ML.Random Forest(Diabet DataSet) by parisan ahmadi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ab434a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4983352b",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "238f1b29",
   "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",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
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       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
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       "      <td>31</td>\n",
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       "      <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",
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       "      <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",
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       "    <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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       "      <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",
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       "    <tr>\n",
       "      <th>764</th>\n",
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       "      <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",
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       "      <th>765</th>\n",
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       "      <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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       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
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       "      <td>0</td>\n",
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       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
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       "  </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"diabetes.csv\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57d05596",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a7558ce",
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   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>Outcome</th>\n",
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       "  </thead>\n",
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       "      <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",
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      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d53d782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a2c2e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
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     "execution_count": 7,
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    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "323fe530",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
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      ],
      "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",
       "   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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0c5f487",
   "metadata": {},
   "outputs": [
    {
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       "    .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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>763</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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          False    False          False          False    False  False   \n",
       "1          False    False          False          False    False  False   \n",
       "2          False    False          False          False    False  False   \n",
       "3          False    False          False          False    False  False   \n",
       "4          False    False          False          False    False  False   \n",
       "..           ...      ...            ...            ...      ...    ...   \n",
       "763        False    False          False          False    False  False   \n",
       "764        False    False          False          False    False  False   \n",
       "765        False    False          False          False    False  False   \n",
       "766        False    False          False          False    False  False   \n",
       "767        False    False          False          False    False  False   \n",
       "\n",
       "     DiabetesPedigreeFunction    Age  Outcome  \n",
       "0                       False  False    False  \n",
       "1                       False  False    False  \n",
       "2                       False  False    False  \n",
       "3                       False  False    False  \n",
       "4                       False  False    False  \n",
       "..                        ...    ...      ...  \n",
       "763                     False  False    False  \n",
       "764                     False  False    False  \n",
       "765                     False  False    False  \n",
       "766                     False  False    False  \n",
       "767                     False  False    False  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8161a78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 False\n",
       "Glucose                     False\n",
       "BloodPressure               False\n",
       "SkinThickness               False\n",
       "Insulin                     False\n",
       "BMI                         False\n",
       "DiabetesPedigreeFunction    False\n",
       "Age                         False\n",
       "Outcome                     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2c3aff2",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae944ad4",
   "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",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "320d490e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75      0\n",
       "182     0\n",
       "342     0\n",
       "349     0\n",
       "502     0\n",
       "62     44\n",
       "680    56\n",
       "146    57\n",
       "537    57\n",
       "352    61\n",
       "Name: Glucose, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Glucose\"].nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66c953aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7      0\n",
       "15     0\n",
       "49     0\n",
       "60     0\n",
       "78     0\n",
       "81     0\n",
       "172    0\n",
       "193    0\n",
       "222    0\n",
       "261    0\n",
       "Name: BloodPressure, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"BloodPressure\"].nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b739bf8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Glucose\"]=data[\"Glucose\"].replace(0,np.nan)\n",
    "data[\"BloodPressure\"]=data[\"BloodPressure\"].replace(0,np.nan)\n",
    "data[\"SkinThickness\"]=data[\"SkinThickness\"].replace(0,np.nan)\n",
    "data[\"Insulin\"]=data[\"Insulin\"].replace(0,np.nan)\n",
    "data[\"BMI\"]=data[\"BMI\"].replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36e5ec68",
   "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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</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>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</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>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</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>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0      NaN  33.6   \n",
       "1            1     85.0           66.0           29.0      NaN  26.6   \n",
       "2            8    183.0           64.0            NaN      NaN  23.3   \n",
       "3            1     89.0           66.0           23.0     94.0  28.1   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \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  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86b9d6f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a667d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.0000\n",
       "Glucose                     117.0000\n",
       "BloodPressure                72.0000\n",
       "SkinThickness                29.0000\n",
       "Insulin                     125.0000\n",
       "BMI                          32.3000\n",
       "DiabetesPedigreeFunction      0.3725\n",
       "Age                          29.0000\n",
       "Outcome                       0.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72252cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(data.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bc4752c",
   "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",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</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>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</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>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</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>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</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>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</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>78.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>88.0</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>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0    125.0  33.6   \n",
       "1            1     85.0           66.0           29.0    125.0  26.6   \n",
       "2            8    183.0           64.0           29.0    125.0  23.3   \n",
       "3            1     89.0           66.0           23.0     94.0  28.1   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \n",
       "5            5    116.0           74.0           29.0    125.0  25.6   \n",
       "6            3     78.0           50.0           32.0     88.0  31.0   \n",
       "7           10    115.0           72.0           29.0    125.0  35.3   \n",
       "8            2    197.0           70.0           45.0    543.0  30.5   \n",
       "9            8    125.0           96.0           29.0    125.0  32.3   \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",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ba3cbe7",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5d9f0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "plt.subplot(4,4,1)\n",
    "sb.boxplot(data[\"Pregnancies\"])\n",
    "\n",
    "plt.subplot(4,4,2)\n",
    "sb.boxplot(data[\"Glucose\"])\n",
    "\n",
    "plt.subplot(4,4,3)\n",
    "sb.boxplot(data[\"BloodPressure\"])\n",
    "\n",
    "plt.subplot(4,4,4)\n",
    "sb.boxplot(data[\"SkinThickness\"])\n",
    "\n",
    "\n",
    "plt.subplot(4,4,5)\n",
    "sb.boxplot(data[\"Insulin\"])\n",
    "\n",
    "plt.subplot(4,4,6)\n",
    "sb.boxplot(data[\"BMI\"])\n",
    "\n",
    "plt.subplot(4,4,7)\n",
    "sb.boxplot(data[\"DiabetesPedigreeFunction\"])\n",
    "\n",
    "plt.subplot(4,4,8)\n",
    "sb.boxplot(data[\"Age\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff9fa6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Pregnancies\"]=data[\"Pregnancies\"].clip(lower=data[\"Pregnancies\"].quantile(0.05),upper=data[\"Pregnancies\"].quantile(0.95))\n",
    "data[\"BloodPressure\"]=data[\"BloodPressure\"].clip(lower=data[\"BloodPressure\"].quantile(0.05),upper=data[\"BloodPressure\"].quantile(0.95))\n",
    "data[\"SkinThickness\"]=data[\"SkinThickness\"].clip(lower=data[\"SkinThickness\"].quantile(0.07),upper=data[\"SkinThickness\"].quantile(0.93))\n",
    "data[\"Insulin\"]=data[\"Insulin\"].clip(lower=data[\"Insulin\"].quantile(0.25),upper=data[\"Insulin\"].quantile(0.75))\n",
    "data[\"BMI\"]=data[\"BMI\"].clip(lower=data[\"BMI\"].quantile(0.05),upper=data[\"BMI\"].quantile(0.95))\n",
    "data[\"DiabetesPedigreeFunction\"]=data[\"DiabetesPedigreeFunction\"].clip(lower=data[\"DiabetesPedigreeFunction\"].quantile(0.05),upper=data[\"DiabetesPedigreeFunction\"].quantile(0.95))\n",
    "data[\"Age\"]=data[\"Age\"].clip(lower=data[\"Age\"].quantile(0.05),upper=data[\"Age\"].quantile(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a31fc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "plt.subplot(4,4,1)\n",
    "sb.boxplot(data[\"Pregnancies\"])\n",
    "\n",
    "plt.subplot(4,4,2)\n",
    "sb.boxplot(data[\"Glucose\"])\n",
    "\n",
    "plt.subplot(4,4,3)\n",
    "sb.boxplot(data[\"BloodPressure\"])\n",
    "\n",
    "plt.subplot(4,4,4)\n",
    "sb.boxplot(data[\"SkinThickness\"])\n",
    "\n",
    "\n",
    "plt.subplot(4,4,5)\n",
    "sb.boxplot(data[\"Insulin\"])\n",
    "\n",
    "plt.subplot(4,4,6)\n",
    "sb.boxplot(data[\"BMI\"])\n",
    "\n",
    "plt.subplot(4,4,7)\n",
    "sb.boxplot(data[\"DiabetesPedigreeFunction\"])\n",
    "\n",
    "plt.subplot(4,4,8)\n",
    "sb.boxplot(data[\"Age\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78fb49ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc8e010f",
   "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>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.35100</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.67200</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.16700</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.13285</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>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.24500</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.31500</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    148.0           72.0           35.0   125.00  33.6   \n",
       "1              1     85.0           66.0           29.0   125.00  26.6   \n",
       "2              8    183.0           64.0           29.0   125.00  23.3   \n",
       "3              1     89.0           66.0           23.0   121.50  28.1   \n",
       "4              0    137.0           52.0           35.0   127.25  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10    101.0           76.0           42.0   127.25  32.9   \n",
       "764            2    122.0           70.0           27.0   125.00  36.8   \n",
       "765            5    121.0           72.0           23.0   121.50  26.2   \n",
       "766            1    126.0           60.0           29.0   125.00  30.1   \n",
       "767            1     93.0           70.0           31.0   125.00  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                     0.62700   50  \n",
       "1                     0.35100   31  \n",
       "2                     0.67200   32  \n",
       "3                     0.16700   21  \n",
       "4                     1.13285   33  \n",
       "..                        ...  ...  \n",
       "763                   0.17100   58  \n",
       "764                   0.34000   27  \n",
       "765                   0.24500   30  \n",
       "766                   0.34900   47  \n",
       "767                   0.31500   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=pd.DataFrame(data,columns=[\"Pregnancies\",\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"])\n",
    "y=data.Outcome.values.reshape(-1,1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9274591e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d38854e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=RandomForestClassifier(n_estimators=50, max_depth=3)\n",
    "\n",
    "clf=clf.fit(x_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09aa8042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7792207792207793\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy :\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37190a2e",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1782e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "54d177bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [\"Pregnancies\",\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]\n",
    "target_names=[\"0\",\"1\"]\n",
    "fig=plt.figure(figsize=(20,20),dpi=100)\n",
    "plot=tree.plot_tree(clf.estimators_[5], feature_names = feature_names ,filled=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "484f5b4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}