Student Grade Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Acquistion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Dataset (7 sheets).xlsx', sheet_name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>S-I</th>\n",
       "      <th>S-II</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.802817</td>\n",
       "      <td>78.700000</td>\n",
       "      <td>107.142857</td>\n",
       "      <td>56.507143</td>\n",
       "      <td>77.579710</td>\n",
       "      <td>57.388889</td>\n",
       "      <td>11.432857</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>2.065217</td>\n",
       "      <td>3.816176</td>\n",
       "      <td>1.207692</td>\n",
       "      <td>0.942623</td>\n",
       "      <td>3.038462</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.332714</td>\n",
       "      <td>6.140704</td>\n",
       "      <td>4.525352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.514439</td>\n",
       "      <td>20.022524</td>\n",
       "      <td>27.718584</td>\n",
       "      <td>19.490334</td>\n",
       "      <td>25.939072</td>\n",
       "      <td>20.725052</td>\n",
       "      <td>2.062006</td>\n",
       "      <td>2.251248</td>\n",
       "      <td>2.284721</td>\n",
       "      <td>3.060222</td>\n",
       "      <td>2.289399</td>\n",
       "      <td>2.069820</td>\n",
       "      <td>2.574374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.814827</td>\n",
       "      <td>3.248767</td>\n",
       "      <td>3.142292</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.290000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>91.500000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>10.420000</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.250000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>11.755000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>3.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>92.750000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>12.935000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.275000</td>\n",
       "      <td>7.965000</td>\n",
       "      <td>5.525000</td>\n",
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       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "            As:1        As:2        As:3       As:4        As:5       As:6  \\\n",
       "count  71.000000   70.000000   70.000000  70.000000   69.000000  63.000000   \n",
       "mean   41.802817   78.700000  107.142857  56.507143   77.579710  57.388889   \n",
       "std    10.514439   20.022524   27.718584  19.490334   25.939072  20.725052   \n",
       "min     3.000000    3.000000    3.000000   0.000000    0.000000   3.000000   \n",
       "25%    38.250000   72.000000   91.500000  45.750000   65.000000  41.000000   \n",
       "50%    44.000000   82.000000  119.000000  63.500000   80.000000  65.000000   \n",
       "75%    48.000000   92.750000  125.000000  70.000000   95.000000  75.000000   \n",
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       "\n",
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       "count  70.000000  70.000000  69.000000  68.000000  65.000000  61.000000   \n",
       "mean   11.432857   3.800000   2.065217   3.816176   1.207692   0.942623   \n",
       "std     2.062006   2.251248   2.284721   3.060222   2.289399   2.069820   \n",
       "min     5.290000   0.500000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%    10.420000   2.125000   0.000000   1.000000   0.000000   0.000000   \n",
       "50%    11.755000   3.250000   2.000000   3.000000   0.000000   0.000000   \n",
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       "\n",
       "            Qz:6  Qz:7         Qz        S-I       S-II  \n",
       "count  65.000000  60.0  70.000000  71.000000  71.000000  \n",
       "mean    3.038462   2.0   4.332714   6.140704   4.525352  \n",
       "std     2.574374   0.0   1.814827   3.248767   3.142292  \n",
       "min     0.000000   2.0   0.800000   0.370000   0.370000  \n",
       "25%     1.000000   2.0   3.200000   3.750000   2.250000  \n",
       "50%     3.000000   2.0   4.090000   5.430000   3.930000  \n",
       "75%     5.000000   2.0   5.275000   7.965000   5.525000  \n",
       "max    10.000000   2.0  10.000000  15.000000  15.000000  "
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.658537</td>\n",
       "      <td>66.341463</td>\n",
       "      <td>107.32500</td>\n",
       "      <td>111.780488</td>\n",
       "      <td>83.815789</td>\n",
       "      <td>63.300000</td>\n",
       "      <td>11.825750</td>\n",
       "      <td>6.475000</td>\n",
       "      <td>9.052632</td>\n",
       "      <td>4.128571</td>\n",
       "      <td>6.175676</td>\n",
       "      <td>6.837838</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.407250</td>\n",
       "      <td>6.844000</td>\n",
       "      <td>6.432250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.949448</td>\n",
       "      <td>16.902085</td>\n",
       "      <td>22.64145</td>\n",
       "      <td>22.527219</td>\n",
       "      <td>22.988664</td>\n",
       "      <td>22.399863</td>\n",
       "      <td>1.444073</td>\n",
       "      <td>4.723034</td>\n",
       "      <td>4.602863</td>\n",
       "      <td>2.758257</td>\n",
       "      <td>2.614639</td>\n",
       "      <td>2.101765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.385501</td>\n",
       "      <td>2.470872</td>\n",
       "      <td>2.620356</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>1.500000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>100.50000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>10.917500</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.775000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>4.680000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>110.50000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>11.740000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>6.180000</td>\n",
       "      <td>6.090000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>118.25000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>98.250000</td>\n",
       "      <td>82.250000</td>\n",
       "      <td>12.825000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>7.965000</td>\n",
       "      <td>8.060000</td>\n",
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       "      <td>120.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>140.00000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "             As:1        As:2       As:3        As:4        As:5       As:6  \\\n",
       "count   41.000000   41.000000   40.00000   41.000000   38.000000  40.000000   \n",
       "mean    89.658537   66.341463  107.32500  111.780488   83.815789  63.300000   \n",
       "std     19.949448   16.902085   22.64145   22.527219   22.988664  22.399863   \n",
       "min      3.000000    3.000000    3.00000    3.000000    3.000000   0.000000   \n",
       "25%     88.000000   59.000000  100.50000  106.000000   72.250000  52.500000   \n",
       "50%     94.000000   68.000000  110.50000  118.000000   86.500000  69.500000   \n",
       "75%    100.000000   77.000000  118.25000  125.000000   98.250000  82.250000   \n",
       "max    120.000000  100.000000  140.00000  130.000000  120.000000  90.000000   \n",
       "\n",
       "              As       Qz:1       Qz:2       Qz:3       Qz:4       Qz:5  Qz:6  \\\n",
       "count  40.000000  40.000000  38.000000  35.000000  37.000000  37.000000  23.0   \n",
       "mean   11.825750   6.475000   9.052632   4.128571   6.175676   6.837838   2.0   \n",
       "std     1.444073   4.723034   4.602863   2.758257   2.614639   2.101765   0.0   \n",
       "min     9.050000   2.000000   2.000000   0.000000   1.000000   2.000000   2.0   \n",
       "25%    10.917500   3.750000   6.000000   2.000000   4.000000   6.000000   2.0   \n",
       "50%    11.740000   5.000000   8.000000   3.000000   6.000000   8.000000   2.0   \n",
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       "max    15.000000  20.000000  20.000000  10.000000  10.000000  10.000000   2.0   \n",
       "\n",
       "              Qz        S-I       S-II  \n",
       "count  40.000000  40.000000  40.000000  \n",
       "mean    5.407250   6.844000   6.432250  \n",
       "std     2.385501   2.470872   2.620356  \n",
       "min     0.200000   3.180000   1.500000  \n",
       "25%     3.775000   5.250000   4.680000  \n",
       "50%     5.750000   6.180000   6.090000  \n",
       "75%     6.900000   7.965000   8.060000  \n",
       "max    10.000000  15.000000  15.000000  "
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       "      <th>S-II</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.214286</td>\n",
       "      <td>42.790698</td>\n",
       "      <td>79.902439</td>\n",
       "      <td>56.302326</td>\n",
       "      <td>82.325000</td>\n",
       "      <td>64.075000</td>\n",
       "      <td>10.915952</td>\n",
       "      <td>13.268293</td>\n",
       "      <td>15.197674</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>1.763158</td>\n",
       "      <td>19.027027</td>\n",
       "      <td>2.594595</td>\n",
       "      <td>1.023256</td>\n",
       "      <td>5.906190</td>\n",
       "      <td>5.678810</td>\n",
       "      <td>5.786667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.455820</td>\n",
       "      <td>20.471302</td>\n",
       "      <td>26.044966</td>\n",
       "      <td>22.254250</td>\n",
       "      <td>34.123747</td>\n",
       "      <td>21.143451</td>\n",
       "      <td>2.855111</td>\n",
       "      <td>4.432970</td>\n",
       "      <td>9.237050</td>\n",
       "      <td>5.563017</td>\n",
       "      <td>1.543718</td>\n",
       "      <td>0.786171</td>\n",
       "      <td>11.849722</td>\n",
       "      <td>1.892374</td>\n",
       "      <td>0.152499</td>\n",
       "      <td>1.439907</td>\n",
       "      <td>2.425705</td>\n",
       "      <td>3.164942</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.820000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>0.750000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>10.042500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.757500</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.227500</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.250000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.945000</td>\n",
       "      <td>5.715000</td>\n",
       "      <td>6.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>81.750000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>13.232500</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.955000</td>\n",
       "      <td>6.655000</td>\n",
       "      <td>7.072500</td>\n",
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       "      <td>100.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "             As:1       As:2        As:3       As:4        As:5       As:6  \\\n",
       "count   42.000000  43.000000   41.000000  43.000000   40.000000  40.000000   \n",
       "mean    64.214286  42.790698   79.902439  56.302326   82.325000  64.075000   \n",
       "std     24.455820  20.471302   26.044966  22.254250   34.123747  21.143451   \n",
       "min      0.000000   0.000000    3.000000   0.000000    3.000000   3.000000   \n",
       "25%     58.000000  33.500000   70.000000  45.750000   60.000000  53.750000   \n",
       "50%     65.250000  50.000000   89.000000  61.000000   85.000000  70.000000   \n",
       "75%     81.750000  57.000000  100.000000  74.000000  110.000000  80.000000   \n",
       "max    100.000000  75.000000  105.000000  90.000000  120.000000  80.000000   \n",
       "\n",
       "              As       Qz:1       Qz:2       Qz:3       Qz:4       Qz:5  \\\n",
       "count  42.000000  41.000000  43.000000  42.000000  32.000000  38.000000   \n",
       "mean   10.915952  13.268293  15.197674   5.833333   1.937500   1.763158   \n",
       "std     2.855111   4.432970   9.237050   5.563017   1.543718   0.786171   \n",
       "min     2.820000   2.000000   0.000000   0.000000   0.000000   1.000000   \n",
       "25%    10.042500  11.000000   7.000000   1.000000   0.750000   1.000000   \n",
       "50%    10.810000  12.000000  16.000000   4.000000   2.000000   2.000000   \n",
       "75%    13.232500  15.000000  23.000000   9.500000   3.000000   2.000000   \n",
       "max    15.000000  30.000000  40.000000  20.000000   6.000000   3.000000   \n",
       "\n",
       "            Qz:6       Qz:7       Qz:8         Qz        S-I       S-II  \n",
       "count  37.000000  37.000000  43.000000  42.000000  42.000000  42.000000  \n",
       "mean   19.027027   2.594595   1.023256   5.906190   5.678810   5.786667  \n",
       "std    11.849722   1.892374   0.152499   1.439907   2.425705   3.164942  \n",
       "min     0.000000   0.000000   1.000000   3.350000   2.060000   0.750000  \n",
       "25%     9.000000   2.000000   1.000000   4.757500   3.700000   3.227500  \n",
       "50%    17.000000   2.000000   1.000000   5.945000   5.715000   6.090000  \n",
       "75%    29.000000   2.000000   1.000000   6.955000   6.655000   7.072500  \n",
       "max    45.000000  10.000000   2.000000  10.000000  15.000000  15.000000  "
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.00000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.428571</td>\n",
       "      <td>36.171429</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>36.367647</td>\n",
       "      <td>20.757143</td>\n",
       "      <td>21.595588</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>11.571515</td>\n",
       "      <td>5.439394</td>\n",
       "      <td>12.661765</td>\n",
       "      <td>5.55000</td>\n",
       "      <td>9.057143</td>\n",
       "      <td>4.914286</td>\n",
       "      <td>6.886471</td>\n",
       "      <td>5.638182</td>\n",
       "      <td>5.962647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.953431</td>\n",
       "      <td>17.512157</td>\n",
       "      <td>30.661866</td>\n",
       "      <td>22.743400</td>\n",
       "      <td>11.150951</td>\n",
       "      <td>13.546359</td>\n",
       "      <td>19.948382</td>\n",
       "      <td>2.696639</td>\n",
       "      <td>1.699069</td>\n",
       "      <td>4.003257</td>\n",
       "      <td>2.80392</td>\n",
       "      <td>1.474147</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.970841</td>\n",
       "      <td>2.277470</td>\n",
       "      <td>3.034608</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.620000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.290000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>1.310000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.625000</td>\n",
       "      <td>3.72500</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.472500</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>3.652500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>12.310000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.10000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>5.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>92.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>84.250000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>13.390000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.97500</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.422500</td>\n",
       "      <td>6.560000</td>\n",
       "      <td>7.635000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "             As:1       As:2        As:3       As:4       As:5       As:6  \\\n",
       "count   35.000000  35.000000   35.000000  34.000000  35.000000  34.000000   \n",
       "mean    64.428571  36.171429   64.800000  36.367647  20.757143  21.595588   \n",
       "std     38.953431  17.512157   30.661866  22.743400  11.150951  13.546359   \n",
       "min      0.000000   0.000000    0.000000   0.000000   0.000000   0.000000   \n",
       "25%     36.000000  32.500000   55.500000  12.000000  21.000000   7.000000   \n",
       "50%     82.000000  44.000000   74.000000  48.000000  24.000000  27.750000   \n",
       "75%     92.500000  48.000000   84.250000  52.500000  28.000000  32.000000   \n",
       "max    100.000000  50.000000  101.000000  60.000000  35.000000  35.000000   \n",
       "\n",
       "            As:7         As       Qz:1       Qz:2      Qz:3       Qz:4  \\\n",
       "count  30.000000  33.000000  33.000000  34.000000  34.00000  35.000000   \n",
       "mean   22.600000  11.571515   5.439394  12.661765   5.55000   9.057143   \n",
       "std    19.948382   2.696639   1.699069   4.003257   2.80392   1.474147   \n",
       "min     0.000000   4.620000   1.500000   2.000000   1.00000   2.000000   \n",
       "25%     0.000000   9.840000   5.000000  10.625000   3.72500   8.500000   \n",
       "50%    31.500000  12.310000   5.500000  14.000000   5.10000   9.000000   \n",
       "75%    43.000000  13.390000   6.500000  15.000000   6.97500  10.000000   \n",
       "max    45.000000  15.000000  10.000000  20.000000  15.00000  10.000000   \n",
       "\n",
       "            Qz:5         Qz        S-I       S-II  \n",
       "count  35.000000  34.000000  33.000000  34.000000  \n",
       "mean    4.914286   6.886471   5.638182   5.962647  \n",
       "std     0.507093   0.970841   2.277470   3.034608  \n",
       "min     2.000000   4.290000   1.680000   1.310000  \n",
       "25%     5.000000   6.472500   4.120000   3.652500  \n",
       "50%     5.000000   6.950000   5.430000   5.810000  \n",
       "75%     5.000000   7.422500   6.560000   7.635000  \n",
       "max     5.000000  10.000000  15.000000  15.000000  "
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.413793</td>\n",
       "      <td>49.266667</td>\n",
       "      <td>65.793103</td>\n",
       "      <td>33.253333</td>\n",
       "      <td>87.275862</td>\n",
       "      <td>58.230769</td>\n",
       "      <td>10.894483</td>\n",
       "      <td>2.716667</td>\n",
       "      <td>5.807692</td>\n",
       "      <td>1.592593</td>\n",
       "      <td>4.566667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.629630</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>6.393448</td>\n",
       "      <td>5.353214</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.530413</td>\n",
       "      <td>23.722437</td>\n",
       "      <td>26.763033</td>\n",
       "      <td>12.399436</td>\n",
       "      <td>29.036795</td>\n",
       "      <td>17.900408</td>\n",
       "      <td>2.627474</td>\n",
       "      <td>1.304391</td>\n",
       "      <td>2.623269</td>\n",
       "      <td>2.620324</td>\n",
       "      <td>4.005886</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1.626657</td>\n",
       "      <td>2.942788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.009047</td>\n",
       "      <td>3.268672</td>\n",
       "      <td>3.537589</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.460000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.640000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>11.060000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.530000</td>\n",
       "      <td>4.310000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>64.750000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>12.980000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>7.637500</td>\n",
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       "      <td>70.000000</td>\n",
       "      <td>100.000000</td>\n",
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       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "             As:1       As:2        As:3       As:4        As:5       As:6  \\\n",
       "count   29.000000  30.000000   29.000000  30.000000   29.000000  26.000000   \n",
       "mean    60.413793  49.266667   65.793103  33.253333   87.275862  58.230769   \n",
       "std     50.530413  23.722437   26.763033  12.399436   29.036795  17.900408   \n",
       "min      0.000000   0.000000    3.000000   3.000000    3.000000   3.000000   \n",
       "25%      0.000000  50.500000   46.000000  24.000000   74.000000  51.750000   \n",
       "50%     77.000000  57.000000   71.000000  34.000000   89.000000  57.000000   \n",
       "75%     97.000000  64.750000   83.000000  45.000000  114.000000  72.500000   \n",
       "max    130.000000  70.000000  100.000000  50.000000  120.000000  80.000000   \n",
       "\n",
       "              As       Qz:1       Qz:2       Qz:3       Qz:4       Qz:5  \\\n",
       "count  29.000000  30.000000  26.000000  27.000000  30.000000  27.000000   \n",
       "mean   10.894483   2.716667   5.807692   1.592593   4.566667   3.000000   \n",
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       "min     4.000000   1.000000   2.000000   0.000000   0.000000   0.000000   \n",
       "25%     9.710000   2.000000   4.250000   0.000000   2.000000   0.500000   \n",
       "50%    11.060000   2.000000   5.000000   1.000000   2.000000   2.000000   \n",
       "75%    12.980000   3.500000   6.750000   2.000000  10.000000   3.500000   \n",
       "max    15.000000   5.000000  15.000000  10.000000  10.000000  10.000000   \n",
       "\n",
       "            Qz:6       Qz:7  Qz:8         Qz        S-I       S-II  \n",
       "count  27.000000  26.000000  29.0  29.000000  29.000000  28.000000  \n",
       "mean    1.629630   8.500000   2.0   6.410000   6.393448   5.353214  \n",
       "std     1.626657   2.942788   0.0   2.009047   3.268672   3.537589  \n",
       "min     0.000000   2.000000   2.0   1.200000   0.930000   0.460000  \n",
       "25%     0.000000   6.000000   2.0   5.130000   4.500000   2.640000  \n",
       "50%     1.500000   8.000000   2.0   5.900000   5.530000   4.310000  \n",
       "75%     2.000000  12.000000   2.0   7.900000   8.250000   7.637500  \n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>82.705882</td>\n",
       "      <td>79.939394</td>\n",
       "      <td>95.030303</td>\n",
       "      <td>31.406250</td>\n",
       "      <td>62.818182</td>\n",
       "      <td>67.300000</td>\n",
       "      <td>10.736364</td>\n",
       "      <td>6.433333</td>\n",
       "      <td>2.454545</td>\n",
       "      <td>1.612903</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.206897</td>\n",
       "      <td>10.172414</td>\n",
       "      <td>5.776471</td>\n",
       "      <td>4.837941</td>\n",
       "      <td>4.135000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.826097</td>\n",
       "      <td>19.926332</td>\n",
       "      <td>27.576581</td>\n",
       "      <td>15.853888</td>\n",
       "      <td>32.517740</td>\n",
       "      <td>20.524418</td>\n",
       "      <td>2.722448</td>\n",
       "      <td>2.935318</td>\n",
       "      <td>2.302728</td>\n",
       "      <td>1.927692</td>\n",
       "      <td>3.296811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.852058</td>\n",
       "      <td>4.367649</td>\n",
       "      <td>1.943218</td>\n",
       "      <td>2.169696</td>\n",
       "      <td>2.890607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.837500</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>2.130000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>11.390000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>12.590000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.975000</td>\n",
       "      <td>5.505000</td>\n",
       "      <td>4.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "             As:1        As:2        As:3       As:4        As:5       As:6  \\\n",
       "count   34.000000   33.000000   33.000000  32.000000   33.000000  30.000000   \n",
       "mean    82.705882   79.939394   95.030303  31.406250   62.818182  67.300000   \n",
       "std     24.826097   19.926332   27.576581  15.853888   32.517740  20.524418   \n",
       "min      3.000000    3.000000    3.000000   0.000000    0.000000   3.000000   \n",
       "25%     69.250000   80.000000   90.000000  17.000000   43.000000  55.000000   \n",
       "50%     87.000000   85.000000  100.000000  35.500000   68.000000  73.000000   \n",
       "75%    102.000000   90.000000  110.000000  44.000000   85.000000  83.750000   \n",
       "max    120.000000  100.000000  140.000000  50.000000  120.000000  90.000000   \n",
       "\n",
       "              As       Qz:1       Qz:2       Qz:3       Qz:4  Qz:5       Qz:6  \\\n",
       "count  33.000000  30.000000  33.000000  31.000000  30.000000  33.0  29.000000   \n",
       "mean   10.736364   6.433333   2.454545   1.612903   2.900000   2.0   6.206897   \n",
       "std     2.722448   2.935318   2.302728   1.927692   3.296811   0.0   2.852058   \n",
       "min     2.040000   0.000000   0.000000   0.250000   0.000000   2.0   0.000000   \n",
       "25%     9.550000   5.250000   1.000000   0.500000   0.000000   2.0   4.500000   \n",
       "50%    11.390000   7.000000   2.000000   1.000000   2.000000   2.0   7.000000   \n",
       "75%    12.590000   8.500000   3.500000   2.125000   5.250000   2.0   8.500000   \n",
       "max    15.000000  10.000000  10.000000  10.000000  10.000000   2.0  10.000000   \n",
       "\n",
       "            Qz:7         Qz        S-I       S-II  \n",
       "count  29.000000  34.000000  34.000000  34.000000  \n",
       "mean   10.172414   5.776471   4.837941   4.135000  \n",
       "std     4.367649   1.943218   2.169696   2.890607  \n",
       "min     2.000000   1.400000   2.150000   0.560000  \n",
       "25%     7.000000   4.837500   3.930000   2.130000  \n",
       "50%    10.000000   5.850000   4.500000   3.325000  \n",
       "75%    13.000000   6.975000   5.505000   4.947500  \n",
       "max    20.000000  10.000000  15.000000  15.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As:1</th>\n",
       "      <th>As:2</th>\n",
       "      <th>As:3</th>\n",
       "      <th>As:4</th>\n",
       "      <th>As:5</th>\n",
       "      <th>As:6</th>\n",
       "      <th>As</th>\n",
       "      <th>Qz:1</th>\n",
       "      <th>Qz:2</th>\n",
       "      <th>Qz:3</th>\n",
       "      <th>Qz:4</th>\n",
       "      <th>Qz:5</th>\n",
       "      <th>Qz:6</th>\n",
       "      <th>Qz:7</th>\n",
       "      <th>Qz:8</th>\n",
       "      <th>Qz</th>\n",
       "      <th>S-I</th>\n",
       "      <th>S-II</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>81.583333</td>\n",
       "      <td>44.097222</td>\n",
       "      <td>52.694444</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>80.088235</td>\n",
       "      <td>69.031250</td>\n",
       "      <td>11.116857</td>\n",
       "      <td>3.277778</td>\n",
       "      <td>6.185714</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.291667</td>\n",
       "      <td>3.542857</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>5.757576</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.778000</td>\n",
       "      <td>6.330857</td>\n",
       "      <td>4.557429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.869391</td>\n",
       "      <td>24.885400</td>\n",
       "      <td>29.810180</td>\n",
       "      <td>11.593718</td>\n",
       "      <td>24.498136</td>\n",
       "      <td>20.271121</td>\n",
       "      <td>2.417817</td>\n",
       "      <td>1.461137</td>\n",
       "      <td>2.910644</td>\n",
       "      <td>3.625308</td>\n",
       "      <td>4.427794</td>\n",
       "      <td>3.149163</td>\n",
       "      <td>1.362547</td>\n",
       "      <td>3.335700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.218155</td>\n",
       "      <td>2.594403</td>\n",
       "      <td>3.119882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.875000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>39.375000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>62.875000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>9.955000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>4.590000</td>\n",
       "      <td>2.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>60.750000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>82.750000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>11.060000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>4.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.250000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>76.375000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>86.250000</td>\n",
       "      <td>13.090000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.265000</td>\n",
       "      <td>7.215000</td>\n",
       "      <td>6.465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             As:1       As:2       As:3       As:4        As:5       As:6  \\\n",
       "count   36.000000  36.000000  36.000000  36.000000   34.000000  32.000000   \n",
       "mean    81.583333  44.097222  52.694444  36.500000   80.088235  69.031250   \n",
       "std     40.869391  24.885400  29.810180  11.593718   24.498136  20.271121   \n",
       "min      0.000000   0.000000   0.000000   3.000000    3.000000   3.000000   \n",
       "25%     69.875000  41.500000  39.375000  29.500000   62.875000  53.750000   \n",
       "50%     99.000000  52.750000  60.750000  38.250000   82.750000  72.500000   \n",
       "75%    108.250000  62.000000  76.375000  47.000000   93.750000  86.250000   \n",
       "max    130.000000  70.000000  90.000000  50.000000  120.000000  90.000000   \n",
       "\n",
       "              As       Qz:1       Qz:2       Qz:3       Qz:4       Qz:5  \\\n",
       "count  35.000000  36.000000  35.000000  36.000000  36.000000  35.000000   \n",
       "mean   11.116857   3.277778   6.185714   3.000000   3.291667   3.542857   \n",
       "std     2.417817   1.461137   2.910644   3.625308   4.427794   3.149163   \n",
       "min     4.800000   0.000000   2.000000   0.000000   0.000000   0.000000   \n",
       "25%     9.955000   2.500000   4.250000   0.500000   0.000000   1.000000   \n",
       "50%    11.060000   3.500000   6.000000   1.250000   1.000000   3.000000   \n",
       "75%    13.090000   4.500000   8.500000   4.375000  10.000000   5.750000   \n",
       "max    15.000000   5.000000  15.000000  10.000000  10.000000  10.000000   \n",
       "\n",
       "            Qz:6       Qz:7  Qz:8         Qz        S-I       S-II  \n",
       "count  33.000000  33.000000  33.0  35.000000  35.000000  35.000000  \n",
       "mean    3.181818   5.757576   2.0   6.778000   6.330857   4.557429  \n",
       "std     1.362547   3.335700   0.0   2.218155   2.594403   3.119882  \n",
       "min     0.000000   1.000000   2.0   2.260000   2.430000   0.750000  \n",
       "25%     2.000000   3.000000   2.0   5.130000   4.590000   2.010000  \n",
       "50%     3.500000   5.000000   2.0   7.100000   5.620000   4.120000  \n",
       "75%     4.000000   9.000000   2.0   8.265000   7.215000   6.465000  \n",
       "max     5.000000  12.000000   2.0  10.000000  15.000000  15.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for key in df.keys():\n",
    " display(df[key].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 72 entries, 0 to 71\n",
      "Data columns (total 19 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  72 non-null     object \n",
      " 1   As:1        71 non-null     float64\n",
      " 2   As:2        70 non-null     float64\n",
      " 3   As:3        70 non-null     float64\n",
      " 4   As:4        70 non-null     float64\n",
      " 5   As:5        69 non-null     float64\n",
      " 6   As:6        63 non-null     float64\n",
      " 7   As          70 non-null     float64\n",
      " 8   Qz:1        70 non-null     float64\n",
      " 9   Qz:2        69 non-null     float64\n",
      " 10  Qz:3        68 non-null     float64\n",
      " 11  Qz:4        65 non-null     float64\n",
      " 12  Qz:5        61 non-null     float64\n",
      " 13  Qz:6        65 non-null     float64\n",
      " 14  Qz:7        60 non-null     float64\n",
      " 15  Qz          70 non-null     float64\n",
      " 16  S-I         71 non-null     float64\n",
      " 17  S-II        71 non-null     float64\n",
      " 18  Grade       69 non-null     object \n",
      "dtypes: float64(17), object(2)\n",
      "memory usage: 10.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42 entries, 0 to 41\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  42 non-null     object \n",
      " 1   As:1        41 non-null     float64\n",
      " 2   As:2        41 non-null     float64\n",
      " 3   As:3        40 non-null     float64\n",
      " 4   As:4        41 non-null     float64\n",
      " 5   As:5        38 non-null     float64\n",
      " 6   As:6        40 non-null     float64\n",
      " 7   As          40 non-null     float64\n",
      " 8   Qz:1        40 non-null     float64\n",
      " 9   Qz:2        38 non-null     float64\n",
      " 10  Qz:3        35 non-null     float64\n",
      " 11  Qz:4        37 non-null     float64\n",
      " 12  Qz:5        37 non-null     float64\n",
      " 13  Qz:6        23 non-null     float64\n",
      " 14  Qz          40 non-null     float64\n",
      " 15  S-I         40 non-null     float64\n",
      " 16  S-II        40 non-null     float64\n",
      " 17  Grade       39 non-null     object \n",
      "dtypes: float64(16), object(2)\n",
      "memory usage: 6.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44 entries, 0 to 43\n",
      "Data columns (total 20 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  44 non-null     object \n",
      " 1   As:1        42 non-null     float64\n",
      " 2   As:2        43 non-null     float64\n",
      " 3   As:3        41 non-null     float64\n",
      " 4   As:4        43 non-null     float64\n",
      " 5   As:5        40 non-null     float64\n",
      " 6   As:6        40 non-null     float64\n",
      " 7   As          42 non-null     float64\n",
      " 8   Qz:1        41 non-null     float64\n",
      " 9   Qz:2        43 non-null     float64\n",
      " 10  Qz:3        42 non-null     float64\n",
      " 11  Qz:4        32 non-null     float64\n",
      " 12  Qz:5        38 non-null     float64\n",
      " 13  Qz:6        37 non-null     float64\n",
      " 14  Qz:7        37 non-null     float64\n",
      " 15  Qz:8        43 non-null     float64\n",
      " 16  Qz          42 non-null     float64\n",
      " 17  S-I         42 non-null     float64\n",
      " 18  S-II        42 non-null     float64\n",
      " 19  Grade       41 non-null     object \n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 7.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  36 non-null     object \n",
      " 1   As:1        35 non-null     float64\n",
      " 2   As:2        35 non-null     float64\n",
      " 3   As:3        35 non-null     float64\n",
      " 4   As:4        34 non-null     float64\n",
      " 5   As:5        35 non-null     float64\n",
      " 6   As:6        34 non-null     float64\n",
      " 7   As:7        30 non-null     float64\n",
      " 8   As          33 non-null     float64\n",
      " 9   Qz:1        33 non-null     float64\n",
      " 10  Qz:2        34 non-null     float64\n",
      " 11  Qz:3        34 non-null     float64\n",
      " 12  Qz:4        35 non-null     float64\n",
      " 13  Qz:5        35 non-null     float64\n",
      " 14  Qz          34 non-null     float64\n",
      " 15  S-I         33 non-null     float64\n",
      " 16  S-II        34 non-null     float64\n",
      " 17  Grade       33 non-null     object \n",
      "dtypes: float64(16), object(2)\n",
      "memory usage: 5.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31 entries, 0 to 30\n",
      "Data columns (total 20 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  31 non-null     object \n",
      " 1   As:1        29 non-null     float64\n",
      " 2   As:2        30 non-null     float64\n",
      " 3   As:3        29 non-null     float64\n",
      " 4   As:4        30 non-null     float64\n",
      " 5   As:5        29 non-null     float64\n",
      " 6   As:6        26 non-null     float64\n",
      " 7   As          29 non-null     float64\n",
      " 8   Qz:1        30 non-null     float64\n",
      " 9   Qz:2        26 non-null     float64\n",
      " 10  Qz:3        27 non-null     float64\n",
      " 11  Qz:4        30 non-null     float64\n",
      " 12  Qz:5        27 non-null     float64\n",
      " 13  Qz:6        27 non-null     float64\n",
      " 14  Qz:7        26 non-null     float64\n",
      " 15  Qz:8        29 non-null     float64\n",
      " 16  Qz          29 non-null     float64\n",
      " 17  S-I         29 non-null     float64\n",
      " 18  S-II        28 non-null     float64\n",
      " 19  Grade       28 non-null     object \n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 5.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 19 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  36 non-null     object \n",
      " 1   As:1        34 non-null     float64\n",
      " 2   As:2        33 non-null     float64\n",
      " 3   As:3        33 non-null     float64\n",
      " 4   As:4        32 non-null     float64\n",
      " 5   As:5        33 non-null     float64\n",
      " 6   As:6        30 non-null     float64\n",
      " 7   As          33 non-null     float64\n",
      " 8   Qz:1        30 non-null     float64\n",
      " 9   Qz:2        33 non-null     float64\n",
      " 10  Qz:3        31 non-null     float64\n",
      " 11  Qz:4        30 non-null     float64\n",
      " 12  Qz:5        33 non-null     float64\n",
      " 13  Qz:6        29 non-null     float64\n",
      " 14  Qz:7        29 non-null     float64\n",
      " 15  Qz          34 non-null     float64\n",
      " 16  S-I         34 non-null     float64\n",
      " 17  S-II        34 non-null     float64\n",
      " 18  Grade       33 non-null     object \n",
      "dtypes: float64(17), object(2)\n",
      "memory usage: 5.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37 entries, 0 to 36\n",
      "Data columns (total 20 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  37 non-null     object \n",
      " 1   As:1        36 non-null     float64\n",
      " 2   As:2        36 non-null     float64\n",
      " 3   As:3        36 non-null     float64\n",
      " 4   As:4        36 non-null     float64\n",
      " 5   As:5        34 non-null     float64\n",
      " 6   As:6        32 non-null     float64\n",
      " 7   As          35 non-null     float64\n",
      " 8   Qz:1        36 non-null     float64\n",
      " 9   Qz:2        35 non-null     float64\n",
      " 10  Qz:3        36 non-null     float64\n",
      " 11  Qz:4        36 non-null     float64\n",
      " 12  Qz:5        35 non-null     float64\n",
      " 13  Qz:6        33 non-null     float64\n",
      " 14  Qz:7        33 non-null     float64\n",
      " 15  Qz:8        33 non-null     float64\n",
      " 16  Qz          35 non-null     float64\n",
      " 17  S-I         35 non-null     float64\n",
      " 18  S-II        35 non-null     float64\n",
      " 19  Grade       34 non-null     object \n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 5.9+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for key in df.keys():\n",
    " display(df[key].info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>As:1</th>\n",
       "      <th>As:2</th>\n",
       "      <th>As:3</th>\n",
       "      <th>As:4</th>\n",
       "      <th>As:5</th>\n",
       "      <th>As:6</th>\n",
       "      <th>As:7</th>\n",
       "      <th>Qz:1</th>\n",
       "      <th>Qz:2</th>\n",
       "      <th>Qz:3</th>\n",
       "      <th>Qz:4</th>\n",
       "      <th>Qz:5</th>\n",
       "      <th>Qz:6</th>\n",
       "      <th>Qz:7</th>\n",
       "      <th>Qz:8</th>\n",
       "      <th>S-I</th>\n",
       "      <th>S-II</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.975000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>8.62</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>1.992857</td>\n",
       "      <td>1.21875</td>\n",
       "      <td>1.875</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.93</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.125000</td>\n",
       "      <td>1.890000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.32500</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>0.93</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>2.62500</td>\n",
       "      <td>1.750</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.150000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>2.750</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2.25</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2.330769</td>\n",
       "      <td>2.121429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.85000</td>\n",
       "      <td>2.200</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1.430769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>2.175</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1.938462</td>\n",
       "      <td>2.185714</td>\n",
       "      <td>1.483333</td>\n",
       "      <td>1.68000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2.884615</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.43000</td>\n",
       "      <td>2.750</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.06</td>\n",
       "      <td>4.31</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2.238462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.46000</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>277 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         As:1      As:2      As:3     As:4   As:5      As:6  As:7  Qz:1  \\\n",
       "0    1.975000  2.700000  2.571429  3.00000  2.125  2.812500   0.0   1.5   \n",
       "1    2.000000  1.860000  1.992857  1.21875  1.875  2.850000   0.0   0.3   \n",
       "2    2.125000  1.890000  2.571429  2.32500  1.625  1.875000   0.0   0.0   \n",
       "3    1.025000  1.260000  1.285714  2.62500  1.750  0.375000   0.0   0.2   \n",
       "4    2.150000  1.950000  2.678571  0.37500  2.750  0.937500   0.0   0.6   \n",
       "..        ...       ...       ...      ...    ...       ...   ...   ...   \n",
       "272  2.330769  2.121429  0.000000  2.85000  2.200  1.533333   0.0   1.2   \n",
       "273  1.430769  0.000000  1.900000  1.80000  2.175  2.233333   0.0   0.0   \n",
       "274  1.938462  2.185714  1.483333  1.68000  2.000  1.600000   0.0   0.8   \n",
       "275  2.884615  2.571429  2.800000  2.43000  2.750  2.866667   0.0   1.8   \n",
       "276  2.238462  0.000000  1.666667  2.46000  1.800  0.000000   0.0   0.0   \n",
       "\n",
       "         Qz:2  Qz:3  Qz:4  Qz:5  Qz:6      Qz:7  Qz:8   S-I  S-II Grade  \n",
       "0    0.900000   0.9   0.0   0.2   1.0  0.000000   0.0  9.75  8.62  Pass  \n",
       "1    0.000000   0.1   0.0   0.2   0.4  0.000000   0.0  3.37  3.93  Fail  \n",
       "2    0.000000   0.2   0.0   0.2   0.0  2.000000   0.0  6.56  0.93  Fail  \n",
       "3    0.400000   0.0   0.0   0.0   0.0  2.000000   0.0  5.06  2.81  Fail  \n",
       "4    0.200000   0.0   0.0   0.0   0.0  0.000000   0.0  4.50  2.25  Fail  \n",
       "..        ...   ...   ...   ...   ...       ...   ...   ...   ...   ...  \n",
       "272  1.133333   0.1   0.0   0.2   0.8  0.333333   2.0  6.56  2.90  Fail  \n",
       "273  1.333333   0.0   0.2   0.6   0.0  1.000000   2.0  3.37  1.59  Fail  \n",
       "274  0.933333   0.2   0.0   1.8   1.2  1.166667   2.0  5.25  1.50  Fail  \n",
       "275  0.800000   0.0   0.0   0.6   1.0  0.333333   2.0  8.06  4.31  Pass  \n",
       "276  0.600000   0.0   0.0   0.6   0.0  0.000000   2.0  4.40  1.40  Fail  \n",
       "\n",
       "[277 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Dataset (7 sheets).xlsx', sheet_name=None)\n",
    "for key in df.keys():\n",
    "    total = np.array(df[key].iloc[1].values[1:], dtype=float)\n",
    "    weight = np.array(df[key].iloc[0].values[1:], dtype=float)\n",
    "    nanInTotal = np.isnan(total)\n",
    "    assignmentTotal = []\n",
    "    quizTotal = []\n",
    "    assignmentWeight = []\n",
    "    quizWeight = []\n",
    "    trueCome = 0\n",
    "    index = 0\n",
    "    for isNan in nanInTotal:\n",
    "        if isNan == False and trueCome == 0:\n",
    "            assignmentTotal.append(total[index])\n",
    "            assignmentWeight.append(weight[index])\n",
    "            index = index + 1\n",
    "        elif isNan == False and trueCome == 1:\n",
    "            quizTotal.append(total[index])\n",
    "            quizWeight.append(weight[index])\n",
    "            index = index + 1\n",
    "        elif isNan == True:\n",
    "            trueCome = trueCome + 1\n",
    "            index = index + 1\n",
    "    # print(assignmentTotal, quizTotal, assignmentWeight, quizWeight)\n",
    "    assignmentTotal.extend(quizTotal)\n",
    "    assignmentWeight.extend(quizWeight)\n",
    "    totalByWeights = np.array(assignmentTotal)/np.array(assignmentWeight)\n",
    "    df[key] = df[key].iloc[3:].drop(['Unnamed: 0', 'As', 'Qz'], axis=1).fillna(0) # Data Cleaning & Imputation\n",
    "    # Making the data unit free by converting the assignments and quizzes numbers to absolutes\n",
    "    for i in range(len(totalByWeights)):\n",
    "        df[key].iloc[:, i] = df[key].iloc[:, i] / totalByWeights[i]\n",
    "df_final = pd.concat(df.values(), ignore_index=True).fillna(0).reindex(columns=['As:1','As:2', 'As:3', 'As:4', 'As:5', 'As:6', 'As:7', \n",
    "                                                                                       'Qz:1', 'Qz:2', 'Qz:3', 'Qz:4', 'Qz:5', 'Qz:6', 'Qz:7',\n",
    "                                                                                       'Qz:8', 'S-I',\t'S-II', 'Grade'])\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.astype(bool).sum(axis=0).plot(kind='bar', color=['cornflowerblue', 'lightgreen', 'salmon'], edgecolor='black', alpha=0.8)\n",
    "plt.title('Non-zero Values Count in Each Column', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Columns', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.xticks(rotation=90, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_final.iloc[:,[0,1,2,3,7,8,9,10,15]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.975     , 2.7       , 2.57142857, ..., 0.9       , 0.        ,\n",
       "        9.75      ],\n",
       "       [2.        , 1.86      , 1.99285714, ..., 0.1       , 0.        ,\n",
       "        3.37      ],\n",
       "       [2.125     , 1.89      , 2.57142857, ..., 0.2       , 0.        ,\n",
       "        6.56      ],\n",
       "       ...,\n",
       "       [1.93846154, 2.18571429, 1.48333333, ..., 0.2       , 0.        ,\n",
       "        5.25      ],\n",
       "       [2.88461538, 2.57142857, 2.8       , ..., 0.        , 0.        ,\n",
       "        8.06      ],\n",
       "       [2.23846154, 0.        , 1.66666667, ..., 0.        , 0.        ,\n",
       "        4.4       ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pass', 'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Fail', 'Pass',\n",
       "       'Pass', 'Pass', 'Fail', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass',\n",
       "       'Fail', 'Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Fail', 'Pass',\n",
       "       'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Fail', 'Fail', 'Fail',\n",
       "       'Pass', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Fail',\n",
       "       'Fail', 'Fail', 'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Pass',\n",
       "       'Fail', 'Pass', 'Pass', 'Pass', 'Pass', 'Fail', 'Pass', 'Pass',\n",
       "       'Pass', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Fail',\n",
       "       'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Fail', 'Fail', 'Pass',\n",
       "       'Pass', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Pass',\n",
       "       'Pass', 'Fail', 'Fail', 'Pass', 'Fail', 'Fail', 'Fail', 'Pass',\n",
       "       'Fail', 'Pass', 'Fail', 'Fail', 'Pass', 'Pass', 'Fail', 'Pass',\n",
       "       'Fail', 'Fail', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass',\n",
       "       'Fail', 'Fail', 'Pass', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass',\n",
       "       'Fail', 'Fail', 'Pass', 'Fail', 'Pass', 'Fail', 'Fail', 'Fail',\n",
       "       'Pass', 'Fail', 'Pass', 'Fail', 'Fail', 'Pass', 'Pass', 'Fail',\n",
       "       'Fail', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass',\n",
       "       'Pass', 'Fail', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Pass',\n",
       "       'Fail', 'Pass', 'Fail', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass',\n",
       "       'Fail', 'Pass', 'Pass', 'Pass', 'Pass', 'Fail', 'Pass', 'Pass',\n",
       "       'Fail', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Fail',\n",
       "       'Pass', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Fail',\n",
       "       'Fail', 'Pass', 'Fail', 'Fail', 'Pass', 'Pass', 'Pass', 'Pass',\n",
       "       'Pass', 'Fail', 'Pass', 'Fail', 'Fail', 'Fail', 'Fail', 'Pass',\n",
       "       'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Fail', 'Pass', 'Pass',\n",
       "       'Fail', 'Fail', 'Pass', 'Pass', 'Fail', 'Fail', 'Fail', 'Fail',\n",
       "       'Pass', 'Pass', 'Fail', 'Pass', 'Pass', 'Fail', 'Pass', 'Pass',\n",
       "       'Pass', 'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Pass', 'Fail',\n",
       "       'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Fail', 'Fail', 'Pass',\n",
       "       'Fail', 'Fail', 'Fail', 'Fail', 'Pass', 'Pass', 'Pass', 'Fail',\n",
       "       'Fail', 'Pass', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass',\n",
       "       'Fail', 'Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass',\n",
       "       'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass', 'Pass',\n",
       "       'Pass', 'Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Fail', 'Pass',\n",
       "       'Fail', 'Fail', 'Fail', 'Pass', 'Fail'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df_final.iloc[::,-1].values\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding the Dependent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,stratify=y, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.025       1.26        1.28571429 ...  0.          0.\n",
      "   5.06      ]\n",
      " [ 2.425       2.1         1.62857143 ...  0.4         1.\n",
      "   6.93      ]\n",
      " [ 2.37692308  0.          2.66666667 ...  2.          2.\n",
      "  11.06      ]\n",
      " ...\n",
      " [ 1.5         2.82        2.22857143 ...  0.3         1.2\n",
      "   2.15      ]\n",
      " [ 2.55        2.31        2.67857143 ...  1.3         1.6\n",
      "   8.62      ]\n",
      " [ 2.375       2.7         2.25       ...  0.          0.\n",
      "   3.93      ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(221, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.8         0.4         0.          0.83333333  0.73333333  0.175\n",
      "   0.1         0.          3.        ]\n",
      " [ 2.375       2.64        2.7         1.125       0.7         0.\n",
      "   1.6         0.          4.87      ]\n",
      " [ 2.4         1.62        1.03960396  2.5125      1.          1.45\n",
      "   0.50666667  2.          5.25      ]\n",
      " [ 0.          2.34        2.04950495  0.          1.          1.05\n",
      "   0.6         2.          4.12      ]\n",
      " [ 0.          3.          2.13        1.44        0.8         0.\n",
      "   0.2         0.4         3.75      ]\n",
      " [ 2.4         3.          2.50990099  1.6625      1.1         0.9\n",
      "   0.26666667  2.          3.18      ]\n",
      " [ 0.          2.72142857  2.83333333  3.          2.          0.53333333\n",
      "   0.8         2.          8.06      ]\n",
      " [ 2.475       2.16        1.39285714  1.2375      0.6         0.2\n",
      "   0.4         0.          3.75      ]\n",
      " [ 2.61        1.72        2.71428571  2.36666667  0.73333333  0.875\n",
      "   0.35        0.66666667  6.        ]\n",
      " [ 2.225       2.49        2.67857143  0.9         1.8         1.\n",
      "   0.3         0.8         5.62      ]\n",
      " [ 0.          0.          1.00990099  1.5         0.3         1.\n",
      "   0.2         1.6         1.68      ]\n",
      " [ 2.125       1.89        2.57142857  2.325       0.          0.\n",
      "   0.2         0.          6.56      ]\n",
      " [ 0.          0.          0.          2.625       0.9         1.45\n",
      "   0.52        2.          6.75      ]\n",
      " [ 2.60769231  2.65714286  0.          2.82        1.4         0.33333333\n",
      "   0.2         0.2         3.56      ]\n",
      " [ 1.9         2.25        2.12142857  2.52        1.2         0.6\n",
      "   0.1         0.          4.87      ]\n",
      " [ 1.65        2.4         2.67857143  1.9875      0.7         0.2\n",
      "   1.6         0.          2.25      ]\n",
      " [ 1.2         2.55        1.28571429  2.1         1.6         0.3\n",
      "   0.2         1.8         5.06      ]\n",
      " [ 2.52        1.64        2.54285714  1.26666667  1.          0.45\n",
      "   0.4         0.          6.18      ]\n",
      " [ 2.45        2.55        2.46428571  3.          1.4         0.8\n",
      "   0.05        1.8         5.62      ]\n",
      " [ 2.25        1.65        2.57142857  2.4375      0.2         0.5\n",
      "   0.4         0.          1.5       ]\n",
      " [ 2.05        0.          0.          0.          1.4         0.\n",
      "   0.          0.          3.        ]\n",
      " [ 1.02        2.32        2.8         2.03333333  0.86666667  1.1\n",
      "   1.25        1.          8.43      ]\n",
      " [ 2.3         1.83        2.89285714  2.625       0.2         0.\n",
      "   0.          0.          4.87      ]\n",
      " [ 2.4         2.08        1.85714286  1.9         1.06666667  0.95\n",
      "   0.45        0.          6.37      ]\n",
      " [ 2.55        3.          1.92857143  1.08        1.9         0.\n",
      "   0.          0.2         4.5       ]\n",
      " [ 2.19        2.          2.2         1.51666667  0.73333333  0.625\n",
      "   0.          0.          3.56      ]\n",
      " [ 2.51538462  2.44285714  2.37        2.82        0.8         0.66666667\n",
      "   0.2         2.         10.68      ]\n",
      " [ 2.43        1.36        3.          1.9         1.13333333  1.275\n",
      "   1.05        0.          5.81      ]\n",
      " [ 1.74        2.          1.62857143  1.23333333  0.46666667  0.1\n",
      "   0.2         0.          2.81      ]\n",
      " [ 1.725       0.15        1.45714286  1.3125      0.4         0.4\n",
      "   0.2         0.          3.37      ]\n",
      " [ 2.2         2.88        2.35714286  2.0625      0.6         0.4\n",
      "   0.3         0.2         7.12      ]\n",
      " [ 1.95        1.77        1.95        1.125       1.3         0.4\n",
      "   0.4         0.          3.37      ]\n",
      " [ 2.4         0.          0.          2.97        1.8         0.4\n",
      "   1.9         0.2         7.12      ]\n",
      " [ 2.175       2.19        2.89285714  2.7         0.6         0.2\n",
      "   0.2         0.          5.43      ]\n",
      " [ 2.37692308  2.22857143  2.          1.26        1.8         1.2\n",
      "   0.2         0.          7.31      ]\n",
      " [ 2.88461538  2.57142857  2.8         2.43        1.8         0.8\n",
      "   0.          0.          8.06      ]\n",
      " [ 1.63846154  2.91428571  2.76        2.82        1.2         0.53333333\n",
      "   0.2         0.4         5.62      ]\n",
      " [ 2.93076923  2.91428571  2.94        2.82        2.          1.33333333\n",
      "   0.8         2.         13.21      ]\n",
      " [ 2.55        2.64        1.54455446  0.          1.5         0.85\n",
      "   0.76        1.6         7.87      ]\n",
      " [ 2.4         2.46        1.92857143  1.6875      0.6         0.\n",
      "   0.6         0.          3.56      ]\n",
      " [ 1.625       1.11        2.52857143  1.8         0.2         0.\n",
      "   0.4         0.          6.37      ]\n",
      " [ 2.25        3.          2.14285714  2.25        1.2         0.\n",
      "   1.8         0.          4.31      ]\n",
      " [ 2.525       2.61        1.62857143  1.125       0.8         0.\n",
      "   0.3         0.          5.43      ]\n",
      " [ 2.325       2.4         2.14285714  1.02        0.          0.\n",
      "   0.2         0.          3.28      ]\n",
      " [ 2.385       2.16        2.71428571  2.63333333  0.93333333  1.525\n",
      "   0.6         0.          5.43      ]\n",
      " [ 2.42307692  2.57142857  1.31666667  3.          0.          0.26666667\n",
      "   1.1         0.2         5.81      ]\n",
      " [ 0.75        2.25        2.14285714  1.6875      0.6         0.6\n",
      "   0.6         0.          6.18      ]\n",
      " [ 2.325       2.4         2.25        2.16        1.7         0.\n",
      "   0.1         0.          4.31      ]\n",
      " [ 2.86153846  2.44285714  2.22        1.5         0.8         0.66666667\n",
      "   0.          1.6         7.12      ]\n",
      " [ 0.          2.57142857  2.94        2.76        0.8         1.06666667\n",
      "   0.4         2.          5.25      ]\n",
      " [ 2.45        2.61        2.14285714  2.8125      1.3         0.2\n",
      "   1.7         0.4         6.37      ]\n",
      " [ 2.7         1.6         2.57142857  2.46666667  0.66666667  0.975\n",
      "   0.4         0.33333333  4.87      ]\n",
      " [ 1.875       1.23        2.37857143  2.53846154  0.2         0.9\n",
      "   0.2         1.2         5.25      ]\n",
      " [ 2.7         2.48        3.          2.51666667  0.8         0.8\n",
      "   1.75        0.66666667  8.43      ]\n",
      " [ 2.46923077  2.4         2.75        2.82        2.          1.2\n",
      "   1.9         2.         10.96      ]\n",
      " [ 1.725       2.85        1.28571429  2.82        1.          0.4\n",
      "   0.4         0.5         6.28      ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(56, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1\n",
      " 1 1 1 1 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 0 0 0 1\n",
      " 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0\n",
      " 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1\n",
      " 0 1 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 1 1 0 1 0 0 1 1 0\n",
      " 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(221,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_train)\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 1\n",
      " 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(56,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_test)\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.025000</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.425000</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.628571</td>\n",
       "      <td>2.561538</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.376923</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.957143</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.425000</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.892857</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1.975000</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2.670000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.717822</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.228571</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2.550000</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>2.930769</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2.375000</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 9 columns</p>\n",
       "</div>"
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       "            0     1         2         3    4    5         6    7      8\n",
       "0    1.025000  1.26  1.285714  2.625000  0.2  0.4  0.000000  0.0   5.06\n",
       "1    2.425000  2.10  1.628571  2.561538  0.5  0.7  0.400000  1.0   6.93\n",
       "2    2.376923  0.00  2.666667  3.000000  1.6  1.2  2.000000  2.0  11.06\n",
       "3    2.700000  2.49  2.957143  2.925000  0.8  0.4  1.500000  0.0  10.12\n",
       "4    1.425000  2.25  2.892857  2.812500  0.6  0.0  0.500000  0.2   3.75\n",
       "..        ...   ...       ...       ...  ...  ...       ...  ...    ...\n",
       "216  1.975000  2.70  2.571429  3.000000  1.5  0.9  0.900000  0.0   9.75\n",
       "217  2.670000  3.00  2.717822  2.875000  1.1  1.4  0.933333  1.6   8.25\n",
       "218  1.500000  2.82  2.228571  2.940000  1.4  1.6  0.300000  1.2   2.15\n",
       "219  2.550000  2.31  2.678571  2.930769  0.6  1.8  1.300000  1.6   8.62\n",
       "220  2.375000  2.70  2.250000  0.960000  1.4  0.4  0.000000  0.0   3.93\n",
       "\n",
       "[221 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=pd.DataFrame(X_train)\n",
    "# print(temp.describe())\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.197229</td>\n",
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       "      <td>0.345063</td>\n",
       "      <td>0.485509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.519014</td>\n",
       "      <td>-2.658908</td>\n",
       "      <td>0.770820</td>\n",
       "      <td>1.118238</td>\n",
       "      <td>1.369720</td>\n",
       "      <td>1.012920</td>\n",
       "      <td>2.605751</td>\n",
       "      <td>1.662904</td>\n",
       "      <td>2.222762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929155</td>\n",
       "      <td>0.460820</td>\n",
       "      <td>1.168911</td>\n",
       "      <td>1.025203</td>\n",
       "      <td>-0.188623</td>\n",
       "      <td>-0.632193</td>\n",
       "      <td>1.698468</td>\n",
       "      <td>-0.972777</td>\n",
       "      <td>1.827358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.689436</td>\n",
       "      <td>0.160124</td>\n",
       "      <td>1.080809</td>\n",
       "      <td>0.885650</td>\n",
       "      <td>-0.578209</td>\n",
       "      <td>-1.454750</td>\n",
       "      <td>-0.116099</td>\n",
       "      <td>-0.709209</td>\n",
       "      <td>-0.852134</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.008780</td>\n",
       "      <td>0.723930</td>\n",
       "      <td>0.640298</td>\n",
       "      <td>1.118238</td>\n",
       "      <td>1.174927</td>\n",
       "      <td>0.396003</td>\n",
       "      <td>0.609727</td>\n",
       "      <td>-0.972777</td>\n",
       "      <td>1.671720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0.891071</td>\n",
       "      <td>1.099801</td>\n",
       "      <td>0.840927</td>\n",
       "      <td>0.963179</td>\n",
       "      <td>0.395756</td>\n",
       "      <td>1.424199</td>\n",
       "      <td>0.670213</td>\n",
       "      <td>1.135768</td>\n",
       "      <td>1.040757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>-0.594225</td>\n",
       "      <td>0.874278</td>\n",
       "      <td>0.170420</td>\n",
       "      <td>1.043810</td>\n",
       "      <td>0.980134</td>\n",
       "      <td>1.835477</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>0.608631</td>\n",
       "      <td>-1.525162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.738733</td>\n",
       "      <td>0.235298</td>\n",
       "      <td>0.787135</td>\n",
       "      <td>1.032359</td>\n",
       "      <td>-0.578209</td>\n",
       "      <td>2.246755</td>\n",
       "      <td>1.335554</td>\n",
       "      <td>1.135768</td>\n",
       "      <td>1.196395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0.516573</td>\n",
       "      <td>0.723930</td>\n",
       "      <td>0.199787</td>\n",
       "      <td>-1.412325</td>\n",
       "      <td>0.980134</td>\n",
       "      <td>-0.632193</td>\n",
       "      <td>-1.023383</td>\n",
       "      <td>-0.972777</td>\n",
       "      <td>-0.776418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0   -1.197229 -1.080250 -1.121746  0.653061 -1.357380 -0.632193 -1.023383   \n",
       "1    0.580047 -0.027812 -0.651867  0.574339 -0.773001 -0.015276 -0.297556   \n",
       "2    0.519014 -2.658908  0.770820  1.118238  1.369720  1.012920  2.605751   \n",
       "3    0.929155  0.460820  1.168911  1.025203 -0.188623 -0.632193  1.698468   \n",
       "4   -0.689436  0.160124  1.080809  0.885650 -0.578209 -1.454750 -0.116099   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "216  0.008780  0.723930  0.640298  1.118238  1.174927  0.396003  0.609727   \n",
       "217  0.891071  1.099801  0.840927  0.963179  0.395756  1.424199  0.670213   \n",
       "218 -0.594225  0.874278  0.170420  1.043810  0.980134  1.835477 -0.479013   \n",
       "219  0.738733  0.235298  0.787135  1.032359 -0.578209  2.246755  1.335554   \n",
       "220  0.516573  0.723930  0.199787 -1.412325  0.980134 -0.632193 -1.023383   \n",
       "\n",
       "            7         8  \n",
       "0   -0.972777 -0.301092  \n",
       "1    0.345063  0.485509  \n",
       "2    1.662904  2.222762  \n",
       "3   -0.972777  1.827358  \n",
       "4   -0.709209 -0.852134  \n",
       "..        ...       ...  \n",
       "216 -0.972777  1.671720  \n",
       "217  1.135768  1.040757  \n",
       "218  0.608631 -1.525162  \n",
       "219  1.135768  1.196395  \n",
       "220 -0.972777 -0.776418  \n",
       "\n",
       "[221 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=pd.DataFrame(X_train)\n",
    "# print(temp.describe())\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Prediction before Mid 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "def grid_search_knn(x_train, y_train, param_grid, cv=10):\n",
    " \"\"\"\n",
    " Perform grid search for hyperparameter tuning of K-Nearest Neighbors (KNN) classifier.\n",
    " Args:\n",
    " x_train (numpy.ndarray): Training features.\n",
    " y_train (numpy.ndarray): Training labels.\n",
    " param_grid (dict): Dictionary with parameters names (string) as keys and lists of\n",
    " parameter settings to try as values.\n",
    " cv (int): Number of folds for cross-validation.\n",
    " Returns:\n",
    " sklearn.model_selection.GridSearchCV: GridSearchCV object with the best parameters.\n",
    " \"\"\"\n",
    " knn_model = KNeighborsClassifier()\n",
    " grid_search = GridSearchCV(knn_model, param_grid, cv=cv)\n",
    " grid_search.fit(x_train, y_train)\n",
    " return grid_search.best_params_\n",
    "def grid_search_decision_tree(x_train, y_train, param_grid, cv=10):\n",
    " \"\"\"\n",
    " Perform grid search for hyperparameter tuning of Decision Tree classifier.\n",
    " Args:\n",
    " x_train (numpy.ndarray): Training features.\n",
    " y_train (numpy.ndarray): Training labels.\n",
    " param_grid (dict): Dictionary with parameters names (string) as keys and lists of\n",
    " parameter settings to try as values.\n",
    " cv (int): Number of folds for cross-validation.\n",
    " Returns:\n",
    " sklearn.model_selection.GridSearchCV: GridSearchCV object with the best parameters.\n",
    " \"\"\"\n",
    " tree_model = DecisionTreeClassifier()\n",
    " grid_search = GridSearchCV(tree_model, param_grid, cv=cv)\n",
    " grid_search.fit(x_train, y_train)\n",
    " return grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_grid_search {'algorithm': 'auto', 'n_neighbors': 7, 'weights': 'uniform'}\n",
      "tree_grid_search {'criterion': 'gini', 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "# Example parameter grid for KNN\n",
    "knn_param_grid = {\n",
    " 'n_neighbors': [3, 5, 7, 9],\n",
    " 'weights': ['uniform', 'distance'],\n",
    " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']\n",
    "}\n",
    "# Example parameter grid for Decision Tree\n",
    "tree_param_grid = {\n",
    " 'criterion': ['gini', 'entropy'],\n",
    " 'max_depth': [None, 3, 5, 10]\n",
    "}\n",
    "# Perform grid search for KNN\n",
    "knn_grid_search = grid_search_knn(X_train, y_train, knn_param_grid)\n",
    "print(\"knn_grid_search\",knn_grid_search)\n",
    "# Perform grid search for Decision Tree\n",
    "tree_grid_search = grid_search_decision_tree(X_train, y_train, tree_param_grid)\n",
    "print(\"tree_grid_search\",tree_grid_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters for KNN model\n",
    "knn_params = {'algorithm': 'auto', 'n_neighbors': 7, 'weights': 'uniform'}\n",
    "# Define the hyperparameters for Decision Tree model\n",
    "tree_params = {'criterion': 'gini', 'max_depth': None}\n",
    "# Create KNN model with best hyperparameters\n",
    "knn_model = KNeighborsClassifier(**knn_params)\n",
    "# Create Decision Tree model with best hyperparameters\n",
    "tree_model = DecisionTreeClassifier(**tree_params)\n",
    "# Assuming X_train and y_train are your training data\n",
    "# Train the KNN model\n",
    "knn_model.fit(X_train, y_train)\n",
    "# Train the Decision Tree model\n",
    "tree_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics for KNN model:\n",
      "confusion_matrix: [[23  3]\n",
      " [ 8 22]]\n",
      "accuracy: 0.8035714285714286\n",
      "precision: 0.88\n",
      "recall: 0.7333333333333333\n",
      "f1_score: 0.8\n",
      "specificity: 0.8846153846153846\n",
      "\n",
      "Performance metrics for Decision Tree model:\n",
      "confusion_matrix: [[19  7]\n",
      " [ 7 23]]\n",
      "accuracy: 0.75\n",
      "precision: 0.7666666666666667\n",
      "recall: 0.7666666666666667\n",
      "f1_score: 0.7666666666666667\n",
      "specificity: 0.7307692307692307\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "def evaluate_model(model, X_test, y_test):\n",
    " \"\"\"\n",
    " Evaluate the performance of the specified model on the test data.\n",
    " Args:\n",
    " model: Trained classifier model.\n",
    " X_test (array-like): Test features.\n",
    " y_test (array-like): True labels for the test data.\n",
    " Returns:\n",
    " dict: Dictionary containing performance metrics.\n",
    " \"\"\"\n",
    " # Make predictions\n",
    " y_pred = model.predict(X_test)\n",
    " \n",
    " # Confusion matrix\n",
    " cm = confusion_matrix(y_test, y_pred)\n",
    " \n",
    " # Accuracy\n",
    " accuracy = accuracy_score(y_test, y_pred)\n",
    " \n",
    " # Precision\n",
    " precision = precision_score(y_test, y_pred)\n",
    " \n",
    " # Recall (Sensitivity)\n",
    " recall = recall_score(y_test, y_pred)\n",
    " \n",
    " # F1-score\n",
    " f1 = f1_score(y_test, y_pred)\n",
    " \n",
    " # Specificity\n",
    " tn, fp, fn, tp = cm.ravel()\n",
    " specificity = tn / (tn + fp)\n",
    " \n",
    " # Create a dictionary to store performance metrics\n",
    " performance_metrics = {\n",
    " 'confusion_matrix': cm,\n",
    " 'accuracy': accuracy,\n",
    " 'precision': precision,\n",
    " 'recall': recall,\n",
    " 'f1_score': f1,\n",
    " 'specificity': specificity\n",
    " }\n",
    " \n",
    " return performance_metrics\n",
    "# Evaluate KNN model\n",
    "knn_performance = evaluate_model(knn_model, X_test, y_test)\n",
    "# Evaluate Decision Tree model\n",
    "tree_performance = evaluate_model(tree_model, X_test, y_test)\n",
    "# Print performance metrics for KNN model\n",
    "print(\"Performance metrics for KNN model:\")\n",
    "for metric, value in knn_performance.items():\n",
    " print(f\"{metric}: {value}\")\n",
    "# Print performance metrics for Decision Tree model\n",
    "print(\"\\nPerformance metrics for Decision Tree model:\")\n",
    "for metric, value in tree_performance.items():\n",
    " print(f\"{metric}: {value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Prediction before Final Exams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As:1</th>\n",
       "      <th>As:2</th>\n",
       "      <th>As:3</th>\n",
       "      <th>As:4</th>\n",
       "      <th>As:5</th>\n",
       "      <th>As:6</th>\n",
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       "      <th>Qz:1</th>\n",
       "      <th>Qz:2</th>\n",
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       "      <th>Qz:7</th>\n",
       "      <th>Qz:8</th>\n",
       "      <th>S-I</th>\n",
       "      <th>S-II</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.975000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>8.62</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>1.992857</td>\n",
       "      <td>1.21875</td>\n",
       "      <td>1.875</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.93</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.125000</td>\n",
       "      <td>1.890000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.32500</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>0.93</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>2.62500</td>\n",
       "      <td>1.750</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.150000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>2.750</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2.25</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2.330769</td>\n",
       "      <td>2.121429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.85000</td>\n",
       "      <td>2.200</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1.430769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>2.175</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1.938462</td>\n",
       "      <td>2.185714</td>\n",
       "      <td>1.483333</td>\n",
       "      <td>1.68000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2.884615</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.43000</td>\n",
       "      <td>2.750</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.06</td>\n",
       "      <td>4.31</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2.238462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.46000</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>277 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         As:1      As:2      As:3     As:4   As:5      As:6  As:7  Qz:1  \\\n",
       "0    1.975000  2.700000  2.571429  3.00000  2.125  2.812500   0.0   1.5   \n",
       "1    2.000000  1.860000  1.992857  1.21875  1.875  2.850000   0.0   0.3   \n",
       "2    2.125000  1.890000  2.571429  2.32500  1.625  1.875000   0.0   0.0   \n",
       "3    1.025000  1.260000  1.285714  2.62500  1.750  0.375000   0.0   0.2   \n",
       "4    2.150000  1.950000  2.678571  0.37500  2.750  0.937500   0.0   0.6   \n",
       "..        ...       ...       ...      ...    ...       ...   ...   ...   \n",
       "272  2.330769  2.121429  0.000000  2.85000  2.200  1.533333   0.0   1.2   \n",
       "273  1.430769  0.000000  1.900000  1.80000  2.175  2.233333   0.0   0.0   \n",
       "274  1.938462  2.185714  1.483333  1.68000  2.000  1.600000   0.0   0.8   \n",
       "275  2.884615  2.571429  2.800000  2.43000  2.750  2.866667   0.0   1.8   \n",
       "276  2.238462  0.000000  1.666667  2.46000  1.800  0.000000   0.0   0.0   \n",
       "\n",
       "         Qz:2  Qz:3  Qz:4  Qz:5  Qz:6      Qz:7  Qz:8   S-I  S-II Grade  \n",
       "0    0.900000   0.9   0.0   0.2   1.0  0.000000   0.0  9.75  8.62  Pass  \n",
       "1    0.000000   0.1   0.0   0.2   0.4  0.000000   0.0  3.37  3.93  Fail  \n",
       "2    0.000000   0.2   0.0   0.2   0.0  2.000000   0.0  6.56  0.93  Fail  \n",
       "3    0.400000   0.0   0.0   0.0   0.0  2.000000   0.0  5.06  2.81  Fail  \n",
       "4    0.200000   0.0   0.0   0.0   0.0  0.000000   0.0  4.50  2.25  Fail  \n",
       "..        ...   ...   ...   ...   ...       ...   ...   ...   ...   ...  \n",
       "272  1.133333   0.1   0.0   0.2   0.8  0.333333   2.0  6.56  2.90  Fail  \n",
       "273  1.333333   0.0   0.2   0.6   0.0  1.000000   2.0  3.37  1.59  Fail  \n",
       "274  0.933333   0.2   0.0   1.8   1.2  1.166667   2.0  5.25  1.50  Fail  \n",
       "275  0.800000   0.0   0.0   0.6   1.0  0.333333   2.0  8.06  4.31  Pass  \n",
       "276  0.600000   0.0   0.0   0.6   0.0  0.000000   2.0  4.40  1.40  Fail  \n",
       "\n",
       "[277 rows x 18 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(277, 7)\n"
     ]
    }
   ],
   "source": [
    "temp = df_final.iloc[:, 0:7].values\n",
    "# Sort each row independently\n",
    "temp = np.sort(temp, axis=1)\n",
    "print(temp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempq=df_final.iloc[:,7:15].values\n",
    "tempq=np.sort(tempq, axis=1)\n",
    "tempq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.812500</td>\n",
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       "      <td>8.62</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.860000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>1.992857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.850000</td>\n",
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       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.875000</td>\n",
       "      <td>1.890000</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.325000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.400000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.937500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2.25</td>\n",
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       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1.533333</td>\n",
       "      <td>2.121429</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.330769</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>1.200000</td>\n",
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       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1.430769</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.175000</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.59</td>\n",
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       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>1.938462</td>\n",
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       "      <td>2.185714</td>\n",
       "      <td>0.933333</td>\n",
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       "      <td>1.200000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>275</th>\n",
       "      <td>2.571429</td>\n",
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       "      <td>2.884615</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.06</td>\n",
       "      <td>4.31</td>\n",
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       "      <th>276</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>277 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0    2.125000  2.571429  2.700000  2.812500  3.000000  0.200000  0.900000   \n",
       "1    1.860000  1.875000  1.992857  2.000000  2.850000  0.000000  0.100000   \n",
       "2    1.875000  1.890000  2.125000  2.325000  2.571429  0.000000  0.000000   \n",
       "3    1.025000  1.260000  1.285714  1.750000  2.625000  0.000000  0.000000   \n",
       "4    0.937500  1.950000  2.150000  2.678571  2.750000  0.000000  0.000000   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "272  1.533333  2.121429  2.200000  2.330769  2.850000  0.333333  0.800000   \n",
       "273  1.430769  1.800000  1.900000  2.175000  2.233333  0.200000  0.600000   \n",
       "274  1.600000  1.680000  1.938462  2.000000  2.185714  0.933333  1.166667   \n",
       "275  2.571429  2.750000  2.800000  2.866667  2.884615  0.600000  0.800000   \n",
       "276  0.000000  1.666667  1.800000  2.238462  2.460000  0.000000  0.000000   \n",
       "\n",
       "           7         8    9     10    11  \n",
       "0    0.900000  1.000000  1.5  9.75  8.62  \n",
       "1    0.200000  0.300000  0.4  3.37  3.93  \n",
       "2    0.200000  0.200000  2.0  6.56  0.93  \n",
       "3    0.200000  0.400000  2.0  5.06  2.81  \n",
       "4    0.000000  0.200000  0.6  4.50  2.25  \n",
       "..        ...       ...  ...   ...   ...  \n",
       "272  1.133333  1.200000  2.0  6.56  2.90  \n",
       "273  1.000000  1.333333  2.0  3.37  1.59  \n",
       "274  1.200000  1.800000  2.0  5.25  1.50  \n",
       "275  1.000000  1.800000  2.0  8.06  4.31  \n",
       "276  0.600000  0.600000  2.0  4.40  1.40  \n",
       "\n",
       "[277 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = np.concatenate((temp[:, -5:] , tempq[:, -5:]),axis=1)\n",
    "X = np.concatenate((X1, df_final.iloc[:, [15, 16]].values), axis=1)\n",
    "X=pd.DataFrame(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.700468</td>\n",
       "      <td>2.008619</td>\n",
       "      <td>2.234676</td>\n",
       "      <td>2.460029</td>\n",
       "      <td>2.676644</td>\n",
       "      <td>0.514817</td>\n",
       "      <td>0.776017</td>\n",
       "      <td>1.040138</td>\n",
       "      <td>1.380646</td>\n",
       "      <td>1.890854</td>\n",
       "      <td>5.724910</td>\n",
       "      <td>4.889350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.753311</td>\n",
       "      <td>0.632393</td>\n",
       "      <td>0.557434</td>\n",
       "      <td>0.467748</td>\n",
       "      <td>0.383121</td>\n",
       "      <td>0.439677</td>\n",
       "      <td>0.502911</td>\n",
       "      <td>0.506920</td>\n",
       "      <td>0.523581</td>\n",
       "      <td>0.332361</td>\n",
       "      <td>2.352401</td>\n",
       "      <td>2.712735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>2.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.860000</td>\n",
       "      <td>2.137500</td>\n",
       "      <td>2.335714</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.220000</td>\n",
       "      <td>2.425000</td>\n",
       "      <td>2.614286</td>\n",
       "      <td>2.807143</td>\n",
       "      <td>2.930769</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.030000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>13.870000</td>\n",
       "      <td>12.370000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5   \\\n",
       "count  277.000000  277.000000  277.000000  277.000000  277.000000  277.000000   \n",
       "mean     1.700468    2.008619    2.234676    2.460029    2.676644    0.514817   \n",
       "std      0.753311    0.632393    0.557434    0.467748    0.383121    0.439677   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.375000    1.740000    2.016667    2.250000    2.550000    0.200000   \n",
       "50%      1.860000    2.137500    2.335714    2.550000    2.760000    0.400000   \n",
       "75%      2.220000    2.425000    2.614286    2.807143    2.930769    0.800000   \n",
       "max      2.875000    3.000000    3.000000    3.000000    3.000000    2.000000   \n",
       "\n",
       "               6           7           8           9           10          11  \n",
       "count  277.000000  277.000000  277.000000  277.000000  277.000000  277.000000  \n",
       "mean     0.776017    1.040138    1.380646    1.890854    5.724910    4.889350  \n",
       "std      0.502911    0.506920    0.523581    0.332361    2.352401    2.712735  \n",
       "min      0.000000    0.000000    0.000000    0.200000    0.000000    0.000000  \n",
       "25%      0.400000    0.666667    1.000000    2.000000    4.120000    2.620000  \n",
       "50%      0.700000    1.000000    1.500000    2.000000    5.430000    4.500000  \n",
       "75%      1.100000    1.400000    1.800000    2.000000    7.030000    6.750000  \n",
       "max      2.000000    2.000000    2.000000    2.000000   13.870000   12.370000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,stratify=y, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(221, 12) (221,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(56, 12) (56,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1.628571</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.425000</td>\n",
       "      <td>2.561538</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.93</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>2.376923</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.06</td>\n",
       "      <td>7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2.490000</td>\n",
       "      <td>2.512500</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.957143</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.12</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>2.887500</td>\n",
       "      <td>2.892857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.12</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>0</th>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.75</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.717822</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.933333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>8.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2.175000</td>\n",
       "      <td>2.228571</td>\n",
       "      <td>2.820000</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.15</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2.550000</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.930769</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.62</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5    6    7   \\\n",
       "3    1.025000  1.260000  1.285714  1.750000  2.625000  0.000000  0.0  0.2   \n",
       "86   1.628571  1.900000  2.100000  2.425000  2.561538  0.400000  0.5  0.7   \n",
       "261  2.376923  2.666667  2.750000  3.000000  3.000000  1.600000  2.0  2.0   \n",
       "47   2.490000  2.512500  2.700000  2.925000  2.957143  0.400000  0.6  0.8   \n",
       "16   2.250000  2.750000  2.812500  2.887500  2.892857  0.000000  0.2  0.5   \n",
       "..        ...       ...       ...       ...       ...       ...  ...  ...   \n",
       "0    2.125000  2.571429  2.700000  2.812500  3.000000  0.200000  0.9  0.9   \n",
       "159  2.670000  2.717822  2.875000  2.933333  3.000000  0.933333  1.1  1.4   \n",
       "230  2.175000  2.228571  2.820000  2.833333  2.940000  1.100000  1.2  1.4   \n",
       "101  2.550000  2.678571  2.750000  2.900000  2.930769  1.300000  1.6  1.6   \n",
       "214  0.960000  1.266667  2.250000  2.375000  2.700000  0.400000  1.0  1.4   \n",
       "\n",
       "      8    9      10    11  \n",
       "3    0.4  2.0   5.06  2.81  \n",
       "86   1.0  1.8   6.93  4.68  \n",
       "261  2.0  2.0  11.06  7.21  \n",
       "47   1.5  2.0  10.12  6.93  \n",
       "16   0.6  2.0   3.75  4.12  \n",
       "..   ...  ...    ...   ...  \n",
       "0    1.0  1.5   9.75  8.62  \n",
       "159  1.6  2.0   8.25  8.06  \n",
       "230  1.6  2.0   2.15  3.28  \n",
       "101  1.8  2.0   8.62  9.00  \n",
       "214  1.7  2.0   3.93  2.43  \n",
       "\n",
       "[221 rows x 12 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=pd.DataFrame(X_train)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.887339</td>\n",
       "      <td>-1.158167</td>\n",
       "      <td>-1.697522</td>\n",
       "      <td>-1.487167</td>\n",
       "      <td>-0.057675</td>\n",
       "      <td>-1.197376</td>\n",
       "      <td>-1.598425</td>\n",
       "      <td>-1.735126</td>\n",
       "      <td>-1.946669</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>-0.301092</td>\n",
       "      <td>-0.768641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.096651</td>\n",
       "      <td>-0.164754</td>\n",
       "      <td>-0.221063</td>\n",
       "      <td>-0.042399</td>\n",
       "      <td>-0.213026</td>\n",
       "      <td>-0.280202</td>\n",
       "      <td>-0.591203</td>\n",
       "      <td>-0.714925</td>\n",
       "      <td>-0.765994</td>\n",
       "      <td>-0.248817</td>\n",
       "      <td>0.485509</td>\n",
       "      <td>-0.076836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.883701</td>\n",
       "      <td>1.025272</td>\n",
       "      <td>0.957514</td>\n",
       "      <td>1.188329</td>\n",
       "      <td>0.860312</td>\n",
       "      <td>2.471322</td>\n",
       "      <td>2.430464</td>\n",
       "      <td>1.937597</td>\n",
       "      <td>1.201797</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>2.222762</td>\n",
       "      <td>0.859136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.031834</td>\n",
       "      <td>0.785973</td>\n",
       "      <td>0.866854</td>\n",
       "      <td>1.027799</td>\n",
       "      <td>0.755399</td>\n",
       "      <td>-0.280202</td>\n",
       "      <td>-0.389758</td>\n",
       "      <td>-0.510885</td>\n",
       "      <td>0.217901</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>1.827358</td>\n",
       "      <td>0.755550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.717430</td>\n",
       "      <td>1.154622</td>\n",
       "      <td>1.070838</td>\n",
       "      <td>0.947534</td>\n",
       "      <td>0.598030</td>\n",
       "      <td>-1.197376</td>\n",
       "      <td>-1.195536</td>\n",
       "      <td>-1.123006</td>\n",
       "      <td>-1.553111</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>-0.852134</td>\n",
       "      <td>-0.284007</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>216</th>\n",
       "      <td>0.553678</td>\n",
       "      <td>0.877442</td>\n",
       "      <td>0.866854</td>\n",
       "      <td>0.787004</td>\n",
       "      <td>0.860312</td>\n",
       "      <td>-0.738789</td>\n",
       "      <td>0.214575</td>\n",
       "      <td>-0.306845</td>\n",
       "      <td>-0.765994</td>\n",
       "      <td>-1.127852</td>\n",
       "      <td>1.671720</td>\n",
       "      <td>1.380765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1.267637</td>\n",
       "      <td>1.104675</td>\n",
       "      <td>1.184163</td>\n",
       "      <td>1.045635</td>\n",
       "      <td>0.860312</td>\n",
       "      <td>0.942698</td>\n",
       "      <td>0.617464</td>\n",
       "      <td>0.713356</td>\n",
       "      <td>0.414681</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>1.040757</td>\n",
       "      <td>1.173593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0.619179</td>\n",
       "      <td>0.345257</td>\n",
       "      <td>1.084437</td>\n",
       "      <td>0.831596</td>\n",
       "      <td>0.713434</td>\n",
       "      <td>1.324854</td>\n",
       "      <td>0.818909</td>\n",
       "      <td>0.713356</td>\n",
       "      <td>0.414681</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>-1.525162</td>\n",
       "      <td>-0.594765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1.110435</td>\n",
       "      <td>1.043750</td>\n",
       "      <td>0.957514</td>\n",
       "      <td>0.974289</td>\n",
       "      <td>0.690837</td>\n",
       "      <td>1.783441</td>\n",
       "      <td>1.624686</td>\n",
       "      <td>1.121436</td>\n",
       "      <td>0.808239</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>1.196395</td>\n",
       "      <td>1.521345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>-0.972490</td>\n",
       "      <td>-1.147818</td>\n",
       "      <td>0.050916</td>\n",
       "      <td>-0.149419</td>\n",
       "      <td>0.125923</td>\n",
       "      <td>-0.280202</td>\n",
       "      <td>0.416020</td>\n",
       "      <td>0.713356</td>\n",
       "      <td>0.611460</td>\n",
       "      <td>0.337206</td>\n",
       "      <td>-0.776418</td>\n",
       "      <td>-0.909222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -0.887339 -1.158167 -1.697522 -1.487167 -0.057675 -1.197376 -1.598425   \n",
       "1   -0.096651 -0.164754 -0.221063 -0.042399 -0.213026 -0.280202 -0.591203   \n",
       "2    0.883701  1.025272  0.957514  1.188329  0.860312  2.471322  2.430464   \n",
       "3    1.031834  0.785973  0.866854  1.027799  0.755399 -0.280202 -0.389758   \n",
       "4    0.717430  1.154622  1.070838  0.947534  0.598030 -1.197376 -1.195536   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "216  0.553678  0.877442  0.866854  0.787004  0.860312 -0.738789  0.214575   \n",
       "217  1.267637  1.104675  1.184163  1.045635  0.860312  0.942698  0.617464   \n",
       "218  0.619179  0.345257  1.084437  0.831596  0.713434  1.324854  0.818909   \n",
       "219  1.110435  1.043750  0.957514  0.974289  0.690837  1.783441  1.624686   \n",
       "220 -0.972490 -1.147818  0.050916 -0.149419  0.125923 -0.280202  0.416020   \n",
       "\n",
       "           7         8         9         10        11  \n",
       "0   -1.735126 -1.946669  0.337206 -0.301092 -0.768641  \n",
       "1   -0.714925 -0.765994 -0.248817  0.485509 -0.076836  \n",
       "2    1.937597  1.201797  0.337206  2.222762  0.859136  \n",
       "3   -0.510885  0.217901  0.337206  1.827358  0.755550  \n",
       "4   -1.123006 -1.553111  0.337206 -0.852134 -0.284007  \n",
       "..        ...       ...       ...       ...       ...  \n",
       "216 -0.306845 -0.765994 -1.127852  1.671720  1.380765  \n",
       "217  0.713356  0.414681  0.337206  1.040757  1.173593  \n",
       "218  0.713356  0.414681  0.337206 -1.525162 -0.594765  \n",
       "219  1.121436  0.808239  0.337206  1.196395  1.521345  \n",
       "220  0.713356  0.611460  0.337206 -0.776418 -0.909222  \n",
       "\n",
       "[221 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=pd.DataFrame(X_train)\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "def grid_search_knn(x_train, y_train, param_grid, cv=10):\n",
    " \"\"\"\n",
    " Perform grid search for hyperparameter tuning of K-Nearest Neighbors (KNN) classifier.\n",
    " Args:\n",
    " x_train (numpy.ndarray): Training features.\n",
    " y_train (numpy.ndarray): Training labels.\n",
    " param_grid (dict): Dictionary with parameters names (string) as keys and lists of\n",
    " parameter settings to try as values.\n",
    " cv (int): Number of folds for cross-validation.\n",
    " Returns:\n",
    " sklearn.model_selection.GridSearchCV: GridSearchCV object with the best parameters.\n",
    " \"\"\"\n",
    " knn_model = KNeighborsClassifier()\n",
    " grid_search = GridSearchCV(knn_model, param_grid, cv=cv)\n",
    " grid_search.fit(x_train, y_train)\n",
    " return grid_search.best_params_\n",
    "def grid_search_decision_tree(x_train, y_train, param_grid, cv=10):\n",
    " \"\"\"\n",
    " Perform grid search for hyperparameter tuning of Decision Tree classifier.\n",
    " Args:\n",
    " x_train (numpy.ndarray): Training features.\n",
    " y_train (numpy.ndarray): Training labels.\n",
    " param_grid (dict): Dictionary with parameters names (string) as keys and lists of\n",
    " parameter settings to try as values.\n",
    " cv (int): Number of folds for cross-validation.\n",
    " Returns:\n",
    " sklearn.model_selection.GridSearchCV: GridSearchCV object with the best parameters.\n",
    " \"\"\"\n",
    " tree_model = DecisionTreeClassifier()\n",
    " grid_search = GridSearchCV(tree_model, param_grid, cv=cv)\n",
    " grid_search.fit(x_train, y_train)\n",
    " return grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters for KNN model\n",
    "knn_params = {'algorithm': 'auto', 'n_neighbors': 7, 'weights': 'uniform'}\n",
    "# Define the hyperparameters for Decision Tree model\n",
    "tree_params = {'criterion': 'gini', 'max_depth': None}\n",
    "# Create KNN model with best hyperparameters\n",
    "knn_model = KNeighborsClassifier(**knn_params)\n",
    "# Create Decision Tree model with best hyperparameters\n",
    "tree_model = DecisionTreeClassifier(**tree_params)\n",
    "# Assuming X_train and y_train are your training data\n",
    "# Train the KNN model\n",
    "knn_model.fit(X_train, y_train)\n",
    "# Train the Decision Tree model\n",
    "tree_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics for KNN model:\n",
      "confusion_matrix: [[22  4]\n",
      " [ 6 24]]\n",
      "accuracy: 0.8214285714285714\n",
      "precision: 0.8571428571428571\n",
      "recall: 0.8\n",
      "f1_score: 0.8275862068965518\n",
      "specificity: 0.8461538461538461\n",
      "\n",
      "Performance metrics for Decision Tree model:\n",
      "confusion_matrix: [[19  7]\n",
      " [ 7 23]]\n",
      "accuracy: 0.75\n",
      "precision: 0.7666666666666667\n",
      "recall: 0.7666666666666667\n",
      "f1_score: 0.7666666666666667\n",
      "specificity: 0.7307692307692307\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "def evaluate_model(model, X_test, y_test):\n",
    " \"\"\"\n",
    " Evaluate the performance of the specified model on the test data.\n",
    " Args:\n",
    " model: Trained classifier model.\n",
    " X_test (array-like): Test features.\n",
    " y_test (array-like): True labels for the test data.\n",
    " Returns:\n",
    " dict: Dictionary containing performance metrics.\n",
    " \"\"\"\n",
    " # Make predictions\n",
    " y_pred = model.predict(X_test)\n",
    " \n",
    " # Confusion matrix\n",
    " cm = confusion_matrix(y_test, y_pred)\n",
    " \n",
    " # Accuracy\n",
    " accuracy = accuracy_score(y_test, y_pred)\n",
    " \n",
    " # Precision\n",
    " precision = precision_score(y_test, y_pred)\n",
    " \n",
    " # Recall (Sensitivity)\n",
    " recall = recall_score(y_test, y_pred)\n",
    " \n",
    " # F1-score\n",
    " f1 = f1_score(y_test, y_pred)\n",
    " \n",
    " # Specificity\n",
    " tn, fp, fn, tp = cm.ravel()\n",
    " specificity = tn / (tn + fp)\n",
    " \n",
    " # Create a dictionary to store performance metrics\n",
    " performance_metrics = {\n",
    " 'confusion_matrix': cm,\n",
    " 'accuracy': accuracy,\n",
    " 'precision': precision,\n",
    " 'recall': recall,\n",
    " 'f1_score': f1,\n",
    " 'specificity': specificity\n",
    " }\n",
    " \n",
    " return performance_metrics\n",
    "# Evaluate KNN model\n",
    "knn_performance = evaluate_model(knn_model, X_test, y_test)\n",
    "# Evaluate Decision Tree model\n",
    "tree_performance = evaluate_model(tree_model, X_test, y_test)\n",
    "# Print performance metrics for KNN model\n",
    "print(\"Performance metrics for KNN model:\")\n",
    "for metric, value in knn_performance.items():\n",
    " print(f\"{metric}: {value}\")\n",
    "# Print performance metrics for Decision Tree model\n",
    "print(\"\\nPerformance metrics for Decision Tree model:\")\n",
    "for metric, value in tree_performance.items():\n",
    " print(f\"{metric}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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