================] - 0s 753us/step - loss: 0.4886 - accuracy: 0.7929 - val_loss: 0.4850 - val_accuracy: 0.7894\n",
      "Epoch 206/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4892 - accuracy: 0.7933 - val_loss: 0.4857 - val_accuracy: 0.7853\n",
      "Epoch 207/1000\n",
      "228/228 [==============================] - 0s 762us/step - loss: 0.4885 - accuracy: 0.7911 - val_loss: 0.4867 - val_accuracy: 0.7883\n",
      "Epoch 208/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4881 - accuracy: 0.7955 - val_loss: 0.4858 - val_accuracy: 0.7904\n",
      "Epoch 209/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4901 - accuracy: 0.7911 - val_loss: 0.4854 - val_accuracy: 0.7863\n",
      "Epoch 210/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4874 - accuracy: 0.7911 - val_loss: 0.4877 - val_accuracy: 0.7924\n",
      "Epoch 211/1000\n",
      "228/228 [==============================] - 0s 753us/step - loss: 0.4894 - accuracy: 0.7916 - val_loss: 0.4849 - val_accuracy: 0.7873\n",
      "Epoch 212/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4884 - accuracy: 0.7907 - val_loss: 0.4877 - val_accuracy: 0.7853\n",
      "Epoch 213/1000\n",
      "228/228 [==============================] - 0s 759us/step - loss: 0.4891 - accuracy: 0.7968 - val_loss: 0.4856 - val_accuracy: 0.7924\n",
      "Epoch 214/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4886 - accuracy: 0.7907 - val_loss: 0.4851 - val_accuracy: 0.7924\n",
      "Epoch 215/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4894 - accuracy: 0.7916 - val_loss: 0.4850 - val_accuracy: 0.7894\n",
      "Epoch 216/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4882 - accuracy: 0.7946 - val_loss: 0.4863 - val_accuracy: 0.7853\n",
      "Epoch 217/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4885 - accuracy: 0.7907 - val_loss: 0.4844 - val_accuracy: 0.7873\n",
      "Epoch 218/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4881 - accuracy: 0.7951 - val_loss: 0.4865 - val_accuracy: 0.7853\n",
      "Epoch 219/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4885 - accuracy: 0.7920 - val_loss: 0.4862 - val_accuracy: 0.7843\n",
      "Epoch 220/1000\n",
      "228/228 [==============================] - 0s 767us/step - loss: 0.4877 - accuracy: 0.7951 - val_loss: 0.4843 - val_accuracy: 0.7863\n",
      "Epoch 221/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4896 - accuracy: 0.7872 - val_loss: 0.4840 - val_accuracy: 0.7863\n",
      "Epoch 222/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "228/228 [==============================] - 0s 767us/step - loss: 0.4889 - accuracy: 0.7903 - val_loss: 0.4841 - val_accuracy: 0.7863\n",
      "Epoch 223/1000\n",
      "228/228 [==============================] - 0s 772us/step - loss: 0.4866 - accuracy: 0.7951 - val_loss: 0.4859 - val_accuracy: 0.7924\n",
      "Epoch 224/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4881 - accuracy: 0.7907 - val_loss: 0.4845 - val_accuracy: 0.7894\n",
      "Epoch 225/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4879 - accuracy: 0.7933 - val_loss: 0.4891 - val_accuracy: 0.7843\n",
      "Epoch 226/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4883 - accuracy: 0.7929 - val_loss: 0.4852 - val_accuracy: 0.7863\n",
      "Epoch 227/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4877 - accuracy: 0.7933 - val_loss: 0.4844 - val_accuracy: 0.7894\n",
      "Epoch 228/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4885 - accuracy: 0.7920 - val_loss: 0.4846 - val_accuracy: 0.7945\n",
      "Epoch 229/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4879 - accuracy: 0.7933 - val_loss: 0.4844 - val_accuracy: 0.7883\n",
      "Epoch 230/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4875 - accuracy: 0.7925 - val_loss: 0.4845 - val_accuracy: 0.7873\n",
      "Epoch 231/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4888 - accuracy: 0.7929 - val_loss: 0.4847 - val_accuracy: 0.7873\n",
      "Epoch 232/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4901 - accuracy: 0.7951 - val_loss: 0.4851 - val_accuracy: 0.7853\n",
      "Epoch 233/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4872 - accuracy: 0.7938 - val_loss: 0.4841 - val_accuracy: 0.7883\n",
      "Epoch 234/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4879 - accuracy: 0.7938 - val_loss: 0.4841 - val_accuracy: 0.7883\n",
      "Epoch 235/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4882 - accuracy: 0.7920 - val_loss: 0.4868 - val_accuracy: 0.7863\n",
      "Epoch 236/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4876 - accuracy: 0.7986 - val_loss: 0.4851 - val_accuracy: 0.7914\n",
      "Epoch 237/1000\n",
      "228/228 [==============================] - 0s 745us/step - loss: 0.4890 - accuracy: 0.7903 - val_loss: 0.4845 - val_accuracy: 0.7894\n",
      "Epoch 238/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4870 - accuracy: 0.7982 - val_loss: 0.4850 - val_accuracy: 0.7883\n",
      "Epoch 239/1000\n",
      "228/228 [==============================] - 0s 745us/step - loss: 0.4892 - accuracy: 0.7916 - val_loss: 0.4852 - val_accuracy: 0.7883\n",
      "Epoch 240/1000\n",
      "228/228 [==============================] - 0s 750us/step - loss: 0.4881 - accuracy: 0.7946 - val_loss: 0.4857 - val_accuracy: 0.7873\n",
      "Epoch 241/1000\n",
      "228/228 [==============================] - 0s 741us/step - loss: 0.4878 - accuracy: 0.7968 - val_loss: 0.4856 - val_accuracy: 0.7873\n",
      "Epoch 242/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4864 - accuracy: 0.7977 - val_loss: 0.4887 - val_accuracy: 0.7863\n",
      "Epoch 243/1000\n",
      "228/228 [==============================] - 0s 754us/step - loss: 0.4893 - accuracy: 0.7933 - val_loss: 0.4858 - val_accuracy: 0.7843\n",
      "Epoch 244/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4872 - accuracy: 0.7933 - val_loss: 0.4858 - val_accuracy: 0.7853\n",
      "Epoch 245/1000\n",
      "228/228 [==============================] - 0s 763us/step - loss: 0.4876 - accuracy: 0.7942 - val_loss: 0.4883 - val_accuracy: 0.7853\n",
      "Epoch 246/1000\n",
      "228/228 [==============================] - 0s 758us/step - loss: 0.4877 - accuracy: 0.7942 - val_loss: 0.4845 - val_accuracy: 0.7853\n",
      "Epoch 246: early stopping\n"
     ]
    }
   ],
   "source": [
    "#Artificial neural network ,defining its layers\n",
    "ANN_M = Sequential()\n",
    "ANN_M.add(Dense(5, activation = 'relu',input_dim = 4))\n",
    "ANN_M.add(Dense(4, activation = 'relu'))\n",
    "ANN_M.add(Dense(3, activation = 'softmax'))\n",
    "ANN_M.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "history2 = ANN_M.fit(X_train_M,new_y_train_M,epochs=1000, batch_size=10,callbacks=[early_stop],validation_data=(X_test_M,new_y_test_M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b943eedc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(ANN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b744f006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_History(history2,246)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a6ea2cef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 434us/step\n",
      "0.78\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score\n",
    "y_pred_M = ANN_M.predict(X_test_M)\n",
    "y_pred_M = y_pred_M > 0.50\n",
    "print(round(accuracy_score(new_y_test_M,y_pred_M),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93270b35",
   "metadata": {},
   "source": [
    "# THE END "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}