{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7bd58c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ecd1ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <td>66</td>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
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       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('diabetes.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b3d17e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e307f834",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</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>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "29ba7ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val=data.isna().sum()\n",
    "missing_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5c0632ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
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       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_data=data.corr()\n",
    "corr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "36df3511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_data, annot=True,cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5dd3771d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3okeNSdKVV16pgwcPqq6uTgsWLJDb7ZYk+f1+DRs2LPo8v9+vCRMm9Lpfu90uu92e0OxAIkydOlVTp041OwYAAEBa4IgxIAk6Ojrk8XhijhjzeDzq6OgwORmQ/v7v//5PWVmx01l2drYikYgkadSoUXK73WpqaoquDwaD2rVrl8rKypKaFQAAAEBqoRgDEswwDK1evbrXcU6rBC7O9ddfrx/96Ed69tln9Y9//ENbtmxRfX29brzxRknvXYB86dKl+uEPf6jf/va32rt3r+bPn6/hw4dr9uzZ5oYHAAAAYCpOpQQSzOv1qrW19Yzx7u5utba2yuv1asSIESYkAzLDz372M33ve9/TN77xDR05ckTDhw/X1772NS1fvjy6zV133aXOzk599atf1fHjx/WpT31K27dv5/pLAAAAgMXZjAw4XCUYDMrpdCoQCCg/P9/sOEAMwzB01113qa2tLXpql/TeqV7FxcW67777ZLPZTEwInIn31TPxmgBAfPG+CgBIBZxKCSSYzWbTkiVLzjhl0jAMLVmyhFIMAAAAAACTUIwBJjEMg+uLAQAAAABgIooxIMFOX2T/w0eG2Ww2Lr4PAAAAAICJKMaABDt98f0PXl9MkiKRSPTi+wAAAAAAIPkoxoAEKyoqUklJibKzs2PGs7OzNXnyZBUVFZmUDAAAAAAAa6MYAxLs9MX3exvn4vsAAAAAAJiDYgxIgoKCAs2dOzdagtlsNs2dO1eXXnqpyckAAAAAALAuijEgSebNm6fBgwdLkoYMGaK5c+eanAgAAAAAAGujGAOSxOFwqKamRi6XS8uWLZPD4TA7EgAAAAAAltbH7ACAlUydOlVTp041OwYAAAAAABBHjAEAAAAAAMCiKMYAAAAAAABgSRRjAAAAAAAAsCSKMQAAAAAAAFgSxRiQRM3NzZozZ46am5vNjgIAAAAAgOVRjAFJEgqFVF9fL7/fr/r6eoVCIbMjAQAAAABgaRRjQJJs3LhRx44dkyQdO3ZMHo/H5EQAAAAAAFgbxRiQBB0dHfJ4PDIMQ5JkGIY8Ho86OjpMTgYAAAAAgHVRjAEJZhiGVq9e3ev46bIMAAAAAAAkF8UYkGBer1etra3q7u6OGe/u7lZra6u8Xq9JyQAAAAAAsDaKMSDBioqKVFJSouzs7Jjx7OxsTZ48WUVFRSYlAwAAAADA2ijGgASz2WxasmRJr+M2m82EVACATNXc3Kw5c+aoubnZ7CgAAAApj2IMSIKCggLNnTs3WoLZbDbNnTtXl156qcnJAACZJBQKqb6+Xn6/X/X19QqFQmZHAgAASGkUY0CSzJs3T4MHD5YkDRkyRHPnzjU5EQAg02zcuFHHjh2TJB07dkwej8fkRAAAAKmNYgxIEofDoZqaGrlcLi1btkwOh8PsSACADNLR0SGPxxO927FhGPJ4POro6DA5GQAAQOqiGAOSaOrUqdq8ebOmTp1qdhQAQAYxDEOrV6/udfx0WQYAAIBYFGMAAABpzuv1qrW1Vd3d3THj3d3dam1tldfrNSkZAABAaqMYAwAASHNFRUUqKSlRdnZ2zHh2drYmT56soqIik5IBAACkNooxAACANGez2bRkyZJex0/fFRkAAACx4l6M1dXVqaSkRHl5eRo6dKhmz56t/fv3f+TznnzySY0ePVoOh0NXXnmlnnvuuXhHAwAAyFgFBQVn3PF47ty5uvTSS01KBAAAkPriXozt2LFDVVVV2rlzp1544QWdOnVK1157rTo7O3t9TnNzs2699VYtWrRIf/zjHzV79mzNnj1b+/bti3c8AEAGGjlypGw22xlLVVWVJCkUCqmqqkqDBw/WgAEDVFlZKb/fb3JqIP4qKyuVlfXeP++ysrJ00003mZwIAAAgtdmMBN+m6O2339bQoUO1Y8cOTZ8+vcdt5syZo87OTm3bti06NmXKFE2YMEHr1q37yO8RDAbldDoVCASUn58ft+wAYFXp9r769ttvx1x0fN++fbrmmmv0+9//XjNmzNCdd96pZ599Vhs2bJDT6VR1dbWysrL06quvnvP3SLfXBNb0yCOP6IknnpBhGLLZbPrSl76k22+/3exYQI94XwUApIKEX2MsEAhIkgYNGtTrNi0tLSovL48Zq6ioUEtLS4/bh8NhBYPBmAUAYF2XXHKJ3G53dNm2bZs+/vGP69Of/rQCgYAeeeQR1dfXa+bMmSouLtb69evV3NysnTt3mh0diJuOjg55PB6d/szTMAx5PB51dHSYnAwAACB1JbQYi0QiWrp0qaZNm6Zx48b1up3P55PL5YoZc7lc8vl8PW5fV1cnp9MZXQoLC+OaGwCQvrq6uvTEE0/o9ttvl81mU1tbm06dOhXzAczo0aNVVFTU6wcwEh/CIL0YhqHVq1f3Op7gEwQAAADSVkKLsaqqKu3bt0+bNm2K635ra2sVCASiS3t7e1z3DwBIX1u3btXx48f15S9/WdJ7H77k5ORo4MCBMdud7QMYiQ9hkF68Xq9aW1tjTimWpO7ubrW2tsrr9ZqUDAAAILUlrBirrq7Wtm3b9Pvf/14FBQVn3dbtdp9xEWS/3y+3293j9na7Xfn5+TELAADSe9dYmjVrloYPH35R++FDGKSToqIilZSU9Lhu8uTJKioqSnIiAACA9BD3YswwDFVXV2vLli166aWXNGrUqI98TllZmZqammLGXnjhBZWVlcU7HgAggx08eFAvvviivvKVr0TH3G63urq6dPz48Zhtz/YBjMSHMEgvNptNn/3sZ3tc99nPflY2my3JiQAAANJD3IuxqqoqPfHEE/J4PMrLy5PP55PP59M777wT3Wb+/Pmqra2NPl6yZIm2b9+uBx54QG+++aa+//3v6/XXX1d1dXW84wEAMtj69es1dOhQXXfdddGx4uJi9e3bN+YDmP3798vr9fIBDDJGJBLRmjVrelzX0NCgSCSS5EQAAADpIe7F2Nq1axUIBDRjxgwNGzYsumzevDm6jdfr1eHDh6OPp06dKo/Ho4cffljjx4/Xf//3f2vr1q1nvWA/AAAfFIlEtH79ei1YsEB9+vSJjjudTi1atEg1NTX6/e9/r7a2Ni1cuFBlZWWaMmWKiYmB+Nm1a1evN4gIBoPatWtXkhMBAACkhz4fvcn5OZe7Hr388stnjH3xi1/UF7/4xXjHAVJKc3OzVq9erSVLlmjq1KlmxwEyyosvviiv16vbb7/9jHUrV65UVlaWKisrFQ6HVVFR0evRNUA6Ki0tVX5+fo/lmNPpVGlpqQmpAAAAUl9C70oJ4H2hUEj19fXy+/2qr69XKBQyOxKQUa699loZhqFPfvKTZ6xzOBxqaGjQ//7v/6qzs1NPPfXUWa8vBqSbrKwsfeMb3+hx3Te+8Q1lZfFPPgAAgJ7wryQgSTZu3Khjx45Jko4dOyaPx2NyIgBApjAM44wbGZ324osvntMR/QAAAFZEMQYkQUdHhzweT/QPE8Mw5PF41NHRYXIyAEAm8Hq9am1t7XFda2urvF5vkhMBAACkB4oxIMEMw9Dq1at7HedTfADAxSoqKlJJSckZp0xmZWVp8uTJKioqMikZAABAaqMYAxLs9Kf43d3dMePd3d18ig8AiAubzaYlS5YoEonEjEciES1ZskQ2m82kZAAAAKmNYgxIsNOf4mdnZ8eMZ2dn8yk+ACBu9u3b1+P43r17k5wEAAAgfVCMAQl2+lP83sb5FB8AcLG6u7v105/+tMd1P/3pT884ahkAAADvoRgDkqCgoEBz586NlmA2m01z587VpZdeanIyAEAm2LZtW6/lV3d3t7Zt25bkRAAAAOmBYgxIknnz5mnw4MGSpCFDhmju3LkmJwIAZIovfOELZ5yyf1qfPn30hS98IcmJAAAA0kMfswMAVuFwOFRTU6PVq1dryZIlcjgcZkcCgLRmGIZCoZDZMVLG0qVL9cADD/Q43tXVZUKi1OJwOLh8AQAAOIPNMAzD7BAXKxgMyul0KhAIKD8/3+w4AJD2eF89E69J6nnnnXc0a9Yss2MgTTQ2Nio3N9fsGPgA3lcBAKmAUymBJGpubtacOXPU3NxsdhQAAAAAACyPUymBJAmFQqqvr9fRo0dVX1+vSZMmcTolAFwEh8OhxsZGs2OklFAopBtvvFGSNG3aNN19990mJ0odzLkAAKAnFGNAkmzcuFHHjh2TJB07dkwej0e33367yakAIH3ZbDZOjTuLu+++m9cHAADgI3AqJZAEHR0d8ng8On1JP8Mw5PF41NHRYXIyAAAAAACsi2IMSDDDMLR69epexzPg/hcAAAAAAKQlijEgwbxer1pbW9Xd3R0z3t3drdbWVnm9XpOSAQAAAABgbRRjQIIVFRWppKRE2dnZMePZ2dmaPHmyioqKTEoGAAAAAIC1UYwBCWaz2bRkyZIeT5lcsmSJbDabCakAAAAAAADFGJAEBQUFuuKKK2LGrrjiCl166aUmJQIAAAAAABRjQBJ0dHTojTfeiBl74403uCslAAAAAAAmohgDEuz03ScjkUjMeHd3N3elBAAAAADARBRjQIKdvivlhwswwzC4KyUAAAAAACaiGAMSrKioSJ/85Cd7XHf55ZdzV0oAAAAAAExCMQYkmGEYOnToUI/r/vnPf3IqJQAAAAAAJqEYAxJs165dOnnyZI/rTp48qV27diU5EQAAAAAAkCjGgIQrLS1Vfn5+j+ucTqdKS0uTnAgAAAAAAEgUY0DCZWVlafny5T2uW7FihbKy+N8QAAAAAAAz8Bc5kARXX321rrzyypixq666SpMmTTIpEQAAAAAAoBgDkuTee++NHh2WlZWle+65x+REAAAAAABYG8UYkCQDBw7U9OnTJUnTp0/XwIEDzQ0EAAAAAIDFUYwBSRIKhbRv3z5J0r59+xQKhUxOBGSOf/7zn7rttts0ePBg5ebm6sorr9Trr78eXW8YhpYvX65hw4YpNzdX5eXl+tvf/mZiYgAAAACpgGIMSJKNGzfq2LFjkqRjx47J4/GYnAjIDP/6178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      "text/plain": [
       "<Figure size 1500x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(15, 12))\n",
    "plt.subplot(3,3,1)\n",
    "bp=sns.boxplot(data['Pregnancies'])\n",
    "plt.subplot(3,3,2)\n",
    "bp=sns.boxplot(data['Glucose'])\n",
    "plt.subplot(3,3,3)\n",
    "bp=sns.boxplot(data['BloodPressure'])\n",
    "plt.subplot(3,3,4)\n",
    "bp=sns.boxplot(data['SkinThickness'])\n",
    "plt.subplot(3,3,5)\n",
    "bp=sns.boxplot(data['Insulin'])\n",
    "plt.subplot(3,3,6)\n",
    "bp=sns.boxplot(data['BMI'])\n",
    "plt.subplot(3,3,7)\n",
    "bp=sns.boxplot(data['DiabetesPedigreeFunction'])\n",
    "plt.subplot(3,3,8)\n",
    "bp=sns.boxplot(data['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6c544ea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentiles: 25th=0.462, 75th=61.000, IQR=60.538\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</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>762</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "3              1       89             66             23       94  28.1   \n",
       "5              5      116             74              0        0  25.6   \n",
       "6              3       78             50             32       88  31.0   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "762            9       89             62              0        0  22.5   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "3                       0.167   21        0  \n",
       "5                       0.201   30        0  \n",
       "6                       0.248   26        1  \n",
       "..                        ...  ...      ...  \n",
       "762                     0.142   33        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[536 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import percentile\n",
    "# calculate interquartile range\n",
    "q25, q75 = percentile(data, 25), percentile(data, 75)\n",
    "iqr = q75 - q25\n",
    "print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr))\n",
    "# calculate the outlier cutoff\n",
    "cut_off = iqr * 1.5\n",
    "lower, upper = q25 - cut_off, q75 + cut_off\n",
    "# identify outliers\n",
    "df = data[~((data < (q25 - 1.5 * iqr)) |(data > (q75 + 1.5 * iqr))).any(axis=1)]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "438e9316",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(15, 12))\n",
    "plt.subplot(3,3,1)\n",
    "bp=sns.boxplot(df['Pregnancies'])\n",
    "plt.subplot(3,3,2)\n",
    "bp=sns.boxplot(df['Glucose'])\n",
    "plt.subplot(3,3,3)\n",
    "bp=sns.boxplot(df['BloodPressure'])\n",
    "plt.subplot(3,3,4)\n",
    "bp=sns.boxplot(df['SkinThickness'])\n",
    "plt.subplot(3,3,5)\n",
    "bp=sns.boxplot(df['Insulin'])\n",
    "plt.subplot(3,3,6)\n",
    "bp=sns.boxplot(df['BMI'])\n",
    "plt.subplot(3,3,7)\n",
    "bp=sns.boxplot(df['DiabetesPedigreeFunction'])\n",
    "plt.subplot(3,3,8)\n",
    "bp=sns.boxplot(df['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64489a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0de949d6",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# X = feature values, all the columns except the species column\n",
    "X = data.iloc[:, :-1]\n",
    "display(X)\n",
    "\n",
    "# y = target values, only the species column\n",
    "y = data.iloc[:, -1]\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "af7c8ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b6ac071c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  4.25592D+02    |proj g|=  7.37050D+03\n",
      "\n",
      "At iterate   50    f=  3.73430D+02    |proj g|=  1.32122D+02\n",
      "\n",
      "At iterate  100    f=  2.90810D+02    |proj g|=  4.94892D+02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    9    100    112      1     0     0   4.949D+02   2.908D+02\n",
      "  F =   290.80979408501042     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      " This problem is unconstrained.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(verbose=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Train the model\n",
    "model = LogisticRegression(verbose=1)\n",
    "model.fit(x_train, y_train) #Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "33192d04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0\n",
      " 0 1 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0\n",
      " 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 1 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 1\n",
      " 0 0 1 0 0 0]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.84      0.81        96\n",
      "           1       0.70      0.60      0.65        58\n",
      "\n",
      "    accuracy                           0.75       154\n",
      "   macro avg       0.74      0.72      0.73       154\n",
      "weighted avg       0.75      0.75      0.75       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Test the model\n",
    "predictions = model.predict(x_test)\n",
    "print(predictions)# printing predictions\n",
    "\n",
    "print()# Printing new line\n",
    "\n",
    "#Check precision, recall, f1-score\n",
    "print( classification_report(y_test, predictions) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042386d4",
   "metadata": {},
   "source": [
    "# Using KNN method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ef326578",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "distortions = []\n",
    "inertias = []\n",
    "mapping1 = {}\n",
    "mapping2 = {}\n",
    "K = range(1, 10)\n",
    " \n",
    "for k in K:\n",
    "    # Building and fitting the model\n",
    "    kmeanModel = KMeans(n_clusters=k).fit(X)\n",
    "    kmeanModel.fit(X)\n",
    " \n",
    "    distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_,\n",
    "                                        'euclidean'), axis=1)) / X.shape[0])\n",
    "    inertias.append(kmeanModel.inertia_)\n",
    " \n",
    "    mapping1[k] = sum(np.min(cdist(X, kmeanModel.cluster_centers_,\n",
    "                                   'euclidean'), axis=1)) / X.shape[0]\n",
    "    mapping2[k] = kmeanModel.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1ba940b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('Values of K')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method using Distortion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b00c0e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(K, inertias, 'bx-')\n",
    "plt.xlabel('Values of K')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('The Elbow Method using Inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b861318d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Using KNN Classifier for model building \n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors=7)\n",
    "KNN.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0fa39a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = KNN.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "00e8aaac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN with K=3 is 73.38%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#validation of built model\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('The accuracy of the KNN with K=3 is {}%'.format(round(accuracy_score(pred,y_test)*100,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a5e293",
   "metadata": {},
   "source": [
    "# After removing outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94e2623",
   "metadata": {},
   "source": [
    "# Logistic regression algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1d45bc1f",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "3              1       89             66             23       94  28.1   \n",
       "5              5      116             74              0        0  25.6   \n",
       "6              3       78             50             32       88  31.0   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "762            9       89             62              0        0  22.5   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "3                       0.167   21  \n",
       "5                       0.201   30  \n",
       "6                       0.248   26  \n",
       "..                        ...  ...  \n",
       "762                     0.142   33  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[536 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "3      0\n",
       "5      0\n",
       "6      1\n",
       "      ..\n",
       "762    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 536, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = feature values, all the columns except the species column\n",
    "X = df.iloc[:, :-1]\n",
    "display(X)\n",
    "\n",
    "# y = target values, only the species column\n",
    "y = df.iloc[:, -1]\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b0f0460a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "79b2be77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.96667D+02    |proj g|=  1.10990D+04\n",
      "\n",
      "At iterate   50    f=  2.02517D+02    |proj g|=  4.42026D+01\n",
      "\n",
      "At iterate  100    f=  1.97367D+02    |proj g|=  4.69292D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    9    100    117      1     0     0   4.693D-01   1.974D+02\n",
      "  F =   197.36675032332181     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      " This problem is unconstrained.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(verbose=1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model\n",
    "model = LogisticRegression(verbose=1)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4d518a37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.98      0.90        82\n",
      "           1       0.85      0.42      0.56        26\n",
      "\n",
      "    accuracy                           0.84       108\n",
      "   macro avg       0.84      0.70      0.73       108\n",
      "weighted avg       0.84      0.84      0.82       108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Test the model\n",
    "predictions = model.predict(x_test)\n",
    "print(predictions)# printing predictions\n",
    "\n",
    "print()# Printing new line\n",
    "\n",
    "#Check precision, recall, f1-score\n",
    "print( classification_report(y_test, predictions) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada0bcab",
   "metadata": {},
   "source": [
    "# KNN Algotithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e557db81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=7)\n",
    "KNN.fit(x_train, y_train)\n",
    "pred = KNN.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5528c2d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN with K=7 is 82.41%\n"
     ]
    }
   ],
   "source": [
    "#validation of built model\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('The accuracy of the KNN with K=7 is {}%'.format(round(accuracy_score(pred,y_test)*100,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee4d7bfe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b51c60b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e8eac62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_filename= \"knn_pickle.pkl\"\n",
    "\n",
    "with open(\"knn_pickle.pkl\", \"wb\") as f:\n",
    "    pickle.dump(KNN,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6b6150e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(pkl_filename, \"rb\") as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2fc32a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cbe5d4ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "Setting up a public link... we have recently upgraded the way public links are generated. If you encounter any problems, please report the issue and downgrade to gradio version 3.13.0\n",
      ".\n",
      "Running on public URL: https://d94a2ec9-ca36-46c1.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://d94a2ec9-ca36-46c1.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_prediction(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age):\n",
    "    with open(pkl_filename, \"rb\") as f:\n",
    "        model  = pickle.load(f)\n",
    "        preds = model.predict([[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age]])\n",
    "    if preds == 1:\n",
    "            return \"patient having diabetese\"\n",
    "    return \"patient does not have diabetese\"\n",
    "\n",
    "#Create the input component for Gradio since we are expecting 4 inputs\n",
    "\n",
    "Pregnancies=gr.Number(label = \"Preg_number\")\n",
    "Glucose= gr.Number(label = \"glucose level\")\n",
    "BP = gr.Number(label = \"BP Level\")\n",
    "SkinThickness = gr.Number(label = \"Pkin thickness\")\n",
    "Insulin = gr.Number(label = \"insulin level\")\n",
    "BMI= gr.Number(label = \"BMI Level\")\n",
    "Dpf = gr.Number(label = \"dpf\")\n",
    "Age= gr.Number(label = \"Patients age\")\n",
    "# We create the output\n",
    "output = gr.Textbox()\n",
    "\n",
    "\n",
    "app = gr.Interface(fn = make_prediction, inputs=[Pregnancies, Glucose, BP, SkinThickness, Insulin, BMI, Dpf, Age], outputs=output)\n",
    "app.launch(share =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02257143",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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