diabetes prediction model using knn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "confused-showcase",
   "metadata": {},
   "source": [
    "import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "inside-spectrum",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constitutional-hampton",
   "metadata": {},
   "source": [
    "Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "expired-document",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape (768, 9)\n"
     ]
    },
    {
     "data": {
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       "    }\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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('diabetes.csv')\n",
    "print(\"Shape\",df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lesser-velvet",
   "metadata": {},
   "source": [
    "Measuring the statistical values of the dataset-range of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "scenic-terror",
   "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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "through-andrew",
   "metadata": {},
   "source": [
    "#proportion of non diabetic cases is more-500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "possible-password",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "atlantic-accessory",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.298000</td>\n",
       "      <td>109.980000</td>\n",
       "      <td>68.184000</td>\n",
       "      <td>19.664000</td>\n",
       "      <td>68.792000</td>\n",
       "      <td>30.304200</td>\n",
       "      <td>0.429734</td>\n",
       "      <td>31.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.865672</td>\n",
       "      <td>141.257463</td>\n",
       "      <td>70.824627</td>\n",
       "      <td>22.164179</td>\n",
       "      <td>100.335821</td>\n",
       "      <td>35.142537</td>\n",
       "      <td>0.550500</td>\n",
       "      <td>37.067164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "Outcome                                                                      \n",
       "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
       "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction        Age  \n",
       "Outcome                                                  \n",
       "0        30.304200                  0.429734  31.190000  \n",
       "1        35.142537                  0.550500  37.067164  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Outcome').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "inner-charm",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperating the data and labels\n",
    "X = df.drop(columns ='Outcome',axis=1)\n",
    "Y= df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "leading-cemetery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "administrative-niagara",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "extraordinary-knight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "documentary-blast",
   "metadata": {},
   "source": [
    "graphs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "talented-cosmetic",
   "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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "chief-stand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'correlation matrix of squared correlations \\n\\n\\n\\n\\n')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (19,15))\n",
    "plt.matshow(df.corr()**2,fignum = fig.number)\n",
    "plt.xticks(range(df.shape[1]),df.columns,fontsize = 14, rotation = 45)\n",
    "plt.yticks(range(df.shape[1]),df.columns,fontsize = 14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize = 14)\n",
    "plt.title(\"correlation matrix of squared correlations \\n\\n\\n\\n\\n\",fontsize =20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "processed-hypothesis",
   "metadata": {},
   "source": [
    "check which columns are useful and which are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "above-cincinnati",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    500\n",
      "1    268\n",
      "Name: Outcome, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1-->diabetic\n",
    "#0-->healthy\n",
    "\n",
    "print(df[\"Outcome\"].value_counts())\n",
    "fig = plt.figure(figsize = (10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "together-auckland",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Glucose')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#glucose for diabetic\n",
    "fig = plt.figure(figsize =(16,6))\n",
    "\n",
    "sns.distplot(df[\"Glucose\"][df[\"Outcome\"] == 1])\n",
    "plt.xticks([i for i in range(0,201,15)],rotation = 45)\n",
    "plt.ylabel(\"Glucose count\")\n",
    "plt.title(\"Glucose\",fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "official-replacement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Insulin')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#insulin for diabetic \n",
    "\n",
    "fig = plt.figure(figsize = (16,6))\n",
    "\n",
    "sns.distplot(df[\"Insulin\"][df[\"Outcome\"]==1])\n",
    "plt.xticks()\n",
    "plt.title(\"Insulin\",fontsize = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "confirmed-search",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'BMI')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI for diabetic\n",
    "fig = plt.figure(figsize =(16,6))\n",
    "\n",
    "sns.distplot(df[\"BMI\"][df[\"Outcome\"]==1])\n",
    "plt.xticks()\n",
    "plt.title(\"BMI\",fontsize = 20)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "immune-presence",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'diabetespedigreefunction')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RSW4Jm4psZg8BJwL5ZlYG/BxIB3D3ScB5wDfMrBbYBVzo7g7UmtmNwItAFLjb3T9KVE4RERERERFJbQkrbN39ov203wrc2kzbZGByInKJiIiIiIhI2xL2VZFFREREREREDooKWxEREREREUlpKmxFREREREQkpamwFRERERERkZSmwlZERERERERSmgpbERERERERSWkqbEVERERERCSlqbAVERERERGRlKbCVkRERERERFJaWtgBROTg1NbVU7m7FgAzAyA7PUpGmv5uJSIiIiLtgwpbkRS0fVcNC9dXsGBdBUvKK6muq9+rPWJQ3DWHQd1zGdy9I4VdsokERa+IiIiISFujwlYkhZRvr+K5OWtZvKESgLzsdEYX59G7czYY4LF+m3dWs7i8klfnr+eV+evJzUrjhCEFHNWvK+lRjeSKiIiISNuiwlYkBVTX1vP6wnLeWrSBjLQIXzi8B8N6d6JHbuae6ceNnXYE7Nhdy+INlby3bDPPzVnLlI83cOJh3Snp14W0iApcEREREWkbVNiKJLmF67bzzw/WsHVXDWOKu3D68J50zGzZP90OmWmMLMpjZFEeSzZU8vK89Twzew1vLdrAeWP70D+/Q4LTi4iIiIgkngpbkST23rLN/POD1RTkZnLNcQMOqhAdWNCRAcd3YFF5Jc/MXsOdby3lhCEFnHx4D6IRnX8rIiIiIqlLha1Iknrz4w28+NE6hvToyMXj+h6SqxybGUN65PKtzw/i+TlreePjDSwqr+SrJX3Iz808BKlFRERERFqfTrITSTLuzgtz1/LiR+sYUdSZrx19aIraeJlpUb4ypoiLxxWzeUc1t76+mPlrtx/SfYiIiIiItBYVtiJJxN15ZvYapizayLj+XbmgpE9CL/I0vLAz3z55MAW5mfzj3RW8s2RjwvYlIiIiIpIoKmxFksi0JZuYvmwzxw3OZ+LI3q1y79nO2elcc9wAhvbM5dk5a/n1c/Ooq/eE71dERERE5FBRYSuSJJZsqORfc9cyrFcnTjuiZ7O38UmEjLQIlxzdl2MGduOuqcv45gMzqaqpa7X9i4iIiIgcDBW2Iklgy85qHnpvJd06ZnL+2KJWGaltLGLGWSN68/Ozh/HSvPVcfV8pu6pV3IqIiIhI8lNhKxKymrp6Hpi+grp659LxfclMj4aa54pj+/P780by9pKNXHnvDHZW14aaR0RERERkf1TYioTs6fdXs3ZrVVLdcue8sUXc/NVRTF+2icvvnkHlbhW3IiIiIpK8VNiKhGju6m28v2orJw3tztBencKOs5eJowq55aLRzFy5hcvumk5FVU3YkUREREREmpSwwtbM7jazcjOb20z7JWY2J3hMM7ORcW3LzexDM/vAzEoTlVEkTNt21fDs7DX07pzFSYd1DztOk84a0ZtbLxrNnLJtXH1fqS4oJSIiIiJJKZEjtvcCp++jfRlwgruPAH4N3NGo/SR3H+XuJQnKJxKq/548nx3VtZwzpohopPUvFtVSZxzZiz9cMJL3lm/mxgdnUVNXH3YkEREREZG9JKywdfcpwOZ9tE9z9y3B03eBokRlEUk205Zs5OEZq/jcoHwK87LDjrNfE0cV8quJw3llfjn//vgc6nWfWxERERFJImlhBwhcBfwr7rkDL5mZA7e7e+PRXJGUVVVTx0+e/JC+3XL4/NAeYcdpsUuP7su2ndX870sf0ykrjV986YhWvdeuiIiIiEhzQi9szewkYoXt5+JWH+vua8ysO/CymS0IRoCb2v5a4FqA4uLihOcVOVh/enURyzft5MFrxrN8486w4xyQG04axLZdNfztrWV0zsnge6cMCTuSiIiIiEi4V0U2sxHAncBEd9/UsN7d1wRfy4GngHHNvYa73+HuJe5eUlBQkOjIIgdlcXkFd0xZyldL+nDMwPyw4xwwM+MnZx7OBSVF3PLqIu6auizsSCIiIiIi4Y3Ymlkx8CRwqbt/HLe+AxBx94pg+VTgVyHFFDmkfvfCQnLSo/z76YeFHeUzMzP++ysjqKiq5dfPzaNTVhrnl/QJO5aIiIiItGMJK2zN7CHgRCDfzMqAnwPpAO4+CfgZ0A34S3CeXm1wBeQewFPBujTgQXd/IVE5RVpL6fLNvDxvPT847TC6dcwMO85BiUaMmy8cReV9pfzwiTl0yk7ntCN6hh1LRERERNqphBW27n7RftqvBq5uYv1SYOSntxBJXe7Of02eT49OmVx5bP+w4xwSmWlRJn1tLF+7azrfevB97rniKI4dlHrTq0VEREQk9YV6jq1Ie/HiR+uZtXIr3/3CELIzomHHOWQ6ZKZx7+XjGFDQgWvvL2X2qq1hRxIRERGRdkiFrUiC1dTV8z8vLGBQ946cN7bt3a65c0469185jq4dM7j8nvdYXF4RdiQRERERaWdU2Iok2CMzVrF04w5+ePpQ0qJt859c905Z/OOq8aRFI3ztzvco25JatzESERERkdTWNj9liySJndW13PzKIo7q14UvHN497DgJ1bdbB+6/chw7q2u59K732Fi5O+xIIiIiItJOqLAVSaAHp69kY+Vufnj6UIIrfbdph/fqxN2XH8Xabbv4+t3vsb2qJuxIIiIiItIOtKiwNbMnzOyLZqZCWKSFqmrquGPKUiYM6EZJv65hx2k1Jf268tevjWXhugquvq+Uqpq6sCOJiIiISBvX0kL1r8DFwCIz+62ZDU1gJpE24YlZZZRX7ObGzw8KO0qrO+mw7vzhgpHMWL6ZGx+cRU1dfdiRRERERKQNa1Fh6+6vuPslwBhgOfCymU0zsyvMLD2RAUVSUW1dPZPeXMLIPnkcM7Bb2HFCMXFUIb/60hG8Mr+cHz4+h/p6DzuSiIiIiLRRLZ5abGbdgMuBq4H3gT8RK3RfTkgykRT2zOw1rNq8ixtPGtQuzq1tzqUT+vG9U4bw5Pur+eWzH+Gu4lZEREREDr20lnQysyeBocDfgbPdfW3Q9IiZlSYqnEgqqq93/vLGEob2zOXkoW37Ssgt8a3PD2LbrhrumrqM9GiEn37x8HZd7IuIiIjIodeiwha4090nx68ws0x33+3uJQnIJZKyXpq3jsXlldxy0WgiERVwZsZ/fPFwauvquXPqMtKiEX54+mEqbkVERETkkGlpYfufwORG694hNhVZRALuzq2vL6Zftxy+eGSvsOMkDTPjF186gpp6Z9KbS8iIGt879bCwY4mIiIhIG7HPwtbMegKFQLaZjQYahlg6ATkJziaScqYu3sjc1dv53blHEtVo7V7MjP+cOJy6OueW1xYTiRj/dvJgjdyKiIiIyEHb34jtacQuGFUE/DFufQXwkwRlEklZd09dRn7HTL48ujDsKEkpEjH++ytHUufOza8soqqmXtOSRUREROSg7bOwdff7gPvM7Fx3f6KVMomkpKUbKnl94Qa+84XBZKZFw46TtCIR43/OHUFWeoRJby6hqqaOn501TOcji4iIiMhntr+pyF9z938A/czse43b3f2PTWwm0i7dO205GdEIl4zvG3aUpBeJGL+eOJystCh3Tl3Gruo6/usrmr4tIiIiIp/N/qYidwi+dkx0EJFUtm1XDY/PLOPskb0pyM0MO05KMDN++sXDycmIcstri9lRXcsfLhip0W4REREROWD7m4p8e/D1l60TRyQ1PTpjFTur67ji2H5hR0kpZrGrI3fITOO//7WATZXVTLp0LJ2z08OOJiIiIiIpJNKSTmb2P2bWyczSzexVM9toZl9LdDiRVFBbV8+905Yzvn9Xhhd2DjtOSrruhIH831dHUrpiMxdMeoe123aFHUlEREREUkiLClvgVHffDpwFlAFDgB8kLJVICnll/npWb93FFcf2DztKSjtndBH3XD6O1Vt38ZW/TGPhuoqwI4mIiIhIimhpYdswL/BM4CF335ygPCIp5+63l1PUJZtThvUIO0rK+9zgfB657mjq6p1z/zqNV+evDzuSiIiIiKSAlha2z5rZAqAEeNXMCoCqxMUSSQ1zV2/jvWWbufyYfrqi7yFyRO/OPH3DsfTP78DV95dy2+uLcfewY4mIiIhIEmtRYevuPwImACXuXgPsACYmMphIKnhg+gqy06OcX9In7ChtSu+8bB67fgJfGtmb37+4kBsfep+d1bVhxxIRERGRJLW/2/3EO5zY/Wzjt7n/EOcRSRnbq2p4+v01fGlkb13FNwGy0qPc/NVRDOvVid++sIAl5ZXcdskYBhbo7mMiIiIisreWXhX578D/Ap8DjgoeJfvZ5m4zKzezuc20m5ndYmaLzWyOmY2JazvdzBYGbT9q8Xcj0oqefn81u2rq+NrRfcOO0maZGdedMJB7rxjH+u1VnP3nqTwxsyzsWCIiIiKSZFo6YlsCDPMDO9HtXuBWmh/VPQMYHDzGA38FxptZFLgNOIXYFZhnmNkz7j7vAPYtklDuzgPvrmREUWeOLNItfhLthCEF/OvfjufbD7/PTY/N5u0lG/n1xOF0yDyQSSciIiIi0la19OJRc4GeB/LC7j4F2NfVkycC93vMu0CemfUCxgGL3X2pu1cDD6PzeSXJlK7YwsL1FVwyvjjsKO1Gz85ZPHj1eL598mCeen81Z/95Ku+v3BJ2LBERERFJAi0tbPOBeWb2opk90/A4yH0XAqvinpcF65pb3yQzu9bMSs2sdMOGDQcZSaRlHnh3BblZaZw9snfYUdqVtGiE750yhAeuHk9VTR3n/nUav3thAbtr68KOJiIiIiIhauk8vl8kYN9N3RvF97G+Se5+B3AHQElJie4JIgm3qXI3kz9cx8Xji8nJ0FTYMBwzMJ8Xvns8v3luPn99Ywmvzl/P/54/khFFeWFHExEREZEQtPR2P28Cy4H0YHkGMOsg910GxN8jpQhYs4/1Iknh8ZllVNfVc7GmIYeqU1Y6vztvBPdccRTbdtVwzl+m8evn5lG5W7cFEhEREWlvWjTcZGbXANcCXYGBxKYGTwJOPoh9PwPcaGYPE7t41DZ3X2tmG4DBZtYfWA1cCFx8EPsR2ePB6SsPavt6d26fspR+3TpQunwLpct1jmfYTjqsOy995wR+9+IC7pq6jOfnrOXnZw/j9OE9MWtqAoiIiIiItDUtPcf2BuBYYDuAuy8Cuu9rAzN7CHgHOMzMyszsKjO73syuD7pMBpYCi4G/Ad8MXrsWuBF4EZgPPOruHx3QdyWSIEs2VLJ5RzXj+3cNO4rE6ZyTzn+dcyRPfvMYunTI4BsPzOLKe2ewbOOOsKOJiIiISCto6QmCu929umH0w8zS2Md5rwDuftF+2p1YwdxU22Riha9IUpmxfAs5GVGO6N0p7CjShDHFXXj2xmO5d9pybn5lEaf+35t8fUI/vnXyYDpnp4cdT0REREQSpKUjtm+a2U+AbDM7BXgMeDZxsUSSz47dtcxfs51RffJIi7b0n460trRohKuPG8Br3z+Bc8cUcdfbyzjpf9/g7++uoKauPux4IiIiIpIALR2x/RFwFfAhcB2x0dQ7ExVKJBl9sGorde6U9NU05NZ0MOdFjyjKI79jJs9/uJb/9/Rcbn75Y04Z1oPhhZ2JtKHzb3UhMxEREWnvWlTYunu9mT0NPO3uulmstDvuzswVWyjqkk3Pzllhx5ED0Dsvm6s/158F6yp4ad46Hp6xit4fb+DUI3oyuHtHXWBKREREpA3Y53xKi/mFmW0EFgALzWyDmf2sdeKJJIfVW3exbnsVY/t2CTuKfAZmxuG9OvGtzw/m/LFF7Kqp495py/nbW8tYsUkXmBIRERFJdfs7UfA7xK6GfJS7d3P3rsRuzXOsmX030eFEkkXp8i2kR42RRXlhR5GDEDFjdHEXvnvKEM4e2ZuNlbu5fcpS7n9nOWu37Qo7noiIiIh8RvsrbC8DLnL3ZQ0r3H0p8LWgTaTNq66tZ3bZVob37kxWejTsOHIIpEUiTBjQje+fehinDuvB8k07uPW1xTwyYyWbKneHHU9EREREDtD+zrFNd/eNjVe6+wYz070zpF2Yu2Ybu2vrKemni0a1NRlpEU48rDvj+ndlyscbeWfpRj5cvY2xfbvy+aHddYsgERERkRSxv8K2+jO2ibQZpcu30K1DBv265YQdRRIkJyON04f35JhB3Xhj4QZmLNvM+yu3ML5/V044rDsdM1t6AXkRERERCcP+Pq2NNLPtTaw3QJeGlTZvY+Vulm/awanDerSbq+cezO11Ul2nrHS+NLI3xw3K57UF5UxbsokZK7Zw7MB8jhucr6noIiIiIklqn4Wtu+tTnLRrM1dswYAxxboacnvSpUMG544t4rgh+bwyv5zXF5bz7tJNHD+kgAkDupGRtr/LE4iIiIhIa9L8OpFm1NU7s1Zu4bCeuXTSuZbtUvfcLC4eV8yarbt4ed56XvxoHdMWb+TEod05ql8X0iIqcEVERESSgQpbkWYsWl9BRVUtJbp3bbvXOy+brx/TjxWbdvDiR+t5dvYa3lq0gVMO78HIPnlE2sk0dREREZFkpeEGkWaUrthCx8w0DuvZKewokiT6duvANcf154pj+tEhI43HZpbx1zeWsHRjZdjRRERERNo1jdiKNKGiqoYF67Zz7KB8ohGNxsknzIzBPXIZ2L0jc8q28uJH67nzrWUM69WJ04f3JL9jZtgRRURERNodjdiKNOH9lVupdxiracjSjIgZo/p04btfGMIpw3qweEMlf3p1ES9+tI7q2vqw44mIiIi0KxqxFWnE3SldsYW+XXPonqu7Wsm+ZaRFOOmw7ozt24UX567jzY838MGqrZwxvCdHFnZuN7eJEhEREQmTRmxFGlm5eScbK3drtFYOSKesdM4v6cN1xw8gJyPKwzNWcdfby9hYsTvsaCIiIiJtngpbkUZKV2whIy3CkUWdw44iKahvtw7ccNIgvjSyN2u27uKW1xbx2oJyaus1PVlEREQkUTQVWSTO7po6PizbxoiizmSmRcOOIykqYsbRA7oxrHcnnp+zllfmr2dO2VbOGV1I324dwo4nIiIi0uZoxFYkzoert1FdV69718oh0SkrnYvGFXPZhL5U19Zzx5SlPD9njS4uJSIiInKIacRWJE7pii0U5GbSp2tO2FGkDRnasxP9v9CBF+au4+0lm1iwroLzxhZp9FZERETkENGIrUigfHsVKzfvpKRvF13JVg65zLQoE0cVctXn+lPvzh1TljL5w7XU1Gn0VkRERORgqbAVCcxcsYWIwag+eWFHkTZsYEFHvv35wRzVvytTF2/kr28sYf32qrBjiYiIiKS0hBa2Zna6mS00s8Vm9qMm2n9gZh8Ej7lmVmdmXYO25Wb2YdBWmsicInX1zqxVWxnasxO5Welhx5E2LjM9ypdHFfL1CX2p2F3Lba8vZtqSjbh72NFEREREUlLCClsziwK3AWcAw4CLzGxYfB93/727j3L3UcCPgTfdfXNcl5OC9pJE5RQBWLhuOzt21+qiUdKqDuvZiX87eTADCzry3Jy13PfOcip314YdS0RERCTlJHLEdhyw2N2Xuns18DAwcR/9LwIeSmAekWaVrthCblYag3vkhh1F2pmOmWlcNqEvXxrZm6UbdvDn1xaxdGNl2LFEREREUkoiC9tCYFXc87Jg3aeYWQ5wOvBE3GoHXjKzmWZ2bcJSSru3vaqGj9dXMKa4C9GILholrc+C+95+48SBZKZFuOutZby2oJx6TU0WERERaZFEFrZNVQjNfUo7G3i70TTkY919DLGpzDeY2fFN7sTsWjMrNbPSDRs2HFxiaZfeX7mVeoexxZqGLOHq1TmbG04cxIiizrwyfz33vr2cHZqaLCIiIrJfiSxsy4A+cc+LgDXN9L2QRtOQ3X1N8LUceIrY1OZPcfc73L3E3UsKCgoOOrS0L+7OzBWb6dcth/zczLDjiJCZHuWCkj6cM7qQ5Zt2cNsbi1m9dVfYsURERESSWiIL2xnAYDPrb2YZxIrXZxp3MrPOwAnAP+PWdTCz3IZl4FRgbgKzSju1YtNONlZWM7Zv17CjiOxhZhzVryvXHj8Ad7j9zSV8sGpL2LFEREREklbCClt3rwVuBF4E5gOPuvtHZna9mV0f1/Uc4CV33xG3rgcw1cxmA+8Bz7v7C4nKKu1X6YotZKRFGF7YKewoIp9S1CWHG04aRFGXHB4tLeP5OWuoq9d5tyIiIiKNpSXyxd19MjC50bpJjZ7fC9zbaN1SYGQis4nsrqnjw9VbGVmUR2ZaNOw4Ik3qmJnGVZ/rz+S5a3l7ySbWbqviwnHFdMxM6Nu3iIiISEpJ5FRkkaQ2Z/U2aupc966VpBeNGGeP6M15Y4tYuXknf3ld592KiIiIxFNhK+3WzBVbKMjNpE/XnLCjiLTImOIuXHf8QJzYebfvr9R5tyIiIiKgwlbaqfLtVazcvJOSvl0w071rJXUUdsnmhpMG0adrDo/NLOOFueuo13m3IiIi0s6psJV2aeaKLUQMRvXJCzuKyAHrmJnGlcf2Z1z/rkxZtIFvPjCLXdV1YccSERERCY0KW2l36uqdWau2MrRnJ3Kz0sOOI/KZRCPGxJG9+eKRvXhx3jq+esc7lG+vCjuWiIiISChU2Eq7s3DddnbsrtVFoyTlmRnHDsrnb5eWsLi8ki/f9jbz124PO5aIiIhIq1NhK+1O6Yot5GamMbhHbthRRA6JLwzrwaPXTaDe4by/TuP1BeVhRxIRERFpVSpspV3ZvquGhesqGF3chWhEF42StmN4YWeevuFY+uV34Kr7ZnDv28vCjiQiIiLSalTYSrvy/qqtOGgasrRJPTtn8eh1E/j80B784tl5/Pyfc6mtqw87loiIiEjCqbCVdqO+3ildvpm+3XLIz80MO45IQnTITOP2S8dy9ef6c987K7j27zPZsbs27FgiIiIiCaXCVtqNd5ZuYtOOasb16xp2FJGEikaM/zhrGL/+8nDeWFgeu2Jyha6YLCIiIm2XCltpNx6cvpLs9CjDCzuHHUWkVVx6dF/+dlkJS8p3cM5t01i0viLsSCIiIiIJocJW2oUNFbt58aN1jCnOIz2qX3tpP04+PHbF5Oq6er7y12lMW7Ix7EgiIiIih5w+4Uu78NjMVdTWO0dpGrK0Q0cWdeapbx5Dz05ZfP3u93jq/bKwI4mIiIgcUipspc2rr3cefm8V4/p3pXunrLDjiISiqEsOj3/jGMb27cJ3H5nNn19dhLuHHUtERETkkFBhK23e20s2snLzTi4ZXxx2FJFQdc5O574rx3HO6EL+8PLH/OiJD6nR7YBERESkDVBhK23eg9NX0iUnndOH9ww7ikjoMtOi/PGCkXzr84N4pHQVV91XSkVVTdixRERERA6KCltp08orqnh53nrOG1tEZlo07DgiScHMuOnUw/jduUfy9uKNnD/pHdZu2xV2LBEREZHPTIWttGmPlZZRW+9cNE7TkEUa++pRxdxz+VGUbdnFObdNY/7a7WFHEhEREflMVNhKm1VX7zw4fSUTBnRjQEHHsOOIJKXjhxTw6HUTADh/0jtM+XhDyIlEREREDpwKW2mzXltQzuqtu7hsQt+wo4gktWG9O/HUDcdQ1CWbK++dwaOlq8KOJCIiInJAVNhKm3XftOX06pzFKcN6hB1FJOn16pzNY9dPYMLAbvz743P440sLdTsgERERSRkqbKVNWlxewdTFG/na0X1Ji+rXXKQlcrPSufvyo7igpIhbXlvMTY/OprpWtwMSERGR5JcWdgCRRPj7OyvIiEb46lF9wo4iklLSoxF+d+4I+nTJ4Q8vf8zabVVMunQsnbPTw44mIiIi0qyEDmWZ2elmttDMFpvZj5poP9HMtpnZB8HjZy3dVqQ5FVU1PD6zjLNG9CK/Y2bYcURSjpnxrZMH88cLRlK6YjPnT5pG2ZadYccSERERaVbCRmzNLArcBpwClAEzzOwZd5/XqOtb7n7WZ9xW5FOenLWaHdV1fP2YfmFHEWkVD05fmbDXvmxCPx6YvoIzbn6Ly47pR2Fe9n63uXi8bq8lIiIirSuRI7bjgMXuvtTdq4GHgYmtsK20Y+7Ofe8sZ2SfPEb2yQs7jkjKG1jQkeuOH0g0YvxtylIWrtO9bkVERCT5JLKwLQTi7xlRFqxrbIKZzTazf5nZEQe4rchepi7eyNINO/i6bvEjcsj06JTF9ScOJD83g/vfWcH0ZZvCjiQiIiKyl0QWttbEusb3jpgF9HX3kcCfgacPYNtYR7NrzazUzEo3bNjwWbNKG3HftBV065DBF0f0CjuKSJvSKSuda44bwJAeufzzgzW8MHcd9bodkIiIiCSJRBa2ZUD8JWmLgDXxHdx9u7tXBsuTgXQzy2/JtnGvcYe7l7h7SUFBwaHMLylm6YZKXl2wnovHF5OZFg07jkibk5kW5WtH92Vc/65MWbSBR0tXUVOn2wGJiIhI+BJZ2M4ABptZfzPLAC4EnonvYGY9zcyC5XFBnk0t2VaksbumLiM9EuGyCf3CjiLSZkUjxsSRvTn9iJ7MKdvG3VOXUVFVE3YsERERaecSVti6ey1wI/AiMB941N0/MrPrzez6oNt5wFwzmw3cAlzoMU1um6iskvo2Ve7m8ZllfGVMIQW5usWPSCKZGccPKeDCo/qwZtsubnt9sW4HJCIiIqFK2O1+YM/04smN1k2KW74VuLWl24o05+/vrmB3bT1XH9c/7Cgi7caIojzyO2bywPQV3DFlKRNHFTK2b5ewY4mIiEg7lMipyCKtoqqmjvvfWcHJQ7szqHtu2HFE2pXeedl888RBFHfL4YlZZTw7e43OuxUREZFWp8JWUt4Ts8rYvKOaa44fEHYUkXapQ2YaVxzTn88NyuedpZv42p3T2Vi5O+xYIiIi0o6osJWUVl/v3PnWMkYUdWZ8/65hxxFpt6IR48wje3FBSREfrNrKl/48lQ/LtoUdS0RERNoJFbaS0l6Zv55lG3dwzXEDCC6wLSIhGtWnC0984xjMjHMnTeOJmWVhRxIREZF2QIWtpLS/vbWUwrxszhjeM+woIhIYXtiZZ248lrHFXbjpsdn8+Mk5VNXUhR1LRERE2jAVtpKy3l26iRnLt3D1cf1Ji+pXWSSZdOuYyd+vGscNJw3kofdW8eXb3mZxeWXYsURERKSNUjUgKevPry0iv2MmF40rDjuKiDQhLRrhB6cN5b4rx1FesZsv3TqVJ2dparKIiIgceipsJSWVLt/M24s3cf0JA8hKj4YdR0T24YQhBUz+9nEML+zM9x6dzb89/D7bdtWEHUtERETaEBW2kpL+9Ooi8jtmcMn4vmFHEZEW6Nk5iwevHs9NpwzhuTlrOePmKbyzZFPYsURERKSNUGErKWfWyi28tWgj1xw3gOwMjdaKpIq0aIRvnTyYJ75xDJnpUS6+813+e/J8XVhKREREDpoKW0k5f351EV1y0vna0RqtFUlFo/rk8fy3P8dF44q5fcpSvnjLW8xcsSXsWCIiIpLCVNhKSplTtpXXF27g6uMG0CEzLew4IvIZ5WSk8V/nHMl9V46jqqae8yZN45fPfsTO6tqwo4mIiEgKUmErKeWWVxfTOTudyyZotFakLThhSAEvfvd4Lj26L/e8vZzTbp7CawvWhx1LREREUowKW0kZ76/cwivz13PV5/qTm5UedhwROUQ6Zqbxq4nDefS6CWREI1x5bylX31fKqs07w44mIiIiKUKFraQEd+e/Jy8gv2MmV32uf9hxRCQBxvXvyr/+7Xh+fMZQpi3ZyBf++CY3v/Ixu6p1cSkRERHZNxW2khJenV/Oe8s3829fGKxza0XasIy0CNedMJDXbjqRU4/oyc2vLOLE/32dh99bSW1dfdjxREREJEmpsJWkV1tXz29fWMCA/A5ceFSfsOOISCvo2TmLP180mkevm0BhXjY/evJDTrt5Ci9+tA53DzueiIiIJBkVtpL0Hp9ZxuLySv799MNIj+pXVqQ9Gde/K0984xhuv3QsANf9fSZn3zqVf324lvp6FbgiIiISozmdktR2Vtfyx5c/ZkxxHqcd0TPsOCISAjPjtCN6cvLQ7jz5/mr++sYSvvHALAYWdOAbJw5i4qje+qOXiIhIO6dPApLU7p66jPKK3fzkzMMxs7DjiEiI0qIRLijpwyvfO4E/XzSa9GiE7z82m2N/+xr/9/LHrN9eFXZEERERCYlGbCVplVdUMenNpZw6rAcl/bqGHUdEkkQ0Ypw9sjdnjejFGws3cN87y/nTq4u49fXFnHZEDy4aV8wxA/OJRvTHMBERkfZCha0krf96fj7VtfX86IyhYUcRkSRkZpw0tDsnDe3Oik07+Me7K3i0tIzJH66jIDeTL43szZdHFTK8sFPSzfh4cPrKsCPs5eLxxWFHEBEROSgqbCUpTVu8kac/WMO3Pz+IAQUdw44jIkmub7cO/PSLw7jp1MN4fUE5T72/mvvfWc5dU5dR3DWHkw/vzslDezCuf1cy0nQWjoiISFuT0MLWzE4H/gREgTvd/beN2i8Bfhg8rQS+4e6zg7blQAVQB9S6e0kis0ryqK6t5z/+OZfirjl886RBYccRkRSSlR7ljCN7ccaRvdi2s4bJc9fy0kfreGD6Su55ezkdM9OYMLAb4/t35egB3Ti8VydNWRYREWkDElbYmlkUuA04BSgDZpjZM+4+L67bMuAEd99iZmcAdwDj49pPcveNicooyelvby1l6YYd3HPFUWSlR8OOIyIHKNmm2d5zxTh2Vtfy9uJNvLZgPW8v3sTL89YDkJuVxpjiLhxZ2JnhhZ0YXtiZwrzspJu6LCIiIvuWyBHbccBid18KYGYPAxOBPYWtu0+L6/8uUJTAPJICVm3eyS2vLuKM4T056bDuYccRkTYiJyONU4b14JRhPQBYu20X7y3bzLtLN/P+yi1MXbyRuuC+uJ2z0xnUvSMDCzowqHtH+ud3pDAvm8K8bDplp6noFRERSUKJLGwLgVVxz8vYezS2sauAf8U9d+AlM3Pgdne/49BHlGTi7vz8mY+IRoz/d9awsOOISBvWq3M2E0cVMnFUIQBVNXXMX7uduau3MX9dBUvKK3ltQTmPlpbttV2HjCi987L3PArzsujVOZvunTLpnptF99xM8nLSVfyKiIi0skQWtk39r+5NdjQ7iVhh+7m41ce6+xoz6w68bGYL3H1KE9teC1wLUFysqzqmsmdmr+G1BeX89MzD6Z2XHXYcEWlHstKjjC7uwujiLnut37qzmuWbdrJm667gURX7um0XH63ZxsbK6k+9VkY0QkFuZlDsflLwNhS/BbmZVFTV0CEzjYgKYBERkUMikYVtGdAn7nkRsKZxJzMbAdwJnOHumxrWu/ua4Gu5mT1FbGrzpwrbYCT3DoCSkpImC2dJfmu27uL/PT2XMcV5XHFsv7DjiIgAkJeTwaicDEb1yWuyvaqmjnXbqiiv2E15RRXl23fHlrfH1i3buIPpyzazdWfNp7aNGHTMTCM3K50uOel065hJfscMunaIfe2YqWnPIiIiLZXIwnYGMNjM+gOrgQuBi+M7mFkx8CRwqbt/HLe+AxBx94pg+VTgVwnMKiGqr3e+/9hsauudP14wirSobsUhIqkhKz1Kv/wO9MvvsM9+u2vr2FDxSdH7/IfrqKiqoaKqloqqGtZuq2Le2u3Ux/15NiMtQrcOGXTrGBv57dU5Nu05LyddI70iIiKNJKywdfdaM7sReJHY7X7udvePzOz6oH0S8DOgG/CX4K/SDbf16QE8FaxLAx509xcSlVXCdc+05Uxbson//sqR+/1wKCKSijLTohR1yaGoSw4Am3d8egS3rt7ZurOaTTuq2VS5m43B17Vbd/HR6m17zuXJTIvQq3M2fbpm06dLDn265tA5O70VvxsREZHkY+5tZ/ZuSUmJl5aWhh1DDsDCdRWcfetUjhuUz51fL0n4tLtkuw2JiEhLVNfWs357Feu2VbFm2y5Wb93F2m1Ve13JuX9+B/p160D//A7kd8w4oPfTi8frGhUiIpL8zGxmMBD6KYmciiyyT7tr6/jOIx+Qm5nGb88doXPJRESakZEWoU/X2Ohsg9q6etZuq2LVlp2s2LSTxeWVfLBqKwCdstIY0iOXIT1yGdS9o+4JLiIibZ4KWwnNL5+dx/y127nj0rEU5GaGHUdEJKWkRT8pdo8ZGLtl2qbKapZt3MGiDZXMXbON0hVbiBj069aBw3rGCt3uuZn6Q6KIiLQ5KmwlFA9MX8GD01dy/QkDOfWInmHHERFJeWZGfm4m+bmZHNW/K3X1zsrNO1m4roKP11fwr7nr+NfcdeTlpHNYj1yOLOxMv/wOuhCViIi0CSpspdXNWL6ZXzzzEScMKeAHpx0WdhwRkTYpGjH658fOuT19eE+27qzm4/WVLFxfwayVW5i+bDO5mWkcUdiZwT06Mra4C5GIilwREUlNKmylVa3dtotv/GMWhXnZ3HLhaKL6ECUi0irycjIY178r4/p3pbq2ngXrtvPh6m2ULt/M+ZPeoWenLM48shdfHNGLMcV5mq4sIiIpRYWttJqqmjqu+/tMdlXX8tA14+mco9tTiIiEISMtwoiiPEYU5bG7po6uHTN4bs5a/vHuCu5+exmFedl8eXRvzh1TxICCjmHHFRER2S8VttIqaurqufHBWcwp28btl45lcI/csCOJiAiQmR5l4qhCJo4qZHtVDa/MW88/P1jDX99Ywm2vL2FMcR7nji3irBG9db9cERFJWipsJeHq6p2bHp3NK/PL+fXEIzhNF4sSEUlKnbLS+cqYIr4ypoj126t4+v3VPDGrjJ8+NZdfPjuPU4b14LwxRRw3OJ+0aCTsuCIiInuosJWEcnd++tSHPDN7DT88fSiXTugXdiQREWmBHp2yuO6EgVx7/AA+XL2NJ2aW8czsNTw/Zy0FuZl8ZXQh55cUMai7ZuCIiEj4VNhKwrg7//n8fB6esYobTxrEN04cGHYkERE5QGa253zcn35xGK8tKOfxmWXcOXUZt09ZyujiPC4o6cNZI3qRm6WpyiIiEg4VtpIQ9fXObybP566py7j8mH7cdOqQsCOJiMhBykiLcPrwnpw+vCflFbGpyo+WlvHjJz/kl89+xJlH9uL8sX0Y37+rbh0kIiKtSoWtHHJVNXXc9Nhsnp+zlsuP6cfPzhqm20aIiLQx3XOzuPb4gVxz3AA+WLWVR0vLeG72Gp6ctZrirjmcP7aIc8cW0TsvO+yoIiLSDqiwlUNq284arvl7Ke8t28xPzhzKNccNUFErItKGmRmji7swurgLPztrGC98tJZHZ5Txh5c/5o+vfMznBuVzQUkfThnWg6z0aNhxRUSkjVJhK4fMqs07ufLeGSzftIM/XTiKiaMKw44kIiKtKDsjyjmjizhndBGrNu/ksZllPDGzjG899D6ds9OZOKo3F5T04YjenfRHTxEROaTM3cPOcMiUlJR4aWlp2DHapckfruWHT8wB4PZLx3LMwPyQEzXtwekrw44gItKu1LuzdMMOSldsZt6a7dTWOz07ZTGmOI8ji/KavTfuxeOLWzmpiIgkOzOb6e4lTbVpxFYOSlVNHb96bh4PTl/JyD55/PnC0RR3ywk7loiIJImIGYO6d2RQ947sqq5jdtlWZq7YwuS56/jX3HX0L+jAqKI8jujdmewMTVUWEZHPRoWtfGbz1mznO4+8z8frK7nuhAHcdMphZKRFwo4lIiJJKjsjytEDunH0gG5sqNjN7LKtzF61lSffX80/Z6/hsB65jOyTx9CeujeuiIgcGBW2csC27arh/17+mPvfWU7XDhncd+U4ThhSEHYsERFJIQW5mXzh8B6cPLQ7q7fuYvaqrcxZvY15a7eTmRbh/ZVbOPWInhw/pICOmfq4IiIi+6b/KaTF6uudx2eV8bt/LWDLzmouGd+Xm04dQl5ORtjRREQkRZkZRV1yKOqSwxlH9mLZxh18sGorb368gac/WENGNMKxg7pxyrCefGFYd7rnZoUdWUREkpAKW9mvunrnhbnruO31xcxbu50xxXncd+U4hhd2DjuaiIi0IREzBhZ0ZGBBRy4oKaJ0xRZe+mg9L89fx+tPfchPn4ZRffI4dVhPvnB4dwZ176irK4uICKCrIss+VNfW8/QHq5n0xhKWbtzBgIIOfOvzg5g4spBIJDU/SOiqyCIiqSH+qsjuzsL1FbEid956Ply9DYDuuZkcM7AbxwzMZ8LAbvTpqosXioi0ZboqshyQhesqeKx0FU9/sJqNldUc0bsTf71kDKce0ZNoiha0IiKSusyMoT07MbRnJ7598mBWb93FlI83MG3JJqYu3sjTH6wBoE/XbI4ZkM8xg7oxYUA3unfStGWRfUm2P/jrNl9yMFTYCgDLN+7glfnr+ecHa/hw9TbSo8bJQ3tw4bg+nDCkQFO9REQkaRTmZXPRuGIuGleMu7OovJJpizfy9pJNTJ67lkdKVwFQ1CWbUX3yGF3chVF9OnNYz066EJWISBuld/d2antVDe+v3MqUjzfw+oJylm7cAcCwXp34+dnDmDiqkK4ddFEoERFJbmbGkB65DOmRy+XH9qeu3vlozTamL93MB6u2MmvFFp6bs3ZP/6Iu2QztmcthPXM5rGcnhvbMpX9+B9Kjul2dpI66eqemrp7aeqe2rp66eseBendwqPfYshO7+CdBmwfrY6uc9durCBYxiz0iWLAcfCW2HGlYR+x8+Pg2M4hGjIgGQiRECS1szex04E9AFLjT3X/bqN2C9jOBncDl7j6rJdtKy+3YXcui8koWrtvOB6u2MWvFFj4ur8AdMtIiTBjQja8f04/PD+2u85NERCSlRSPGiKI8RhTl7Vm3fnsVs1dt5eP1FSxYV8HCdRW8vnADdcEH/vRo7KJVxV1zKOySTVGXHArzsinqEnt0zk7XzKUWSIZprR4UbU6siIs9Pin6grrvk+Wgf129U+dOfb1TV+/Uu1NbHzxvWO9QV19PXT171jf0rQu2i3+NWPsnfYu6ZAcFqVNbX09tXVxxGhSotXVOTdBWG9dWE7TV1sfWJeslciJBgRuNGGmRSPDV9voajURIixhpUSM9GiEjGiE9LUJG1CivqCI7PUp2RpSs9Cg5GdHY8/QoWXHLORmx5znpUdL0RykJJKywNbMocBtwClAGzDCzZ9x9Xly3M4DBwWM88FdgfAu3lUB1bT2bduymfPtu1mzdxaotOynbsotVm3eyqLySsi279vTNzUpjTHEXzjyyF2P7dmFM3zxyMjRwLyIibVePTlmcekRPTj2i5551u2vrWFK+Y0+xu2h9Bcs37WDq4o3srK7ba/uOmWkU5mVTkJtJlw4ZdOuQQZecDLp2SKdLhwy65mTQpUMGuVlp5GSkkZMRJTMtEnox7EFxVl1bH3vUNfpaW09NsLw7+NpQbFXXfbIce+x7ubaunsUbdsQKvaCwqw2KvobC0hsVnPXx6/csN7Vu7+X6+L6NXitsRjByGTGiZnuKvIjB9l01pEVjhV161ILiLkJWeiS2Lij20qINy58UgGkN2wRt0UgkKAw/KSAjseFTIhaMqBL7SvzzCHtGVSNmvL14457f008d77ifV/1ey5/8gaChX3xxX9voa11QjMe3V9fVs6P6k9+hht/FKYs2HvAxz4hGyM4IiuDga0562qfXZaTtKYpj69M+aU8P2jPi26NkRMP/dywtl8iKZhyw2N2XApjZw8BEIL44nQjc77FLM79rZnlm1gvo14JtU8qu6jp21dR98p9Iw38Itb7nP5hP/pOop7rO2V1TR+XuWiqqaqmoqqFydy3bq2LPK6tq2F5Vy8bK3WzdWfOp/XXKSqOoSw6ji7tw4VF9GBxM0+rbNSdlr2gsIiJyqGSmRRnWuxPDenfaa727s3VnDWVbdrF6a+wPxbHlXWyq3L3n6/aq2n2+fsQIRp7S9nxQzkyL7D2SFd17JCtiFowANowkxkYJ9yzv1RYbZYwvVGviCtbdweeJQz2yZwbpQXGVnhbZa3lndd1exVw0YqRHjIhFgmmrn56+Gr/cUHzt1bfRMuxduO09ZbZhumx8e9y02uDnsmd/cVNu92Q2mihKbU+xmmZB0RoUkp9sF1u/r6m4yXhhpIr9/B63tq8e1Yeqmthn5l3VdXuWdwafo6uCrw3tO4PHrura2PKe9bVs3VnNmq2fbLuzupaqmvoDyhONGDnp0bgi+ZN/z9npUTLTo6RHjczg38Ino88RMoKv6VEjIy0afP2kLT0a++NENO73a8+yGZEIe36vGvdpaItG7JPf32B9439nwF7/VtqyRBa2hcCquOdlxEZl99ensIXbppRfPTePh9777FN0stIj5Galk5uZRm5WGrlZ6fTolMXRA7pS0DGLgtxM8jtm7JlC1Tk7/RCmFxERaR/MjC4dYiOwRxY1f7/2mrp6tu6sYcvOajbviD0qq2rZWV0b9+F67w/du2vrY1Nc62JF6u7aukYjW77Xh9rYeYxBsWVx6yORPec4ZqbFf4AOluPWZTRalx6NxLaJ+3C9V/+GD9wR2+vDd0awruGDdFOSYSqypLZoxOiQmUaHBF3krb7ePymUq+vYWVP7yXJQEDcsNxTDe7fXsaumlsrdtWyo2M3uuNkQNXsNVCXD/IHm7fVHpb3WGVcd158fnj40zHifWSIL26be9Rr/lJvr05JtYy9gdi1wbfC00swWtjihyIHLBw58nkzrUb6Dk8z5kjkbKN/BSuZ8oWS7pOVdk/nYQfLnk1ZyAL/T7ZaOUfh+FDySWN/mGhJZ2JYBfeKeFwFrWtgnowXbAuDudwB3HGxYkZYws9LmbgqdDJTv4CRzvmTOBsp3sJI5XzJnA+UTEZGYRF5GbAYw2Mz6m1kGcCHwTKM+zwCXWczRwDZ3X9vCbUVEREREREQSN2Lr7rVmdiPwIrFb9tzt7h+Z2fVB+yRgMrFb/SwmdrufK/a1baKyioiIiIiISOpK6H1e3H0yseI1ft2kuGUHbmjptiJJINmnvSvfwUnmfMmcDZTvYCVzvmTOBsonIiKAebLe4VlERERERESkBRJ5jq2IiIiIiIhIwqmwFQmY2elmttDMFpvZp650bmZDzewdM9ttZt9v1LbczD40sw/MrDSkfBPNbE5DBjP7XEu3TXS2uH5HmVmdmZ0Xty70Yxf0OTHI8JGZvXkg2yY6n5n9IMj2gZnNDY5h16At9ONnZl3M7Kng9+89Mxve0m0PQba7zazczOY2025mdkuw/zlmNiaurTWO3f7yXRLkmmNm08xsZFxb2MduX+8pyXDsTjSzbXH/Nn4W19Ya/273mS8uY1PvKwk/fiIi7Y6766FHu38Qu0jZEmAAsdtNzQaGNerTHTgK+A3w/UZty4H8kPN15JPTC0YAC1q6baKzxfV7jdi58+cl2bHLA+YBxQ0/69Y4dp9lH8DZwGtJdvx+D/w8WB4KvNqKx+94YAwwt5n2M4F/Ebs/+tHA9NY6di3MdwzQJVg+oyFfkhy7Jt9TkujYnQg891l+Z1spX5PvK611/PTQQw892ttDI7YiMeOAxe6+1N2rgYeBifEd3L3c3WcANUmar9LdG06a7wB4S7dNdLbAt4AngPJDuO+WaEm+i4En3X0lxH7WB7Bta+SLdxHw0CHOsC8tyTcMeBXA3RcA/cysRwu3PSjuPgXYvI8uE4H7PeZdIM/Meh3KDPuyv3zuPs3dtwRP3yV233ZIgmO3j/eUVtGCn21zWuPfbUvyNfe+IiIiCaDCViSmEFgV97wsWNdSDrxkZjPN7NpDmiymRfnM7BwzWwA8D1x5INsmMpuZFQLnAJP4tGQ4dkOALmb2RpDjsgPYtjXyAWBmOcDpxP5A0CAZjt9s4CtBxnFAX2IFWmscv/3ZV4ZEH7sDdRWx0WVIjmPX3HsKJM+xm2Bms83sX2Z2RLAuKY4dzb+vQPIcPxGRNiOht/sRSSHWxLoDGZ041t3XmFl34GUzWxD8Nf9QaVE+d38KeMrMjgd+DXyhpdsmONvNwA/dvc7sU92T4dilAWOBk4Fs4B0ze7eF2x6sA9nH2cDb7h4/SpQMx++3wJ/M7APgQ+B9oLaF2ybavjIk+ti1mJmdRKywbTiPNRmOXXPvKZAcx24W0NfdK83sTOBpYDBJcuxo5n3F3T8mOY6fiEibohFbkZgyoE/c8yJgTUs3dvc1wddy4CliU+EOpQPKF3xAGmhm+Qe6bYKylQAPm9ly4DzgL2b25SBrMhy7MuAFd9/h7huBKcDIFm7bGvkaXEijacjJcPzcfbu7X+Huo4DLgAJgWUu2bQXNZmiFY9ciZjYCuBOY6O6bgtXJcOz2aPSekhTHLvi9qwyWJwPprfSe11LNva8kxfETEWlrVNiKxMwABptZfzPLIFZAPNOSDc2sg5nlNiwDpwLNXiUzUfnMbJAFw6HBlV8zgE0t2TbR2dy9v7v3c/d+wOPAN9396WQ5dsA/gePMLC2Y7jsemN/CbVsjH2bWGTghyNqwLimOn5nlBW0AVwNT3H17S7+3BHsGuMxijga2ufvaVjp2+2VmxcCTwKXBSF6D0I9dc+8pSXTsesblG0fsM01rvOe1VJPvK8ly/ERE2hpNRRYB3L3WzG4EXiR2Rc273f0jM7s+aJ9kZj2BUqATUG9m3yF20Zx8YlP1IPZv6kF3f6G18wHnEvsAXwPsAr4aXPilyW1bOVtzepAEx87d55vZC8AcoB64093nAiTy2LU0X9D1HOAld98Rt3lSHD/gcOB+M6sjdhXYq/a17aHMZ2YPEbs6br6ZlQE/B9Ljsk0mdmXkxcBO4Ipg04Qfuxbm+xnQjdgsBoBady9JkmPX5HuKxS4MlgzH7jzgG2ZWG+S7sDXe81qar7n3FTMbQCscPxGR9qbhMv4iIiIiIiIiKUlTkUVERERERCSlqbAVERERERGRlKbCVkRERERERFKaClsRERERERFJaSpsRUREREREJKWpsBURkZRlZnVm9oGZfWRms83se2YWCdpKzOyW/Wx/uZndeoD7/MlB5L3XzJYFmWeZ2YQD2HZPVjO73swu+6w5Wri/fma2K8ja8MjY/5Ytfv3Lzax33PM7zWzYoXp9ERFpX3QfWxERSWW73H0UgJl1Bx4EOgM/d/dSYveePtR+AvzXQWz/A3d/3MxOBW4HRhzoC+zn/tCfYmZp7l57oPsBljQc3wS4HJgLrAFw96sTtB8REWkHNGIrIiJtgruXA9cCN1rMiWb2HICZjTOzaWb2fvD1sLhN+5jZC2a20Mx+3rDSzL5mZu8FI5W3m1nUzH4LZAfrHthHv2gwOjvXzD40s+82EXkKMKi51wjWX2FmH5vZm8Cxcdl+YWbfD5aPMrM5ZvaOmf3ezOYG6y83s8fM7FngJTPrYGZ3m9mM4DhMDPpFg+1mBK9z3b6Os5lVxi2fZ2b3Bsv3mtktwfFdambnxfX79+A4zDaz3wZtJcADwfecbWZvmFlJ0P+ioP9cM/td/L7N7DfB67xrZj32lVVERNoPFbYiItJmuPtSYv+3dW/UtAA43t1HAz9j7xHXccAlwCjg/GAK8+HAV4FjgxHLOuASd/8RwSixu1/SXL/gtQrdfbi7Hwnc00Tcs4EPm3sNM+sF/JJYQXsK0Nw03XuA6919QrBtvAnA193988BPgdfc/SjgJOD3ZtYBuArYFqw/CrjGzPoH2w+Mm4Z8WzP7j9cL+BxwFvBbADM7A/gyMN7dRwL/4+6PExtNvyQ4lrsaXiCYnvw74PPEjuNRZvbloLkD8G7wOlOAa1qQSURE2gFNRRYRkbbGmljXGbjPzAYDDqTHtb3s7psAzOxJYoVZLTAWmGFmANlAeROve3Iz/Z4FBpjZn4HngZfitvm9mf0HsIFYUdnca4wH3nD3DUG2R4Ahe32jZnlArrtPC1Y9SKyojP/eNgfLpwJfahjpBbKA4mD9iLgR1s7AYOBjDnwq8tPuXg/MixtN/QJwj7vvBIjL05yj2Pv7fgA4HngaqAaeC/rNJFbwi4iIqLAVEZG2w8wGEBu1LAcOj2v6NfC6u59jZv2AN+LavNHLOLHi+D53//H+dtlcPzMbCZwG3ABcAFwZNP0gGLFs6HdSU68RjFI2ztbU/vdlR6O+57r7wkb7MeBb7v5io/X9mnnN+ExZjdp2N5HN2P/3sdeu99FW4+4Nr1WHPseIiEhAU5FFRKRNMLMCYBJwa1zx06AzsDpYvrxR2ylm1tXMsolNmX0beBU4z2IXpCJo7xv0rzGzhhHfJvuZWT4QcfcngP8HjNlH9Ob2NR040cy6Bfs7v/GG7r4FqDCzo4NVF+5jPy8C3woKWcxsdNz6bzR8T2Y2JJii3Jz1Zna4xa4+fc4++jV4CbjSzHIavr9gfQWQ20T/6cAJZpYfnGt8EfBmC/YjIiLtmP7SKSIiqSzbzD4gNrW4Fvg78Mcm+v0PsanI3wNea9Q2NdhuEPBgcDVlgunCLwUFXA2xkdcVwB3AHDObFZxn21S/XcA9wTqAZkd+3X1eU6/h7u+a2S+Ad4C1wCwg2sRLXAX8zcx2EBuJ3tbMrn4N3BxkN2A5sWnLdwL9gFnB+g3ECvzm/IjYdOBVxK5q3HEffXH3F8xsFFBqZtXAZGJXlr4XmGRmu4idC9zQf62Z/Rh4ndjo7WR3/+e+9iEiImKf/qO2iIiIpAoz6+julcHyj4Be7v5vIccSERFpVRqxFRERSW1fDEY404iNKF8ebhwREZHWpxFbERERERERSWm6eJSIiIiIiIikNBW2IiIiIiIiktJU2IqIiIiIiEhKU2ErIiIiIiIiKU2FrYiIiIiIiKQ0FbYiIiIiIiKS0v4/2nCFNO4GXYQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#diabeticpedigreefunction for diabetic\n",
    "fig = plt.figure(figsize = (16,5))\n",
    "sns.distplot(df[\"DiabetesPedigreeFunction\"][df[\"Outcome\"] == 1])\n",
    "plt.xticks([i*0.15 for i in range(1,12)])\n",
    "plt.title(\"diabetespedigreefunction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "creative-stopping",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Omem8Sdn65JwC7a1p1gNv7lVjW5fXsQAAQIQL5RXeBZJKnHN7nXOdkh6XtOSoY5ZIetT1ekdSmpnlBt83B4+JDj5cn3MeCb5+RNIVIfwMACJETXOHNpU1aOHoDCXEcjeHV+aOStdNi4t1pKVT976+R4fr27yOBAAAIlgoC2++pIN93pcFtw3oGDPzm9kGSVWSljnnVgaPyXHOlUtS8JlpVgGc0Ou7quX3mc4Yl+l1lGFvQk6ybj1zjCTpl2/s0ZZDDR4nAgAAkSqUhdf62Xb09JzHPMY51+OcmyWpQNICM5t2Ut/c7FYzW2Nma6qrq0/mVAARpr61U+sP1GlecYaS45iZeSjIS4vXV84Zq5EpcXps1QG9sqOSGZwBAMApF8rCWyapsM/7AkmHT/YY51y9pNckXRzcVGlmuZIUfK7q75s75+53zs1zzs3LymI2VmA4e2N3tUyms8ZzdXcoSY6L1hfOHKNZhWl6aXuVHlt1gMmsAADAKRXKwrta0ngzG21mMZKuk/TsUcc8K+mm4GzNiyQ1OOfKzSzLzNIkycziJV0gaUefc24Ovr5Z0jMh/AwAwlxTe5fWlNZpdlGa0hJivI6Do0T7fbpmboEumTZS28sbdferJSpv4L5eAABwaoSs8DrnuiXdIekFSdslPemc22pmt5nZbcHDlkraK6lE0gOSvhLcnivpVTPbpN7ivMw591xw3/clXWhmuyVdGHwPAP16Z+8R9QSczprASI+hysx05vgsff6MMersCeje1/Zo7f46r2MBAIAIENKpSp1zS9Vbavtuu6/Payfp9n7O2yRp9jG+5hFJ55/apAAiUVdPQCv31WrSyGRlJsV6HQcnMDozUXecO05PrD6oP6wr0+6qJi2Zmc+ayQAA4EML5ZBmAPDUhoP1au3s0enMzBw2kuOidcvpo3XB5GxtOdSgn728SyVVzSc+EQAAoB8UXgARyTmnt0pqlJsap9GZiV7HwUnw+0znTcrRbWePVWyUXw++tU/PbjysDia0AgAAJ4nCCyAilVQ3q6qpQ6ePzZRZfyugYagrSE/QHeeN0+KxI/TO3iP6yUu7tPlQA8sXAQCAAaPwAohIb5XUKCk2SjMKUr2Ogo8g2u/TZTPydNvZY5UYG6XfrzqgR94u1ZHmDq+jAQCAMEDhBRBxqhrbtauyWQvHZCjKz4+5SFCUkaCvnDNOH5+eq/1HWvWTl3bpmQ2H1Nje5XU0AAAwhIV0lmYA8MKKvUcU5TMtHD3C6yg4hfw+0+njMjW9IFWv7qjS6tJarTtQp/rWLn3prDFKT2SdZQAA8H4UXgARpa2zR+sP1GlmYZqSYvkRF4lS4qK1ZFa+zhiXqZd3VOmXb+zRwyv26ao5Bfrc6cUal53sdUQAADBE8NsggIiy/mCdunqcFo/h6m6kG5EUq0/NK9QPr56hB5fv09Nry/TYygM6e0KWPjWvUOdPzlZcNGv4AgAwnFF4AUQM55xW7qtVYXq88tLivY6DQTIhJ1nf/+QMfeNjE/XYygP67cr9uv2xdUqKjdLHpo7U5bPytHB0BuUXAIBhiMILIGKUHmlVdVOHPjmnwOso8MCIpFh99fzx+sq54/TO3iP68/pD+tuWCv1hXZlio3xaMDpDZ43P0qIxIzRhZJJioyjAAABEOgovgIixct8RxUX7ND2fpYiGs3cntzp9XKa+c8U0rdhTozd39z7+e+l2SVK03zQ+O1lT81I0LjtJBekJKkiPV0F6vNISYuT3sXYzAACRgMILICI0d3Rr66FGLRiToZgoliJCr7hov86blKPzJuVIksob2rRuf722HG7QlkMNemVHlZ5aW/a+c8yk5NgopSXEKDkuSjFRPkX7fYrx++QLFmHn3HvHv/vSyb3//bG2B89LjPErKzm295EUq+LMRI3PSZZzTmYUbgAATgUKL4CIsG5/nXqc04LiDK+jYAjLTY3Xx2fE6+Mzct/b1tDWpUN1bSqra1VZXZvqWzvV0NalhrYuNbZ3q6snoM7ugFo7u9UTcJKZ3q2j7/ZSk94rqR/cZ+9ttD77q5s7tK28UTXNnb1fNygmyqfs5FgVj0jUmMxEjRqRqPgYhl8DAPBhUHgBhL2Ac1pVWqviEYnKSYnzOg7CTGp8tFLjozUlL8WT7x8IOB1p6dS+mhbtrmrSXzeV63B9u97Ze0TLS2pkkgrS4zW9IE0z8lOVEh/tSU4AAMIRhRdA2NtT1azalk5dODnH6yjASfP57L2hzQtGZ+jd68ddPQEdrGvV3uoW7aho1NLN5Xp+c7lGZyZq7qh0zShI415jAABOgMILIOytKq1VQoxfUz26QgeEQrTfpzGZSRqTmaQLJueouqlDG8vqtfFgvZ5aW6YXt1XqtLEjNL+YJZcAADgWCi+AsNbc0a3t5Y06bWymovxMVoXIlZUcqwsm5+j8SdnaVdmkN3bX6PktFXp1Z5XOHJ+lM8ZlKpp/AwAAvA+FF0BY23iwXgEnzR2V7nUUYFCYmSaOTNHEkSkqq2vVqzurtWxbpVaX1uqSabmalpfCLM8AAATxp2AAYcs5p7X761SQHs9kVRiWCtIT9JlFo/T5M0YrLsqv3686oF8t36ea5g6vowEAMCRQeAGErcMN7apobNecIq7uYngbm5WkO84bpyWz8lTR0K6fv7JbK/cded96wQAADEcUXgBha93+OkX5TDML0ryOAnjOZ6aFo0foH84fr+IRiXpmw2E9+vZ+NbV3eR0NAADPUHgBhKXunoA2HKzX5NwUxccwQy3wrtT4aN18WrE+MSNXe6qbddfLu7WvpsXrWAAAeILCCyAsba9oUltXD5NVAf3wmem0sZm6/dxxio+J0q+X79WqfbVexwIAYNBReAGEpXX765QSF6Vx2UleRwGGrJyUOH357LEal52kP284pGc2HFJPgPt6AQDDB4UXQNhpbO/SrsomzS5Kl4/lV4Djio/x66bFxTpzXKZW7qvVIytK1dHd43UsAAAGBevwAgg7Gw7Uy0may+zMkPTYygNeRxjyfGa6ZHquslNi9cd1h/Tg8n26+bRiJcTwawAAILJxhRdA2NlwsF6F6fHKTI71OgoQVuaOytANC4t0uKFdv3pzHzM4AwAiHoUXQFipCK69O6swzesoQFiampeqmxcX60hLh+5/Y6/qWju9jgQAQMhQeAGElQ0H6+UzaTpr7wIf2rjsJH3+9NFq6ezWr5fvUyNXegEAEYrCCyBsBJzTxrJ6jctOUlIs9x4CH0XRiETdctpoNXd068Hl+9TS0e11JAAATjkKL4Cwsf9IqxrauhjODJwihRkJumnRKNW2dOqhFfvU3sXszQCAyELhBRA2NhysV7TfNDk3xesoQMQYk5WkGxeOUmVDhx5eUarO7oDXkQAAOGUovADCQncgoC2HGjQlN0WxUX6v4wARZeLIZF07v1AHa1v1xOoDCjjndSQAAE4JCi+AsLCrolltXT0MZwZCZFp+qj4xM0/bK5r0183lXscBAOCUYNYXAGFhQ1m9EmL8Gped7HUUIGItHjNCdS2dWl5So4yEGJ0+LtPrSAAAfCQUXgBDXntXj3aUN2pecbr8PvM6DhDRLp42UrUtnVq6uVzpCdGakpfqdSQAAD60kA5pNrOLzWynmZWY2Z397Dczuyu4f5OZzQluLzSzV81su5ltNbN/7HPOt8zskJltCD4uDeVnAOC9beWN6g44zWLtXSDkfGb61LxC5afH64k1B3W4vs3rSAAAfGghK7xm5pd0t6RLJE2RdL2ZTTnqsEskjQ8+bpV0b3B7t6R/ds5NlrRI0u1HnfsT59ys4GNpqD4DgKFhc1mD0uKjVZiR4HUUYFiIifLpM4tGKT7ar9+u3M8avQCAsBXKK7wLJJU45/Y65zolPS5pyVHHLJH0qOv1jqQ0M8t1zpU759ZJknOuSdJ2SfkhzApgiGrr7NHuqiZNz0+VGcOZgcGSHBetTy8apeb2bv1+9QH1BJi5GQAQfkJZePMlHezzvkwfLK0nPMbMiiXNlrSyz+Y7gkOgHzSz9FOWGMCQs628QQEnTS/gPkJgsBWkJ2jJrHztrW7R37YwczMAIPyEsvD2dynm6D8PH/cYM0uS9AdJX3PONQY33ytprKRZksol/bjfb252q5mtMbM11dXVJxkdwFCxqaxBGYkxyk+L9zoKMCzNHZWuxWNH6K09R7T+QJ3XcQAAOCmhLLxlkgr7vC+QdHigx5hZtHrL7u+cc3989wDnXKVzrsc5F5D0gHqHTn+Ac+5+59w859y8rKysj/xhAAy+1o5u7aluZjgz4LFLp+VqdGai/rzhkCoa2r2OAwDAgIWy8K6WNN7MRptZjKTrJD171DHPSropOFvzIkkNzrly6/3N9teStjvn/rfvCWaW2+ftlZK2hO4jAPDS1sONvcOZ8xnODHjJ7zNdN79QcVF+PbbqgDq6eryOBADAgISs8DrnuiXdIekF9U469aRzbquZ3WZmtwUPWyppr6QS9V6t/Upw++mSPiPpvH6WH/qhmW02s02SzpX09VB9BgDe2nyoQSMSY5SbGud1FGDYS46L1rXzC3WkuUN/3nBIzjGJFQBg6IsK5RcPLhm09Kht9/V57STd3s95y9X//b1yzn3mFMcEMAQ1B4cznz0xi+HMwBAxJitJF0zJ0bJtlSrOTNTC0SO8jgQAwHGFckgzAHxoWw83yInhzMBQc/aELI3PTtJzm8p1qL7N6zgAABwXhRfAkLS5rEGZSbEamcJwZmAo8ZnpU/MKlRjj1xOrD6izO+B1JAAAjonCC2DIaWrv0r6aFmZnBoaoxNgofWpeoY40d+q5TUcvwAAAwNBB4QUw5Gwrb5STNC0/xesoAI5hTFaSzpqQpTX767TlUIPXcQAA6BeFF8CQs/Vwo0YkxjCcGRjizp+crfy0eP1p/SE1tHV5HQcAgA+g8AIYUlo7u7W3ullT8xjODAx1UT6frp1XqO5AQE+tOagASxUBAIYYCi+AIWV7eZMCjuHMQLjITI7VZTPytLemRW/urvE6DgAA70PhBTCkbD3coLT4aOWnxXsdBcAAzR2Vrql5KVq2rUJlda1exwEA4D0UXgBDRkdXj0qqmjU1L4XhzEAYMTNdOTtfyXHRemL1QXV093gdCQAASRReAEPIjsomdQecpualeh0FwElKiInSNXMLVNvSqb9uKvc6DgAAkii8AIaQrYcalBQbpaIRCV5HAfAhsFQRAGCoofACGBI6uwPaWdmkKXkp8jGcGQhbLFUEABhKKLwAhoTdVU3q6nGaxnBmIKxF+Xy6dn7vUkV/WFemQICligAA3qHwAhgSth5uVHy0X6MzE72OAuAjykyK1aXTc1VS1axH3y71Og4AYBij8ALwXHcgoB0VjZqcmyK/j+HMQCRYUJyhiTnJ+t7zO1RS1eR1HADAMEXhBeC5fTUtau8KaGpeitdRAJwiZqar5uQrIcavrz2xQZ3dAa8jAQCGIQovAM9tO9yoaL9pXHaS11EAnELJcdH63lUztOVQo+56ebfXcQAAwxCFF4CnAs5pe3mjJuQkK9rPjyQg0lw8baSumVuge14r0dr9tV7HAQAMM/x2CcBTh+ra1NjerSm5DGcGItU3L5uivLR4ff2JjWru6PY6DgBgGKHwAvDUtvJG+UyaODLZ6ygAQiQ5Llr/+6lZOljXqu8+t83rOACAYYTCC8BT28obVZyZqISYKK+jAAihBaMzdNvZY/X46oNatq3S6zgAgGGCwgvAM9VNHapu6tBUhjMDw8LXL5igKbkpuvMPm1TT3OF1HADAMEDhBeCZbeWNkqTJFF5gWIiJ8umn181SU0e37vzDJjnnvI4EAIhwFF4Antl2uEH5afFKS4jxOgqAQTIhJ1n/evEkvbS9Sk+sPuh1HABAhKPwAvBEY3uXDta1cXUXGIZuOa1Yp48bof96bpv2H2nxOg4AIIJReAF4YntwOPPUPAovMNz4fKb/d81MRflMX39ig7p7Al5HAgBEKAovAE9sL29URmKMspNjvY4CwAO5qfH6zhXTtO5Ave57fY/XcQAAEYrCC2DQdXT3aE91iyaPTJaZeR0HgEeWzMrXZTPz9NOXdmtTWb3XcQAAEYjCC2DQlVQ1qyfgNIn7d4Fh77tLpik7OVb/8Pv1au7o9joOACDCUHgBDLrt5U2Ki/apeESi11EAeCw1IVo/vW62DtS26pt/3uJ1HABAhKHwAhhUAee0s6JRE3OS5fcxnBmAtGB0hv7h/PH64/pD+uO6Mq/jAAAiCIUXwKA6WNuqls4ehjMDeJ+vnjdeC0Zn6P/78xbtq2GpIgDAqUHhBTCodlQ0yWfShOxkr6MAGEL8PtPPrpul6Cif/uH369XZzVJFAICPjsILYFBtL29UcWai4mP8XkcBMMTkpsbrh5+coc2HGvTDv+3wOg4AIAJQeAEMmiPNHapq6tDkkQxnBtC/i6aO1E2LR+lXy/fp1Z1VXscBAIQ5Ci+AQbOjokmSNJn7dwEcx79dOlmTRibrX57cqKrGdq/jAADCWEgLr5ldbGY7zazEzO7sZ7+Z2V3B/ZvMbE5we6GZvWpm281sq5n9Y59zMsxsmZntDj6nh/IzADh1tlc0Kjs5VhmJMV5HATCExUX79YsbZquls1v/9ORGBQLO60gAgDAVssJrZn5Jd0u6RNIUSdeb2ZSjDrtE0vjg41ZJ9wa3d0v6Z+fcZEmLJN3e59w7Jb3snBsv6eXgewBDXFtnj0prWri6C2BAxmUn61uXTdXykhrd98Yer+MAAMJUKK/wLpBU4pzb65zrlPS4pCVHHbNE0qOu1zuS0sws1zlX7pxbJ0nOuSZJ2yXl9znnkeDrRyRdEcLPAOAU2VXVpICTJo9kdmYAA3Pt/EJ9fEaufvziLq3ce8TrOACAMDSgwmtmfzCzj5vZyRTkfEkH+7wv099L64CPMbNiSbMlrQxuynHOlUtS8Dn7JDIB8Mj28kYlxvhVkJHgdRQAYcLM9P2rpmtURoLu+P167ucFAJy0gRbYeyXdIGm3mX3fzCYN4BzrZ9vRN+Ec9xgzS5L0B0lfc841DjDru+feamZrzGxNdXX1yZwK4BTrCTjtqmzSpJEp8ll//+wBoH/JcdG699Nz1dzerTseW6+uHtbnBQAM3IAKr3PuJefcjZLmSCqVtMzMVpjZLWYWfYzTyiQV9nlfIOnwQI8Jft0/SPqdc+6PfY6pNLPc4DG5kvpds8A5d79zbp5zbl5WVtZAPiaAECk90qL2roAm5TKcGcDJmzgyWd+7arpWldayPi8A4KQMeIiymY2Q9FlJX5C0XtLP1FuAlx3jlNWSxpvZaDOLkXSdpGePOuZZSTcFZ2teJKnBOVduZibp15K2O+f+t59zbg6+vlnSMwP9DAC8saO8UVE+07jsJK+jAAhTV8zO102LR+mBN/dp6eZyr+MAAMJE1EAOMrM/Spok6TeSLnv3HlpJT5jZmv7Occ51m9kdkl6Q5Jf0oHNuq5ndFtx/n6Slki6VVCKpVdItwdNPl/QZSZvNbENw278555ZK+r6kJ83s85IOSLrmJD4vgEHmnNP2iiaNyUpUbJTf6zgAwti/f3yyNh9q0L88tVFjshI1aSSzvgMAjm9AhVfSr4Jl8z1mFuuc63DOzTvWScFzlh617b4+r52k2/s5b7n6v79Xzrkjks4fYG4AHqtu6lBtS6fOHJ/pdRQAYS42yq/7Pj1Xl/18ub7wyBo9e8cZrOsNADiugQ5p/m4/294+lUEARKbtFU2SxJUYAKdETkqc7r9pnqqaOnT779YxiRUA4LiOW3jNbKSZzZUUb2azzWxO8HGOJNYWAXBCO8oblZcWp9T4Y81vBwAnZ1Zhmr535XS9vfeI/vuv272OAwAYwk40pPlj6p2oqkBS38mjmiT9W4gyAYgQzR3dOlDbqnMnsVw2gFPrk3MLtL28Ub9avk/jc5J048JRXkcCAAxBxy28zrlHJD1iZp90zv1hkDIBiBC7KprkJE3OZTgzgFPvzksmqaS6Wd98Zqvy0+J1zkT+uAYAeL8TDWn+dPBlsZn909GPQcgHIIxtr2hUSlyU8lLjvI4CIAJF+X36xQ1zNDEnWXc8tl7byxu9jgQAGGJONGlVYvA5SVJyPw8A6FdXT0C7K5s1KTdFvUtrA8CplxQbpQc/O19JsVH63MOrVdnY7nUkAMAQcqIhzb8MPn97cOIAiBT7alrU2RPQ5JH8bQxAaI1MjdODn52va+5boc89vFpPfGmxkmIHuvIiACCSDWhZIjP7oZmlmFm0mb1sZjV9hjsDwAdsL29UtN80JivJ6ygAhoEpeSn6xY1ztKOiSbc+ukYd3T1eRwIADAEDXYf3Iudco6RPSCqTNEHSN0KWCkBYc85pR0WTxmcnK9o/0B8zAPDRnDsxWz+6eoZW7Dmirz2+QT0B53UkAIDHBvqb6LsLaF4q6ffOudoQ5QEQAcob2tXQ1qXJuQxnBjC4rppToP/4+GQ9v6VC//HnLXKO0gsAw9lAb3D5i5ntkNQm6StmliWJWSEA9Gt7RaNM0sSRLEcEYPB94cwxqm3p1D2v7VFGYrS+8bFJXkcCAHhkQIXXOXenmf1AUqNzrsfMWiQtCW00AOFqR3mTCjMSmDQGgGe+8bGJqmvt1N2v7lFclF9fPX+815EAAB44md9GJ6t3Pd6+5zx6ivMACHOVje06VN+mi6bkeB0FwDBmZvruFdPV0RXQj5ftUnSUT7edPdbrWACAQTagwmtmv5E0VtIGSe9Oe+hE4QVwlJe3V0mSJucynBmAt/w+04+umanugNP3n9+hKJ/pC2eO8ToWAGAQDfQK7zxJUxwzPwA4gZe2Vyo9IVrZybFeRwEA+X2m//3UTHUHAvruX7fL7zPdcvpor2MBAAbJQAvvFkkjJZWHMAuAMNfa2a23Smo0d1S6zMzrOAAgSYry+/Sz62arJ7BO3/7LNnV0BxjeDADDxEALb6akbWa2SlLHuxudc5eHJBWAsLR8d406ugOaxOzMAIaYaL9Pv7hhjv7pyY36/vM71NbZo69dMJ4/zgFAhBto4f1WKEMAiAwvb69SclyURmcmeh0FAD4g2u/TT6+dpbgon3728m61dfXo/14yidILABFsoMsSvW5moySNd869ZGYJkvyhjQYgnAQCTi/vqNLZE7Lk9/HLI4Chye8z/eCTMxQf49f9b+xVU3u3vnvFNH5uAUCEGugszV+UdKukDPXO1pwv6T5J54cuGoBwsrGsXjXNHbpwSo5aOnpOfAIAeMTnM3378qlKjovS3a/uUW1Lh3523WzFRfO3fACINL4BHne7pNMlNUqSc263pOxQhQIQfl7aXim/z3TOBH40ABj6zEzf+Ngk/edlU/TC1krd9OAqNbR1eR0LAHCKDbTwdjjnOt99Y2ZR6l2HFwAk9d6/O784XakJ0V5HAYABu+X00brr+tlaf6BO1/7ybZU3tHkdCQBwCg208L5uZv8mKd7MLpT0lKS/hC4WgHBysLZVOyqadMHkHK+jAMBJu3xmnh787HyV1bXpirvf0pZDDV5HAgCcIgMtvHdKqpa0WdKXJC2V9B+hCgUgvLy8vVKSdD6FF0CYOnN8lp7+8mJF+Xy65r639cLWCq8jAQBOgQEVXudcQNKfJX3FOXe1c+4B5xxDmgFIkl7aXqWxWYksRwQgrE0amaI/3X6aJo5M1m2/Xatfvr5H/LoDAOHtuIXXen3LzGok7ZC008yqzeybgxMPwFDX2N6llfuO6IIpXN0FEP6yk+P0+K2LdOn0XH3v+R268w+b1dkd8DoWAOBDOtEV3q+pd3bm+c65Ec65DEkLJZ1uZl8PdTgAQ98bu6rV1eO4fxdAxIiL9uvn183WV88bpyfWHNTND65SQyszOANAODpR4b1J0vXOuX3vbnDO7ZX06eA+AMPcy9urlJ4QrTlF6V5HAYBTxucz/fNFE/W/n5qptfvrdOU9b6m0psXrWACAk3SiwhvtnKs5eqNzrloSa48Aw1x3T0Cv7KjSuZOy5feZ13EA4JS7ak6BfvfFhapr7dSSu9/S8t0f+LUIADCEnajwdn7IfQCGgbX769TQ1qULGc4MIILNL87QM7efoZEpcbr5oVV66K19TGYFAGEi6gT7Z5pZYz/bTVJcCPIACCMvba9UjN+nMydkeR0FAEKqaESC/vCV0/Sp+97Wt/+yTc9tKteSmXmK8g90hceh6YaFRV5HAICQOm7hdc75BysIgPDinNOybZVaNHaEkmJP9LczAAh/SbFRumFhkV7ZUaVXdlSpuqlDNy4sUnIcd3kBwFAV3n+WBOCZkqpmlR5p1UUsRwRgGPGZ6YLJObp+QZHKG9p0z2t7dKiuzetYAIBjoPAC+FBe3FYpSbqQwgtgGJqen6ovnTVWZtIv39ijjQfrvY4EAOgHhRfAh/LitkrNLEhVTgq38wMYnvLS4vWVc8apID1BT6w5qL9tqVCAyawAYEih8AI4aZWN7dp4sF4XTR3pdRQA8FRSbJQ+d0axFozO0Bu7q/Wbt/ervavH61gAgKCQFl4zu9jMdppZiZnd2c9+M7O7gvs3mdmcPvseNLMqM9ty1DnfMrNDZrYh+Lg0lJ8BwActYzgzALwnyufTFbPytWRWnnZXNene1/aoprnD61gAAJ14WaIPzcz8ku6WdKGkMkmrzexZ59y2PoddIml88LFQ0r3BZ0l6WNIvJD3az5f/iXPu/4UoOoATWLatUqNGJGh8dpLXUQAMcY+tPOB1hEGzcPQIZSXH6rGVB3TPayW6fn6Rxuckex0LAIa1UF7hXSCpxDm31znXKelxSUuOOmaJpEddr3ckpZlZriQ5596QVBvCfAA+hOaObr2954gumpIjM/M6DgAMKWMyk3T7OeOUFh+jh1eUavnuajnu6wUAz4Sy8OZLOtjnfVlw28ke0587gkOgHzSz9P4OMLNbzWyNma2prq4+mdwAjuP1ndXq7AnowincvwsA/UlPjNGXzh6jKXkpWrqlQn9YV6aunoDXsQBgWApl4e3v0s/Rf+IcyDFHu1fSWEmzJJVL+nF/Bznn7nfOzXPOzcvKyjrBlwQwUC9uq1BGYozmjur3b00AAEmxUX5dv6BI50/O1roD9frVm3vV2NbldSwAGHZCWXjLJBX2eV8g6fCHOOZ9nHOVzrke51xA0gPqHToNYBB09QT06o4qnTcpW34fw5kB4Hh8Zjp/Uo5uXFikysYO3fNaicrqWr2OBQDDSigL72pJ481stJnFSLpO0rNHHfOspJuCszUvktTgnCs/3hd99x7foCslbTnWsQBOrVX7atXY3q2LmJ0ZAAZsal6qbjt7rPw+0/1v7NX6A3VeRwKAYSNkhdc51y3pDkkvSNou6Unn3FYzu83MbgsetlTSXkkl6r1a+5V3zzez30t6W9JEMyszs88Hd/3QzDab2SZJ50r6eqg+A4D3e3FrheKifTpzPLcJAMDJGJkap6+cM06FGQl6am2ZXthaoQCTWQFAyIVsWSJJcs4tVW+p7bvtvj6vnaTbj3Hu9cfY/plTmRHAwDjntGxbpc4Yl6X4GL/XcQAg7CTGRulzp4/WsxsP6/Vd1Wpo69JVc/IV5QvlgDsAGN74CQtgQLYebtThhnZdNJXhzADwYfl9pitm5emiKTnacLBeD68oVXtXj9exACBiUXgBDMiL2yrlM+n8SdleRwGAsGZmOmditq6ZW6DSmhbd/8ZeNTCDMwCEBIUXwIAs21apuaPSNSIp1usoABARZhel67OnjVZda6fufa1EFQ3tXkcCgIhD4QVwQgdrW7W9vFEXTRnpdRQAiCjjspN061ljJEm/fGOP9lQ3e5wIACILhRfACS3bVilJupDliADglMtNjddtZ49Vany0Hn6rVBsOsmwRAJwqFF4AJ7RsW6XGZyepODPR6ygAEJHSEmL0pbPGqmhEgp5cU6YVe2q8jgQAEYHCC+C46ls7taq0ltmZASDE4mP8uuW0Yk3JTdFzm8r16s4qOdbqBYCPhMIL4Lhe2VGlnoDThdy/CwAhF+X36foFRZpdmKZl2yr1t60VlF4A+AiivA4AYGhbtq1S2cmxmpGf6nUUABgW/D7TJ+cWKCbKpzd316ijK6DLZ+XJZ+Z1NAAIOxReAMfU3tWj13dV68rZ+fL5+EULAAaLz0yXz8xTXLRfr++qVnt3j66ZWyg/P4sB4KRQeAEc0/LdNWrt7GF2ZgDwgJnpY1NHKi7arxe2VqizO6DrFxQp2s8daQAwUPzEBHBMz2+pUHJclE4bm+l1FAAYts6ekKXLZ+ZpZ0WTHllRqo6uHq8jAUDYoPAC6FdXT0Avba/UhZNzFBPFjwoA8NKiMSN09dwClR5p0UMrStVO6QWAAeG3WAD9envPETW0deniaczODABDweyidF03v0hlda166K19lF4AGAAKL4B+Pb+lQgkxfp01IcvrKACAoGn5qbp+QZEO1bdRegFgACi8AD6gJ+C0bFuFzp2Urbhov9dxAAB9TM1L1Q0LRulwfbsefGuf2jopvQBwLBReAB+wurRWNc2duoThzAAwJE3JS9ENC4tUTukFgOOi8AL4gL9tqVBslE/nTsz2OgoA4Bgm56boxoVFqmhs16/f2qvWzm6vIwHAkEPhBfA+gYDT37ZU6KwJWUqMZaluABjKJgVLb2Vjhx5cvo/SCwBHofACeJ+NZfWqaGxnODMAhIlJI1P06YWjVNXUoV9TegHgfSi8AN7nb1sqFO03nT85x+soAIABmjgyWZ9eNErVTVzpBYC+KLwA3uOc0/NbKnTa2Eylxkd7HQcAcBIm5CTrxoWjVNnUoYfeKmUiKwAQhRdAH5sPNehAbas+Pj3X6ygAgA9h4shkfTo4kRWzNwMAhRdAH3/dVK5ov+ljU7l/FwDC1cSRKbpxQZEqGtr10Ip9au+i9AIYvii8ACT1Dmd+blO5zhiXqdQEhjMDQDiblPv3dXofeovSC2D4ovACkCRtOFivQ/Vt+sSMPK+jAABOgcm5Kbp+QaEO1bfp4RWl6qD0AhiGKLwAJEnPbSpXjN+nC6cyOzMARIopeam6bn6RyupaKb0AhiUKLwAFAk5LN5frrAlZSoljODMARJJp+am6dn6RDta16uG3S9XRTekFMHxQeAFo3YE6lTe06xMzmJ0ZACLR9PxUfWpeoQ7WtuqRFfvV2R3wOhIADAoKL4De4cxRPp0/OdvrKACAEJlRkKZr5hVq/5EWPfJ2KaUXwLBA4QWGuZ7gcOZzJ2YpmeHMABDRZgZLb2lNix59u5R1egFEPAovMMytKa1VVVOHPs7szAAwLMwqTNPVcwu0r6ZFX3h0NUsWAYhoFF5gmHtuU7nion06fxLDmQFguJhdlK5Pzi3Qij1H9MVH11B6AUQsCi8wjHX1BPTXzeU6f3KOEmOjvI4DABhEc4rS9aOrZ2p5SY1u/c1aSi+AiEThBYax5btrVNvSqStm5XsdBQDggavnFugHV83QG7uq9SVKL4AIROEFhrE/rT+ktIRonT0hy+soAACPfGp+ob5/1XS9vqtaX/7tWtbpBRBRQlp4zexiM9tpZiVmdmc/+83M7gru32Rmc/rse9DMqsxsy1HnZJjZMjPbHXxOD+VnACJVS0e3lm2r1KXTcxUTxd++AGA4u25Bkf7nyul6dWe1vvLbdZReABEjZL/lmplf0t2SLpE0RdL1ZjblqMMukTQ++LhV0r199j0s6eJ+vvSdkl52zo2X9HLwPYCT9OK2CrV19TCcGQAgSbphYZG+e8U0vbyjSrf/bj3r9AKICKG8rLNAUolzbq9zrlPS45KWHHXMEkmPul7vSEozs1xJcs69Iam2n6+7RNIjwdePSLoiFOGBSPfn9YeVnxaveaMYJAEA6PXpRaP0nSVT9dL2St3x2Dp19VB6AYS3UBbefEkH+7wvC2472WOOluOcK5ek4HO/a6mY2a1mtsbM1lRXV59UcCDS1TR3aHlJjZbMypPPZ17HAQAMIZ9ZXKxvXz5VL26r1FcfW0/pBRDWQll4+/st2n2IYz4U59z9zrl5zrl5WVlMyAP09dzGw+oJOF0xm+HMAIAPuvm0Yn3zE1P0t60V+sfHKb0AwlcoF94sk1TY532BpMMf4pijVZpZrnOuPDj8ueojJwWGmT9vOKzJuSmakJPsdRQAwBD1uTNGK+CcvvvX7TLboJ9dO0tRfiY5BBBeQvlTa7Wk8WY22sxiJF0n6dmjjnlW0k3B2ZoXSWp4d7jycTwr6ebg65slPXMqQwORrrSmRRsO1uuKWXleRwEADHFfOHOM/v3SyfrrpnL9w+NMZAUg/ITsCq9zrtvM7pD0giS/pAedc1vN7Lbg/vskLZV0qaQSSa2Sbnn3fDP7vaRzJGWaWZmk/3TO/VrS9yU9aWafl3RA0jWh+gxAJPrT+kMyky6n8AIABuCLZ42RmfTdv25Xa+ca3XvjXMXH+L2OBQADEsohzXLOLVVvqe277b4+r52k249x7vXH2H5E0vmnMCYwbAQCTk+vLdMZ4zKVmxrvdRwAQJj4wpljlBgbpX/702bd/OAq/fqz85QcF+11LAA4IW7EAIaRd/Yd0aH6Nl09t8DrKACAMHP9giLddd1srTtQpxseWKnalk6vIwHACVF4gWHk6TVlSo6L0semjvQ6CgAgDF02M0/33zRXuyqbdO0v31ZFQ7vXkQDguCi8wDDR1N6lpVvKddnMPMVFc+8VAODDOW9Sjh753AIdrm/TNb9coQNHWr2OBADHROEFhom/bipXe1dA1zCcGQDwES0aM0KPfXGRmtq7dfV9K7SrssnrSADQLwovMEw8tbZM47KTNKswzesoAIAIMLMwTU/culiS9Klfvq21++s8TgQAH0ThBYaBPdXNWru/TlfPLZCZeR0HABAhJo5M1lO3LVZqfLRueOAdvbC1wutIAPA+FF5gGHh6bZn8PtNVs/O9jgIAiDCjRiTqj18+TZNyU3Tbb9fq0bdLvY4EAO+h8AIRrifg9Md1ZTp7QpayU+K8jgMAiEAjkmL1+BcX6fxJOfrmM1v1vee3KxBwXscCAAovEOle31WlysYOJqsCAIRUfIxfv/zMXH16UZF++fpe/eMTG9TR3eN1LADDXJTXAQCE1u/eOaCs5FhdMCXH6ygAgAjn95m+s2Sa8tMS9IO/7VBVY7vuv2meUuOjvY4GYJjiCi8QwQ7Vt+nVnVW6dl6hov38cwcAhJ6Z6cvnjNXPrpuldQfqdPW9K3Sovs3rWACGKX4DBiLYE6sOyEm6bkGh11EAAMPMkln5euRzC1TR2K4lv3hLa/fXeh0JwDBE4QUiVFdPQI+vPqhzJ2arID3B6zgAgGHotLGZ+tNXTlNSrF/X379ST6456HUkAMMMhReIUC9vr1RVU4duXFjkdRQAwDA2LjtZf779dC0YnaH/8/Qmfee5beruCXgdC8AwQeEFItTvVh5QXmqczpmY7XUUAMAwl5YQo4dvma9bTi/Wr5fv0y0Pr1ZDa5fXsQAMAxReIAKV1rTozd01um5Bkfw+8zoOAACK8vv0n5dN1Q8/OUPv7D2iK+55SyVVTV7HAhDhWJYIiEC/X3VAfp/p2vlMVgUAOLbHVh7w5Pt+7vTR+u3KA/r4Xct13fxCTRyZckq+7g3cxgPgKFzhBSJMe1ePnlpbpgsmZysnJc7rOAAAfMCoEYm6/ZyxGpEYo0ff3q+Xd1Qq4JzXsQBEIAovEGGe2XBItS2d+uxpo72OAgDAMaUlxOjWs8ZqVmGaXt5epUdWlKq5o9vrWAAiDIUXiCDOOT24vFSTc1O0aEyG13EAADiumCifrp5boCtn5WtfTYt+8cpu7T/S4nUsABGEwgtEkLdKjmhnZZM+f8ZomTFZFQBg6DMzzR+doS+dPVZRfp8eeHOvlpfUyDHEGcApQOEFIsiDb+1TZlKsLpuZ63UUAABOSn5avG4/Z5wmjUzR0s3lemzVAbV39XgdC0CYo/ACEWJPdbNe2VGlzywapdgov9dxAAA4afExft24sEiXTBup7eWNuvvVEh2ub/M6FoAwRuEFIsTDb5UqJsqnGxexJAMAIHyZmc4cn6UvnjlGXT0B3fv6Hi0vqWEWZwAfCoUXiAD1rZ16em2ZrpiVp8ykWK/jAADwkY0akaivnjdeE3KStXRzuR5ZUarG9i6vYwEIMxReIAI8vvqg2rp69LkzWIoIABA5EmOj9OmFRbpiVr5Kj7Torpd3a9vhRq9jAQgjFF4gzLV39ejB5ft0+rgRmjQyxes4AACcUmamBaMzdPu545QWH63frtyvP68/pM7ugNfRAIQBCi8Q5p5eW6aqpg7dfs44r6MAABAy2clxuu2csTprfKZWl9bqF0xoBWAAKLxAGOvqCei+1/dodlGaFo8d4XUcAABCKsrn08XTcvW5M0ars7tH9762R6/sqFJPgAmtAPSPwguEsWc3HFZZXZvuOHeczMzrOAAADIqxWUn6h/PGa0peil7aXql7Xy9RRWO717EADEEUXiBMBQJO97xWosm5KTpvUrbXcQAAGFQJsVG6fkGRblhQpIbWLt39Sol+8cpudfdwby+Av6PwAmHqb1srtKe6RbefO5aruwCAYWtafqr+8YIJmpKXov/34i5dec8K7axo8joWgCGCwguEIeecfv5KicZkJeqSablexwEAwFNJwau999w4R4fq23TZz5fr7ldLuNoLgMILhKNXd1Zpe3mjvnz2WPl9XN0FAECSLp2eq2VfP0sXTsnRj17YqSvueUubyxq8jgXAQxReIMwEAk4/fnGXCtLjdcXsfK/jAAAwpIxIitXdN87RPTfOUWVjh5bcvVzf/stWNXd0ex0NgAcovECY+evmcm093Kh/vmiCov38EwYAoD+XTs/VS/90tm5YWKSHV5Tqwv99XS9urfA6FoBBFtLfls3sYjPbaWYlZnZnP/vNzO4K7t9kZnNOdK6ZfcvMDpnZhuDj0lB+BmAo6eoJ6Mcv7tSkkcm6fCZXdwEAOJ7U+Gh994rpevq205QaH61bf7NWtz66RuUNbV5HAzBIQlZ4zcwv6W5Jl0iaIul6M5ty1GGXSBoffNwq6d4BnvsT59ys4GNpqD4DMNQ8ueagSo+06hsfm8i9uwAADNDcUen6y1fP0L9ePElv7K7WBT9+XQ+9tY9JrYBhIJRXeBdIKnHO7XXOdUp6XNKSo45ZIulR1+sdSWlmljvAc4Fhpa2zR3e9vFtzR6Wz7i4AACcp2u/Tl88Zqxe/drbmFmfo23/Zpk/8fLne3nPE62gAQiiUhTdf0sE+78uC2wZyzInOvSM4BPpBM0vv75ub2a1mtsbM1lRXV3/YzwAMGY+8XarKxg7968WTWHcXAIAPqWhEgh65Zb7uuXGOmtq7df0D7+j2363ToXqGOQORKJSFt7/fyN0AjzneufdKGitplqRyST/u75s75+53zs1zzs3LysoaUGBgqGpo7dK9r+3RuROztGB0htdxAAAIa2b23qRWX7tgvF7aXqnzf/yafvrSLrV39XgdD8ApFMrCWyapsM/7AkmHB3jMMc91zlU653qccwFJD6h3+DMQ0X7y0i41tnfpGx+b5HUUAAAiRnyMX1+7YIJe/uezdf6kHP30pd06/8eva+nmcjl39HUaAOEolIV3taTxZjbazGIkXSfp2aOOeVbSTcHZmhdJanDOlR/v3OA9vu+6UtKWEH4GwHM7K5r0m3f264YFRZqSl+J1HAAAIk5BeoLuvnGOfv/FRUqOi9JXfrdO193/jjYcrPc6GoCPKCpUX9g5121md0h6QZJf0oPOua1mdltw/32Slkq6VFKJpFZJtxzv3OCX/qGZzVLvEOdSSV8K1WcAvOac038+u0XJcVH6l4smeh0HAICItnjsCD331TP0+9UH9dNlu3TF3W/pkmkj9S8fm6ixWUlexwPwIYSs8EpScMmgpUdtu6/Payfp9oGeG9z+mVMcExiy/rq5XO/srdV3r5im9MQYr+MAABDxovw+fWbRKF05O1+/enOvHnhjr17cVqlr5xfqa+ePV3ZKnNcRAZyEUA5pBvARtHZ267//ul1T81J0/YIir+MAADCsJMVG6WsXTNDr/+dcfWbRKD215qDO+tGr+tELO9TY3uV1PAADROEFhqi7Xy1ReUO7vn35VPl9LEMEAIAXMpNi9a3Lp+rlfzpHH5s6Une/ukdn/fBV3f1qiZoovsCQR+EFhqBdlU164I19unJ2vuYVswwRAABeKxqRoJ9dN1vPffUMzS5M049e2KkzfvCqfvrSLjW0UnyBoYrCCwwx3T0BfeOpjUqKi9K/f3yy13EAAEAf0/JT9dAtC/TsHadrwegM/fSl3TrjB6/oRy/sUG1Lp9fxAByFwgsMMQ+8uU8byxr0X0umKjMp1us4AACgHzMK0vTATfO09B/O1JkTMnXPa3t0xg9e0feWbld1U4fX8QAEhXSWZgAnZ3dlk36ybJcunT5Sn5iR53UcAABwAlPyUnTPjXO1u7JJv3i1RA+8uVcPrSjVFbPydMvpozU5N8XriMCwRuEFhojunoD+5elNSoz167+WTPM6DgAAOAnjc5L1s+tm6x/PH68H39qnp9eW6ck1ZTpt7Ah9/ozROnditnxMQgkMOoY0A0PEA2/u08aD9fqvJdMYygwAQJgak5Wk714xXe/83/P1rxdP0t7qFn3+kTU6/39f1yMrStXS0e11RGBYofACQ8C6A3X68Ys7g0OZc72OAwAAPqK0hBh9+ZyxevNfz9Vd189WSny0/vPZrVr0vZf1rWe3ant5o9cRgWGBIc2Ax+paOvXVx9YrNy1O37tqhswY7gQAQKSI9vt0+cw8XT4zT+sO1Onht0r12MoDenhFqWYWpOra+UW6bGaukuOivY4KRCQKL+ChQMDpn5/aqOqmDj395cVKjef/7AAAiFRzitI1pyhddS2d+tP6Q3pi9UH925826zvPbdMnZuTqugWFmlOUzh+/gVOIwgt46P439+qVHVX69uVTNaMgzes4AABgEKQnxuhzZ4zWLacXa8PBej2x+qCe3XhYT60t09isRF0+M1+Xz8rT6MxEr6MCYY/CC3hk1b5a/eiFnfr49FzdtHiU13EAAMAgMzPNLkrX7KJ0/ccnpuivmw7rj+sO6acv79JPXtql/LR4zSxI1fSCtIgYBXbDwiKvI2AYovACHiitadGXfrNGozIS9P1PTmfoEgAAw1xSbJSunV+ka+cXqbyhTc9tLNfDK0q1dEuFnt9SoeLMRM0sSNOUvBQlxfIrPDBQ/GsBBlltS6c++9AqmZke/Ox8JqkAAADvk5sary+eNUaJsVGqaerQxkP12niwQX/ecEjPbDikUSMSNCUvVVNyU5SRGON1XGBIo/ACg6i9q0e3PrpGhxva9fsvLlQx9+YAAIDjyEyO1fmTcnTexGyVN7RrW3mjth1u1NLN5Vq6uVwjU+I0JS9FU3JTlJsax6gx4CgUXmCQBAJO33h6k9bsr9PdN8zR3FEZXkcCAABhwsyUlxavvLR4XTA5R7Utndp2uEHbyhv16o4qvbKjSmnx0ZqQk6wJOckam52o2Ci/17EBz1F4gUEQCDj9f89s0V82Htadl0zSx2fkeh0JAACEsYzEGJ0xPktnjM9Sc0e3tpc3akdFkzaU1WtVaa38ZhqVmaCJwQKcnRzL1V8MSxReIMQCAaf/eGaLHlt5QF8+Z6y+dNYYryMBABCRHlt5wOsInkiKjdL84gzNL85QdyCg/UdatauiSTsrm/R8cNKr1PhoTchJ6r36m5WkuGiu/mJ4oPACIdS37H7lnLH6xscm8tdVAAAQMlE+n8ZmJWlsVpIumZ6r+tZO7a5s1s7KJm0qa9Dq0jr5TBo1IlHjspM0LitJeWnx8vv4/QSRicILhEhPwOk//rxFv191QLefO1b/chFlFwAADK60hBjNH52h+aN7r/4eONKqXZVN2lXZrGXbKrVMlYqN8mlMVpLGZiVqXFaSshj+jAhC4QVCoKWjW//4+Hq9tL1Kd5w7Tv980QT+jwMAAHgqytdbbMdkJeniaVJzR7f2VjerpKpZe6qbtb28UZKUHBelsVm9V3/HZicpNZ4lFBG+KLzAKVbe0KbPP7xGOyoa9Z0lU/WZxcVeRwIAAPiApNgozShI04yCNElSbUun9lQ1q6S6Wbsqm7ThYL0kKTMpVmOzEt8bKh0fw/2/CB8UXuAU2nKoQZ9/ZLVaOnr04Gfn65yJ2V5HAgAAGJCMxBhlBIc/B5xTZWP7ewV4/YF6rdxXK5OUlxavMVmJGpOZqFEjEpkAC0MahRc4BZxzemRFqf7n+R3KTIzR019erEkjU7yOBQAA8KH4zJSbGq/c1HidMT5L3YGAymrbVFLdO/x5RckRvbm7RiYpPz1eo0ckanRWooopwBhiKLzAR3SkuUP/5+lNenlHlc6dmKUfXTNTmUmxXscCAAA4ZaJ8PhVnJqo4M1EXTM5RZ3dAB+tatbe6RftqmrVi7xG9WVLz9yvAmRRgDA0UXuAjeHVnlf716U2qb+3SNz8xRbecXszkVAAAIOLFRP19+SMpR109AR2obdW+mhbtrW7ptwCPTI3VvOIMpcQxCRYGD4UX+BAO17fpv/6yTX/bWqFx2Ul66Jb5mpqX6nUsAAAAT0T7+xTgyVJXT0AHa1u1t6ZF+2r+XoB9Jk0cmaJ5o9I1rzhd84ozlJ8W73V8RDAKL3ASOrp79NBbpfrZS7vl5PSNj03UF84crdgohuoAAAC8K9r/9yWQpN4CPCEnWSv3HdHa/XX647oy/ead/ZKk3NQ4zR2VHizBGZo0MllRfp+X8RFBKLzAAHR2B/TkmoO6+9USlTe068IpOfrmJ6aoMCPB62gAAABDXrTfp8VjR2jx2BGSpO6egHZUNGnt/jqt2V+nNaW1em5TuSQpMcav2UXpmjsqXXNGpWtmQarSEmK8jI8wRuEFjqOts0d/XF+me17do0P1bZo7Kl0/unqmzhif6XU0AACAsBXl92lafqqm5afq5tOKJUmH6tu0prRWa/fXaXVpne56Zbec6z1+1IgEzSxI08zCNM0qTNXUvFQmw8KAUHiBfuyradFv39mvp9YcVGN7t2YVpul/rpqus8ZnMikVAABACOSnxSt/Vr6WzMqXJDW1d2nzoQZtPNigjQfrtbq0Vs9uPCxJ8vtME3OSNTUvRVPyUjQlN0WT81KYEAsfQOEFgupaOvW3rRX6y8bDWrHniKJ8pounjdRnFo3SgtEZFF0AAIBBlBwXrdPGZuq0sX8fWVfZ2K6NB+u1saxem8oa9MqOKj21tuy9/UUZCb0lODdFE0cma3xOsooyEuT38XvccEXhxbB2sLZVb+yu1otbK/VWSY26A07FIxL09Qsm6PoFhcpOifM6IgAAAIJyUuJ00dSRumjqSEmSc07VTR3aerhR28obte1wo7YebtDzWyreOycmyqcxmYkan5Os8dlJGp+dpHHZSSrMSGBY9DBA4cWw4ZxTWV2b1h2o0+rSWi3fXaPSI62SpMKMeH3hzDH6xIxcTc1L4WouAABAGDAzZafEKTslTudOyn5ve3NHt0qqmrW7sqn3uapZ6w/U6S/BIdG950q5KXEaNSJRo0Yk9HlOUEF6glLiovidMAKEtPCa2cWSfibJL+lXzrnvH7XfgvsvldQq6bPOuXXHO9fMMiQ9IalYUqmkTznn6kL5ORB+OrsD2lfTop2VTdpZ0agd5U3aWFavmuZOSVJCjF+Lx4zQZ08r1pkTsjQmM5EfaAAAABEiKTZKswrTNKsw7X3bWzu7taeqRSXVTdp/pFUHjrSq9EiLXtpe+d7vie9KjPErNy1eualxykuN18jUOOWlxSk3NV6ZSbHKTI7RiMRYhksPcSErvGbml3S3pAsllUlabWbPOue29TnsEknjg4+Fku6VtPAE594p6WXn3PfN7M7g+38N1efA0NPZHdCRlg5VN73/cbihXQdqW7T/SKsO17cpEJzVz+8zjclM1NkTsjW7KE2zi9I0MYf13QAAAIabhJgoTS9I1fSC1A/sa2rv6i3Bta06VNemww1tKq9vV3lDm3ZUNKmmueO9WaPfZSZlJMS8V4Azk2KVnhCjlPhopcRFKSU+Wqnx0UqJi1ZKfJRS4qKVmhCtpJgo+SjKgyKUV3gXSCpxzu2VJDN7XNISSX0L7xJJjzrnnKR3zCzNzHLVe/X2WOcukXRO8PxHJL0mCm9EeHVHlXZXNam5o0fN7d1q7uhSc0e3mtq733uuae5QfWtXv+dnJMaoKCNBc0el66rZ+RqbnaQJOckak5Wo2CjuzwAAAMCxJcdFv7dUUn86uwOqbGxXeUO7apo7go/O3uem3vfrD9SrrrVTTe3dx/1eZlJybJSS46IVH+NXfHTvIy7Gr/hoX+/7GL/iovvsi/Yr2m+K8vsU4/cpKvg62hd89pui/T5F9Xkf5fMpJqr32WcmM8nnM/lMf39vFnz0DhH39dlmHzhOYTcqMpSFN1/SwT7vy9R7FfdEx+Sf4Nwc51y5JDnnys0sW4gIT645qOe3VMhMSoqJUlJclJJi//48MiVOi8eMUFZybO8jKfa91yOSYii1AAAACJmYKJ8KMxJUmJFwwmN7Ak7NHd1qbOtSQ1uXGtu71NjWpca27vdeN7R1qam9W+3dPWrr7FFbV48a2rpU2dD7uq2rR+2dPWrt6lFPwJ3wew6m1f9+gbKSY72OMSChLLz9Vf+j/5c61jEDOff439zsVkm3Bt92mNkWSe/OaV7Tz+v+th29HwAAAMCHcKPXAXDKZP/A6wT9GtXfxlAW3jJJhX3eF0g6PMBjYo5zbqWZ5Qav7uZKqurvmzvn7pd0vySZ2Rrn3DwzWxPc94HXA9l/sv8BAAAAAADeCeWsPasljTez0WYWI+k6Sc8edcyzkm6yXoskNQSHKx/v3Gcl3Rx8fbOkZ0L4GQAAAAAAYSpkV3idc91mdoekF9S7tNCDzrmtZnZbcP99kpaqd0miEvUuS3TL8c4NfunvS3rSzD4v6YCka0L1GQAAAAAA4Suk6/A655aqt9T23XZfn9dO0u0DPTe4/Yik808yyv1HPR/r9Yn2AwAAAADChLmjF5MCAAAAACAChPIeXgAAAAAAPBPSIc2hZmYXS/qZpGRJ0ZK6JCUEH23qLfRJwcOdpB71LnnkdOzP3t+ySH0vg9cHv36spEpJFZK6mcUZAAAAAIaWsL3Ca2Z+SXdL+rh6y22NegttgqSrJMUFHz+XVCcpoN7ZnqvUOxGWC57XKqlDUmdw2+7g8ZJUq94S3SXpYvUuo5Qo6XJJ+4Pf8wbKLgAAAAAMPWFbeCUtUO/szlnB59fUW1hL1VuCKyU1SCqXtE29ZXiBpBT1Fl2n3uLbGtzXFnzO1N+vAr97pXi/pLnqvYpcFnwtSX+StCSEnxEAAAAA8CGF85DmfEkH+zx3qbeoHpZULKlJvYU2X1Kzesv9l9V7hfZv6r1iGyNpRPDrdaj3v0dK8OuYeq8Qx6h3WPS/qfcqcZKkrwe/3mckJZjZEeccMzkDAAAAwBASzoXXjvHcl9Pf778NSDqi3qJ6unoLcbektOC55er979Gj3nuCt6q3/Baot0x3qLdA75C0WdJi9V7hHSnpdjPb4Zx745R9OgAAAADARxLOQ5rLJBX2eY5Sb7nNU++w5mT1XqE9rL9PXJUuKV69V3kT1VtonXrv330luC9Fvev/5qh3KHRX8GtvDX6tQkl7JD0laU7w9Z/UO1waAAAAADBEhHPhXS1pvKTq4PM56v08xZKeU29hTZW0QdJk9V6dvUbSIfVe2W1R7wRWPvUW2ozgc5V6r+qmqbfkRgePyVZvic5U7z3Dl0iaJGmZpIskbQndRwUAAAAAnCxzzp34qCHKzC6V9FO9f1mitODr7uBhsSf5Zftbliigvw+NblTv1WG/est2raTHnHP/fZLfBwAAAAAQQmFdeAEAAAAAOJZwHtIMAAAAAMAxUXgBAAAAABGJwgsAAAAAiEgUXgAAAABARKLwAgAAAAAiEoUXAIAwZGZXmpkzs0leZwEAYKii8AIAEJ6ul7Rc0nVeBwEAYKii8AIAEGbMLEnS6ZI+r2DhNTOfmd1jZlvN7DkzW2pmVwf3zTWz181srZm9YGa5HsYHAGDQUHgBAAg/V0j6m3Nul6RaM5sj6SpJxZKmS/qCpMWSZGbRkn4u6Wrn3FxJD0r6bw8yAwAw6KK8DgAAAE7a9ZJ+Gnz9ePB9tKSnnHMBSRVm9mpw/0RJ0yQtMzNJ8ksqH9S0AAB4hMILAEAYMbMRks6TNM3MnHoLrJP0p2OdImmrc27xIEUEAGDIYEgzAADh5WpJjzrnRjnnip1zhZL2SaqR9Mngvbw5ks4JHr9TUpaZvTfE2cymehEcAIDBRuEFACC8XK8PXs39g6Q8SWWStkj6paSVkhqcc53qLck/MLONkjZIOm3Q0gIA4CFzznmdAQAAnAJmluScaw4Oe14l6XTnXIXXuQAA8Ar38AIAEDmeM7M0STGSvkPZBQAMd1zhBQAAAABEJO7hBQAAAABEJAovAAAAACAiUXgBAAAAABGJwgsAAAAAiEgUXgAAAABARKLwAgAAAAAi0v8PBbOihe6bc10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age for diabetic\n",
    "fig = plt.figure(figsize = (16,6))\n",
    "\n",
    "sns.distplot(df[\"Age\"][df[\"Outcome\"] == 1])\n",
    "plt.xticks([i*0.15 for i in range(1,12)])\n",
    "plt.title(\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historical-mexico",
   "metadata": {},
   "source": [
    "Data pre-processing using standard scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "peaceful-farming",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperating dependent and independent columns\n",
    "X = df.drop([\"Pregnancies\",\"BloodPressure\",\"SkinThickness\"],axis = 1)\n",
    "y = df.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "crude-group",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train size: (614, 6)\n",
      "y_train size:  (614,) \n",
      "\n",
      "X_test size: (154, 6)\n",
      "y_test size: (154,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)\n",
    "\n",
    "print(\"X_train size:\", X_train.shape)\n",
    "print(\"y_train size: \",y_train.shape,\"\\n\")\n",
    "print(\"X_test size:\", X_test.shape)\n",
    "print(\"y_test size:\",y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "acquired-brown",
   "metadata": {},
   "outputs": [],
   "source": [
    "#standard scaling\n",
    "\n",
    "sc= StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "vanilla-aviation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StandardScaler()\n"
     ]
    }
   ],
   "source": [
    "print(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "selected-midwest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.91569367  0.3736349   0.37852648  0.67740401  1.69955804  1.33352186]\n",
      " [-0.75182191 -0.69965674 -0.50667229 -0.07049698 -0.96569189 -0.74989397]\n",
      " [ 1.38763205  5.09271083  2.54094063 -0.11855487 -0.88240283 -0.74989397]\n",
      " ...\n",
      " [-0.84620959 -0.69965674 -0.94927168 -0.95656442 -1.04898095 -0.74989397]\n",
      " [-1.12937261 -0.69965674 -0.26640405 -0.50001442  0.11706589 -0.74989397]\n",
      " [ 0.47521786 -0.69965674 -4.07275877  0.52121586  2.94889395 -0.74989397]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "protecting-tribute",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.39507259e+00 -6.69261578e-01  1.52657475e+00  2.78935129e+00\n",
      "  -9.30642826e-01  1.50883920e+00]\n",
      " [-4.25892449e-01  2.80308009e-01  3.19441160e-01 -2.76988247e-01\n",
      "  -8.35980346e-01 -6.62761146e-01]\n",
      " [-1.37643502e+00 -6.69261578e-01  3.71360884e-01 -3.17253311e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.38320470e+00  9.92485199e-01 -6.92993461e-01  2.89819971e-01\n",
      "   1.81456910e+00  1.50883920e+00]\n",
      " [-3.03241795e-01 -6.69261578e-01 -8.48752633e-01  5.15923795e-01\n",
      "  -7.86805031e-02 -6.62761146e-01]\n",
      " [-1.22312170e+00 -2.89433743e-01 -5.89154013e-01 -6.51763079e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [ 9.53927407e-01  9.54502416e-01  3.97320746e-01  1.23759765e+00\n",
      "   1.10644458e-01  1.50883920e+00]\n",
      " [ 1.68983133e+00  8.12066978e-01  2.80501367e-01  2.04599625e+00\n",
      "   1.90923158e+00  1.50883920e+00]\n",
      " [ 1.87360820e-01  1.94205479e+00 -4.46374771e-01  3.42739770e+00\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [-5.48543103e-01  1.13492064e+00  8.51618332e-01 -5.24773260e-01\n",
      "   2.19321902e+00 -6.62761146e-01]\n",
      " [ 1.13790339e+00  1.65718391e+00  5.98425392e-02  1.10751051e+00\n",
      "  -3.62667944e-01  1.50883920e+00]\n",
      " [ 2.02712063e+00  1.29634747e+00  1.61743426e+00  1.67431873e+00\n",
      "   2.00389406e+00  1.50883920e+00]\n",
      " [-2.11253805e-01 -6.69261578e-01 -8.09812840e-01  7.76098059e-01\n",
      "   2.38254399e+00 -6.62761146e-01]\n",
      " [-1.19245904e+00  4.22743447e-01 -3.42535323e-01  3.73403165e+00\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [-1.71372432e+00 -6.69261578e-01  1.11762263e-01  3.30085035e-01\n",
      "   9.62606780e-01 -6.62761146e-01]\n",
      " [-3.95229786e-01 -6.69261578e-01 -4.98294495e-01  9.09282503e-01\n",
      "   1.59819772e-02 -6.62761146e-01]\n",
      " [ 1.78181932e+00  5.65178885e-01  2.02621781e-01  6.98665242e-01\n",
      "   3.94631898e-01  1.50883920e+00]\n",
      " [-1.46842301e+00 -6.69261578e-01 -1.05643153e+00 -7.60169022e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [ 3.40674138e-01 -6.69261578e-01 -4.07434978e-01 -2.30528557e-01\n",
      "   2.95051887e+00 -6.62761146e-01]\n",
      " [-6.71193757e-01  1.18881179e-01  3.71360884e-01  1.72564563e-02\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [ 7.08626099e-01 -6.69261578e-01  9.55457781e-01  1.87608653e-01\n",
      "   4.89294379e-01  1.50883920e+00]\n",
      " [-8.86031510e-02 -6.69261578e-01  1.52657475e+00  6.18135113e-01\n",
      "  -1.02530531e+00  1.50883920e+00]\n",
      " [-1.03914572e+00 -6.69261578e-01 -2.90615599e-01  8.65920126e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [-1.83637497e+00 -6.69261578e-01  4.23280608e-01 -7.75655585e-01\n",
      "   1.34125670e+00 -6.62761146e-01]\n",
      " [-7.32519084e-01  6.60135844e-01 -1.03047167e+00 -1.99112957e-02\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [ 9.53728299e-02 -6.69261578e-01  6.04999643e-01  6.37161462e-02\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-5.17880440e-01 -6.69261578e-01 -1.65350836e+00  2.74333407e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [ 9.84590071e-01  6.60135844e-01  1.22803633e+00  2.10794250e+00\n",
      "   6.78619339e-01 -6.62761146e-01]\n",
      " [-2.72579132e-01 -6.69261578e-01 -7.96832909e-01 -5.77427575e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-3.33904459e-01 -6.69261578e-01 -6.67033599e-01 -6.23887265e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [ 4.63324792e-01  6.12657365e-01 -6.67033599e-01  4.75658730e-01\n",
      "   1.81456910e+00 -6.62761146e-01]\n",
      " [-5.79205767e-01  7.14027001e-02  1.22803633e+00  7.96451827e-03\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-5.79205767e-01  3.27786489e-01 -2.38695875e-01  6.24329738e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [ 3.40475030e-02  8.31058370e-01  6.56919367e-01  9.99104570e-01\n",
      "  -3.62667944e-01 -6.62761146e-01]\n",
      " [-5.79404875e-02 -1.94476784e-01 -1.14729105e+00 -8.93353466e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [ 1.44453002e+00 -6.69261578e-01  2.28581643e-01 -1.11016535e+00\n",
      "   8.67944300e-01  1.50883920e+00]\n",
      " [ 4.63324792e-01  5.65178885e-01 -3.68495185e-01 -7.23001270e-01\n",
      "   9.62606780e-01  1.50883920e+00]\n",
      " [-1.19265814e-01  4.93282902e-03 -7.70873047e-01 -5.58843699e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-5.79404875e-02 -7.10327379e-02  5.66059850e-01 -6.61055017e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.81248198e+00 -4.56286685e-03  3.71360884e-01 -6.88930831e-01\n",
      "  -5.51992905e-01 -6.62761146e-01]\n",
      " [-5.79404875e-02  2.04342442e-01  4.88180263e-01  7.17249118e-01\n",
      "  -8.35980346e-01 -6.62761146e-01]\n",
      " [-9.47157728e-01 -6.69261578e-01  1.50061488e+00  2.03095216e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-6.71193757e-01 -1.56494001e-01 -5.50214220e-01  1.10618309e-02\n",
      "   1.59819772e-02 -6.62761146e-01]\n",
      " [ 2.21109661e+00 -6.69261578e-01  4.88180263e-01 -7.81850211e-01\n",
      "  -6.46655385e-01  1.50883920e+00]\n",
      " [ 2.33374726e+00  4.48690128e+00 -8.29367022e-02 -1.03892716e+00\n",
      "   2.00389406e+00  1.50883920e+00]\n",
      " [-5.79205767e-01 -6.69261578e-01 -4.04181567e+00  2.43360281e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-3.95229786e-01 -1.37502609e-01 -7.70873047e-01 -1.13184654e+00\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [ 2.48686147e-01  4.22743447e-01 -6.15113875e-01 -8.49991089e-01\n",
      "  -3.62667944e-01  1.50883920e+00]\n",
      " [-3.64567122e-01  2.70812313e-01  4.75200332e-01  1.27476540e+00\n",
      "  -5.51992905e-01  1.50883920e+00]\n",
      " [ 1.26035493e-01 -6.69261578e-01  1.50702056e-01  1.31857025e-01\n",
      "  -4.57330424e-01  1.50883920e+00]\n",
      " [ 3.71336801e-01 -6.69261578e-01 -5.37234289e-01 -7.69460960e-01\n",
      "   3.94631898e-01 -6.62761146e-01]\n",
      " [-3.95229786e-01 -7.10327379e-02  1.76661919e-01 -5.43357136e-01\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [ 9.23264744e-01  1.90407200e+00  1.38379551e+00  7.69903433e-01\n",
      "   5.83956859e-01  1.50883920e+00]\n",
      " [ 4.93987455e-01 -6.69261578e-01  1.11762263e-01 -3.17253311e-01\n",
      "   6.78619339e-01 -6.62761146e-01]\n",
      " [-9.16495065e-01  1.32483455e+00  1.13717682e+00 -3.48226438e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [-3.70679744e+00 -6.69261578e-01 -8.35772702e-01 -1.09467879e+00\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [-1.10047105e+00 -6.69261578e-01 -5.89154013e-01 -4.41145818e-01\n",
      "  -7.86805031e-02 -6.62761146e-01]\n",
      " [-1.80591141e-01 -6.69261578e-01  2.82456785e+00 -8.80964216e-01\n",
      "  -3.62667944e-01  1.50883920e+00]\n",
      " [ 4.01999465e-01 -6.69261578e-01 -2.90615599e-01  1.50440901e-01\n",
      "  -8.35980346e-01  1.50883920e+00]\n",
      " [-1.80591141e-01 -6.69261578e-01 -4.04181567e+00 -7.19903957e-01\n",
      "  -1.73342983e-01  1.50883920e+00]\n",
      " [ 1.41386736e+00  1.52424417e+00  8.38638401e-01 -1.01724597e+00\n",
      "   1.05726926e+00  1.50883920e+00]\n",
      " [ 8.00614090e-01  2.11297731e+00  4.88180263e-01 -3.35837187e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [ 1.26035493e-01 -6.69261578e-01 -8.35772702e-01 -8.90256154e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [ 5.24650119e-01 -6.69261578e-01  6.69899298e-01  1.36149015e+00\n",
      "  -6.46655385e-01  1.50883920e+00]\n",
      " [ 1.87380731e+00 -6.69261578e-01 -8.29367022e-02 -4.59729694e-01\n",
      "  -2.68005464e-01  1.50883920e+00]\n",
      " [ 2.33374726e+00 -6.69261578e-01 -6.80013530e-01  2.16059682e+00\n",
      "   6.78619339e-01  1.50883920e+00]\n",
      " [ 1.35254203e+00 -6.69261578e-01 -9.59166333e-02 -5.92914138e-01\n",
      "   1.62524414e+00  1.50883920e+00]\n",
      " [ 3.40475030e-02 -6.69261578e-01 -1.05643153e+00 -7.41585146e-01\n",
      "   7.73281820e-01 -6.62761146e-01]\n",
      " [-4.56555113e-01 -6.69261578e-01  7.92281505e-03 -3.51323751e-01\n",
      "   9.62606780e-01 -6.62761146e-01]\n",
      " [-1.49908567e+00 -6.69261578e-01  5.98425392e-02 -6.61055017e-01\n",
      "   5.83956859e-01 -6.62761146e-01]\n",
      " [-3.95229786e-01 -6.69261578e-01 -4.39969091e-02 -1.03892716e+00\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-5.79404875e-02 -6.69261578e-01 -7.70873047e-01 -8.80964216e-01\n",
      "   4.89294379e-01 -6.62761146e-01]\n",
      " [-1.06980838e+00 -6.69261578e-01  6.04999643e-01 -7.91142149e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.90446998e+00 -6.69261578e-01  1.25399619e+00  2.99687124e+00\n",
      "   1.24659422e+00 -6.62761146e-01]\n",
      " [ 1.53651801e+00 -6.69261578e-01  1.61743426e+00 -4.46897970e-02\n",
      "  -5.51992905e-01  1.50883920e+00]\n",
      " [ 4.93987455e-01 -6.69261578e-01  7.92281505e-03 -7.47779771e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [-1.62173632e+00 -2.03972480e-01  5.66059850e-01 -6.45568453e-01\n",
      "   1.43591918e+00 -6.62761146e-01]\n",
      " [ 1.01525273e+00  1.16340773e+00 -2.38695875e-01  1.07034276e+00\n",
      "   6.78619339e-01 -6.62761146e-01]\n",
      " [-1.16179637e+00 -6.69261578e-01 -2.38695875e-01 -5.46454448e-01\n",
      "   2.05306938e-01 -6.62761146e-01]\n",
      " [-7.32519084e-01 -6.69261578e-01  9.03538057e-01 -8.53088402e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [-6.40531094e-01 -6.69261578e-01  9.81417643e-01 -9.39813156e-01\n",
      "   9.62606780e-01 -6.62761146e-01]\n",
      " [ 4.63324792e-01  1.26786038e+00  8.12678539e-01 -2.92474810e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-3.33904459e-01  5.17700406e-01  1.63681987e-01  6.33621676e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [-7.32519084e-01  1.09385484e-01 -1.67946822e+00 -6.02206076e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-7.93844411e-01 -6.69261578e-01  5.53079919e-01 -6.48665766e-01\n",
      "  -3.62667944e-01 -6.62761146e-01]\n",
      " [-8.85832401e-01 -6.69261578e-01  1.43571523e+00 -7.94239461e-01\n",
      "  -2.68005464e-01 -6.62761146e-01]\n",
      " [ 5.55312782e-01 -6.69261578e-01 -7.18953323e-01 -2.83182872e-01\n",
      "  -9.30642826e-01  1.50883920e+00]\n",
      " [-5.48543103e-01  1.18881179e-01  1.57849447e+00 -9.61494345e-01\n",
      "   1.10644458e-01 -6.62761146e-01]\n",
      " [ 1.84314465e+00  1.15391203e+00  6.17979574e-01  2.86722658e-01\n",
      "   1.81456910e+00  1.50883920e+00]\n",
      " [-8.24507074e-01  4.22743447e-01  1.60445433e+00 -4.53535068e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [ 9.53728299e-02 -6.69261578e-01 -4.33394840e-01 -1.21857130e+00\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [ 2.48686147e-01 -6.69261578e-01 -4.07434978e-01 -6.48665766e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [-2.41916468e-01  1.37872571e-01 -2.12736013e-01  4.10615164e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [-5.48543103e-01  1.09385484e-01 -1.52370905e+00 -7.52204504e-03\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [ 1.32187937e+00 -3.30499545e-02  2.15601712e-01 -4.72118944e-01\n",
      "   1.71990662e+00 -6.62761146e-01]\n",
      " [-3.03241795e-01 -2.51450959e-01 -2.12736013e-01 -1.96458117e-01\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [-7.32519084e-01  1.94846746e-01  1.26697613e+00  1.16945677e+00\n",
      "   1.59819772e-02  1.50883920e+00]\n",
      " [ 9.53927407e-01  1.91356770e+00  1.34485571e+00 -6.92028143e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [-3.03241795e-01  1.29634747e+00  7.73738746e-01  2.77696204e+00\n",
      "   2.28788150e+00  1.50883920e+00]\n",
      " [-1.80591141e-01 -6.69261578e-01 -2.90615599e-01 -3.63713001e-01\n",
      "   1.34125670e+00  1.50883920e+00]\n",
      " [-2.11253805e-01  2.03701174e+00  1.69531385e+00 -1.01105135e+00\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [-6.71193757e-01  2.23333834e-01 -8.48752633e-01  4.44685604e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-3.33904459e-01 -6.69261578e-01  8.38638401e-01 -9.36715844e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [-7.93844411e-01 -3.08425135e-01 -1.49774919e+00 -4.93800133e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 2.18023484e-01 -6.69261578e-01  1.15015675e+00  1.88183868e+00\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-3.95229786e-01 -6.69261578e-01 -8.29367022e-02  1.42963103e+00\n",
      "   1.10644458e-01  1.50883920e+00]\n",
      " [ 1.56698157e-01  4.70221927e-01 -4.85314564e-01  6.68134589e-02\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [ 4.93987455e-01 -6.69261578e-01  2.67521436e-01 -1.00175941e+00\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [-6.71193757e-01 -6.15370421e-02 -1.53668898e+00 -6.48665766e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [ 1.90446998e+00 -6.69261578e-01 -1.01749174e+00  5.53091547e-01\n",
      "   1.59819772e-02  1.50883920e+00]\n",
      " [-1.00848306e+00 -5.17330444e-01 -3.55515254e-01  8.44238937e-01\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [-2.72579132e-01  9.92485199e-01  3.97320746e-01 -7.23001270e-01\n",
      "   3.94631898e-01  1.50883920e+00]\n",
      " [ 6.77963436e-01 -6.69261578e-01  4.88180263e-01 -1.12874923e+00\n",
      "   8.67944300e-01  1.50883920e+00]\n",
      " [ 2.18023484e-01 -1.18511217e-01  1.11762263e-01  2.56324746e+00\n",
      "   1.10644458e-01  1.50883920e+00]\n",
      " [ 1.59784334e+00  3.83169826e+00  5.92019712e-01 -7.29195895e-01\n",
      "  -9.30642826e-01  1.50883920e+00]\n",
      " [ 1.22989138e+00 -6.69261578e-01 -9.59166333e-02 -1.01724597e+00\n",
      "   1.43591918e+00  1.50883920e+00]\n",
      " [-3.95229786e-01  1.97054187e+00 -7.57893116e-01  1.20042989e+00\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [ 2.21109661e+00  2.89162437e+00 -6.80013530e-01  5.00437232e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-3.03241795e-01 -6.69261578e-01 -1.34856426e-01 -1.08538685e+00\n",
      "  -8.35980346e-01 -6.62761146e-01]\n",
      " [-2.72778240e-02  7.55092803e-01  1.44869516e+00  9.03087878e-01\n",
      "   1.53058166e+00  1.50883920e+00]\n",
      " [-2.72579132e-01 -6.69261578e-01  1.07227716e+00 -7.97336774e-01\n",
      "   5.83956859e-01 -6.62761146e-01]\n",
      " [-5.48543103e-01 -6.69261578e-01  1.04631730e+00 -5.83622200e-01\n",
      "   3.13984383e+00 -6.62761146e-01]\n",
      " [-1.80591141e-01  6.60135844e-01  9.03538057e-01 -1.06370566e+00\n",
      "  -3.62667944e-01 -6.62761146e-01]\n",
      " [ 3.38483947e-03 -6.69261578e-01 -6.02133944e-01 -7.26098583e-01\n",
      "   2.85585639e+00 -6.62761146e-01]\n",
      " [ 6.77963436e-01 -9.00241297e-02 -6.41073737e-01 -7.35390521e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-9.47157728e-01 -6.69261578e-01 -9.91531875e-01 -9.36715844e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.87360820e-01  8.02571282e-01  4.36260539e-01  3.23890410e-01\n",
      "  -3.62667944e-01 -6.62761146e-01]\n",
      " [-1.49928478e-01 -6.69261578e-01  1.50702056e-01  5.15923795e-01\n",
      "   2.99969418e-01  1.50883920e+00]\n",
      " [ 4.32662128e-01 -6.69261578e-01 -5.76174082e-01  5.99551237e-01\n",
      "   2.85585639e+00 -6.62761146e-01]\n",
      " [ 1.65916867e+00 -6.69261578e-01 -1.06941146e+00 -5.18578634e-01\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [ 5.55312782e-01  6.60135844e-01 -3.29555392e-01 -2.55307058e-01\n",
      "  -5.51992905e-01 -6.62761146e-01]\n",
      " [-4.87217776e-01 -6.69261578e-01 -4.04181567e+00 -5.83622200e-01\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-1.22312170e+00 -6.69261578e-01 -4.46374771e-01 -6.30081890e-01\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.56718068e+00  4.82874633e+00  1.46167509e+00  6.46010927e-01\n",
      "  -3.62667944e-01  1.50883920e+00]\n",
      " [-8.24507074e-01  8.08983960e-02  2.80501367e-01  7.57514183e-01\n",
      "   8.67944300e-01 -6.62761146e-01]\n",
      " [-5.79205767e-01 -6.69261578e-01  2.28581643e-01  5.31410358e-01\n",
      "   1.34125670e+00  1.50883920e+00]\n",
      " [-4.25892449e-01 -6.69261578e-01 -3.68495185e-01 -9.67688970e-01\n",
      "  -2.68005464e-01 -6.62761146e-01]\n",
      " [-5.48543103e-01 -6.69261578e-01  1.03333737e+00 -4.62827006e-01\n",
      "  -7.86805031e-02  1.50883920e+00]\n",
      " [ 6.16638109e-01 -6.69261578e-01 -1.47836357e-01  8.28752374e-01\n",
      "  -4.57330424e-01  1.50883920e+00]\n",
      " [-2.72778240e-02 -6.69261578e-01 -5.63194151e-01 -1.19025301e-01\n",
      "  -4.57330424e-01 -6.62761146e-01]\n",
      " [-1.03914572e+00 -1.75485392e-01  2.02621781e-01 -1.01414866e+00\n",
      "  -6.46655385e-01 -6.62761146e-01]\n",
      " [ 1.01525273e+00  5.27196102e-01  2.09027461e-02 -4.81410882e-01\n",
      "   4.89294379e-01 -6.62761146e-01]\n",
      " [-4.87217776e-01 -6.69261578e-01 -8.87692427e-01 -9.49105094e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-1.80571231e+00 -6.69261578e-01  1.89641850e-01 -3.17253311e-01\n",
      "   8.67944300e-01 -6.62761146e-01]\n",
      " [-2.11253805e-01 -6.69261578e-01  2.15601712e-01 -7.29195895e-01\n",
      "   9.62606780e-01  1.50883920e+00]\n",
      " [-6.71193757e-01 -6.69261578e-01 -7.44913185e-01  1.78316715e-01\n",
      "  -1.02530531e+00 -6.62761146e-01]\n",
      " [-1.00848306e+00 -4.50860573e-01 -8.74712496e-01 -4.69021632e-01\n",
      "  -1.73342983e-01 -6.62761146e-01]\n",
      " [-1.25378436e+00 -6.69261578e-01 -6.41073737e-01 -5.58843699e-01\n",
      "   8.67944300e-01 -6.62761146e-01]\n",
      " [-7.32519084e-01 -6.69261578e-01 -5.11274426e-01  1.86325480e+00\n",
      "  -9.30642826e-01 -6.62761146e-01]\n",
      " [-4.87217776e-01  1.14441633e+00  3.32421091e-01  6.73886741e-01\n",
      "  -2.68005464e-01  1.50883920e+00]\n",
      " [-1.10047105e+00 -2.03972480e-01 -4.33394840e-01 -5.80524888e-01\n",
      "  -3.62667944e-01 -6.62761146e-01]\n",
      " [ 6.47300773e-01 -6.69261578e-01  1.66935399e+00  4.69464105e-01\n",
      "  -9.30642826e-01  1.50883920e+00]\n",
      " [-1.49928478e-01  3.27786489e-01 -6.28093806e-01 -1.19689011e+00\n",
      "  -7.41317865e-01 -6.62761146e-01]\n",
      " [-4.25892449e-01  1.09385484e-01 -4.39969091e-02  1.01459113e+00\n",
      "  -7.41317865e-01 -6.62761146e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rocky-prospect",
   "metadata": {},
   "source": [
    "ML MODELING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "monthly-spine",
   "metadata": {},
   "source": [
    "(1)KNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "herbal-model",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn_classifier = KNeighborsClassifier(n_neighbors =5,metric = 'minkowski',p = 2)\n",
    "kn_classifier.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "casual-foundation",
   "metadata": {},
   "outputs": [],
   "source": [
    "kn_y_pred = kn_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "seeing-battle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[107   0]\n",
      " [  1  46]]\n"
     ]
    }
   ],
   "source": [
    "cm_kn = confusion_matrix(y_test, kn_y_pred)\n",
    "print(cm_kn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dependent-gospel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct: 153\n",
      "Incorrect :  1\n",
      "Accuracy: 0.9935064935064936\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct:\",sum(kn_y_pred == y_test))\n",
    "print(\"Incorrect : \",sum(kn_y_pred != y_test))\n",
    "print(\"Accuracy:\",sum(kn_y_pred ==y_test)/len(kn_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "introductory-scoop",
   "metadata": {},
   "source": [
    "(2)SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "sought-chain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier = SVC(kernel =\"linear\",random_state = 0)\n",
    "svc_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "valid-wonder",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_y_pred = svc_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "isolated-development",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[107   0]\n",
      " [  0  47]]\n"
     ]
    }
   ],
   "source": [
    "svc_cm = confusion_matrix(y_test,svc_y_pred)\n",
    "print(svc_cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "protected-identification",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct: 154\n",
      "Incorrect :  0\n",
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct:\",sum(svc_y_pred == y_test))\n",
    "print(\"Incorrect : \",sum(svc_y_pred != y_test))\n",
    "print(\"Accuracy:\",sum(svc_y_pred ==y_test)/len(kn_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artistic-outline",
   "metadata": {},
   "source": [
    "(3)naive bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "amazing-murder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_classifier = GaussianNB()\n",
    "nb_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "studied-cornell",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_y_pred =nb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "italic-genesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[107   0]\n",
      " [  0  47]]\n"
     ]
    }
   ],
   "source": [
    "nb_cm = confusion_matrix(nb_y_pred,y_test)\n",
    "print(nb_cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "eight-encounter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct: 154\n",
      "Incorrect :  0\n",
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct:\",sum(nb_y_pred == y_test))\n",
    "print(\"Incorrect : \",sum(nb_y_pred != y_test))\n",
    "print(\"Accuracy:\",sum(nb_y_pred ==y_test)/len(nb_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "international-partition",
   "metadata": {},
   "source": [
    "Result:\n",
    "the dataset consists of 768 records and 9 columns\n",
    "\n",
    "the ml model KNN was able to classify patients as diabetic or not with an accuracy of 99.35%\n",
    "\n",
    "the ml model SVM,NaiveBias were able to classify patients as diabetic or not with an accuracy of 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "expected-firmware",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tutorial-dancing",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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