{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1ab649b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fff46e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Training_df = pd.read_csv(\"Training.csv\")\n",
    "Testing_df = pd.read_csv(\"Testing.csv\")\n",
    "\n",
    "Training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6c7933d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Training_df,x=\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bf3aa9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>120</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>56</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.733</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.416</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.705</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>88</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.258</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.022</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            9      120             72             22       56  20.8   \n",
       "1            1       71             62              0        0  21.8   \n",
       "2            8       74             70             40       49  35.3   \n",
       "3            5       88             78             30        0  27.6   \n",
       "4           10      115             98              0        0  24.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.733   48        0  \n",
       "1                     0.416   26        0  \n",
       "2                     0.705   39        0  \n",
       "3                     0.258   37        0  \n",
       "4                     1.022   34        0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Testing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "336d10e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Testing_df,x=\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df4ebe80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([Training_df,Testing_df],ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "508edee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df,x=\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9578bef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.00000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "      <td>2768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.822616</td>\n",
       "      <td>121.421965</td>\n",
       "      <td>68.980491</td>\n",
       "      <td>20.549494</td>\n",
       "      <td>79.853324</td>\n",
       "      <td>31.97659</td>\n",
       "      <td>0.486277</td>\n",
       "      <td>32.923049</td>\n",
       "      <td>0.380419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.305432</td>\n",
       "      <td>31.721258</td>\n",
       "      <td>19.133100</td>\n",
       "      <td>15.779713</td>\n",
       "      <td>115.655771</td>\n",
       "      <td>7.76054</td>\n",
       "      <td>0.357403</td>\n",
       "      <td>11.362964</td>\n",
       "      <td>0.485578</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>0.00000</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>100.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.17500</td>\n",
       "      <td>0.248000</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>71.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>32.10000</td>\n",
       "      <td>0.380000</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>142.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>36.50000</td>\n",
       "      <td>0.645250</td>\n",
       "      <td>40.000000</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>67.10000</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  2768.000000  2768.000000    2768.000000    2768.000000  2768.000000   \n",
       "mean      3.822616   121.421965      68.980491      20.549494    79.853324   \n",
       "std       3.305432    31.721258      19.133100      15.779713   115.655771   \n",
       "min       0.000000     0.000000       0.000000       0.000000     0.000000   \n",
       "25%       1.000000   100.000000      64.000000       0.000000     0.000000   \n",
       "50%       3.000000   117.000000      71.000000      23.000000    36.000000   \n",
       "75%       6.000000   142.000000      80.000000      33.000000   129.000000   \n",
       "max      17.000000   199.000000     122.000000      99.000000   846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction          Age      Outcome  \n",
       "count  2768.00000               2768.000000  2768.000000  2768.000000  \n",
       "mean     31.97659                  0.486277    32.923049     0.380419  \n",
       "std       7.76054                  0.357403    11.362964     0.485578  \n",
       "min       0.00000                  0.078000    21.000000     0.000000  \n",
       "25%      27.17500                  0.248000    24.000000     0.000000  \n",
       "50%      32.10000                  0.380000    29.000000     0.000000  \n",
       "75%      36.50000                  0.645250    40.000000     1.000000  \n",
       "max      67.10000                  2.420000    81.000000     1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "af7ec343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0    122.0  33.6   \n",
       "1            1     85.0           66.0           29.0    122.0  26.6   \n",
       "2            8    183.0           64.0           30.0    122.0  23.3   \n",
       "3            1     89.0           66.0           23.0     94.0  28.1   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \n",
       "\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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_cols = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "\n",
    "df[zero_cols] = df[zero_cols].replace(0,np.nan)\n",
    "\n",
    "df[zero_cols] = df[zero_cols].fillna(df[zero_cols].median())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ded7f100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True,fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "af2b1402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0.941849\n",
       "Glucose                     0.526629\n",
       "BloodPressure               0.179202\n",
       "SkinThickness               0.276794\n",
       "Insulin                     3.388650\n",
       "BMI                         0.538308\n",
       "DiabetesPedigreeFunction    2.121402\n",
       "Age                         1.219631\n",
       "Outcome                     0.492887\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "37dd264a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pregnancies',\n",
       " 'Glucose',\n",
       " 'BloodPressure',\n",
       " 'SkinThickness',\n",
       " 'Insulin',\n",
       " 'BMI',\n",
       " 'DiabetesPedigreeFunction',\n",
       " 'Age']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = df.drop(\"Outcome\",axis=1).columns.tolist()\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ea2f677a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SCEQIiz2BFqJDj6k5iVLpNgMARBECEcKirN6n94nOw3eJBYbeU1gNAIgCBCKExYE6r94nOA4fiFKYiwgAEEUIRAiLA2YLkX8Js0N8NxcRs1UDACKPQISQ83h9UtEUiI7UQkSXGQAgmhCIEHL7axpFxSDD55X4I/yGmUXVTM4IAIgGBCKEXEllvd57a8rloCmIAli+AwAQTQhECLnSyga991bvP+I5ZpdZo9cnDR5/ATYAAJFCIELIlVaZgejAEc9xOeyS4PT/+jH0HgAQaQQihLHLrOyo59FtBgCIFgQihLGF6OiBKDAXEUPvAQARRiBCyJWaLUTfE4gYeg8AiBYEIkSshSg5zt9CVNtIUTUAILIIRAi50qqWtRAlxvmnsa5zE4gAAJFFIEJIeX2G7DVbiGqOPMpMSXQ1BSJaiAAAEUYgQkjtr2kQnyFit/knZjwaWogAANGCQISwTMqYptbsMPzrmR1JktllRgsRACDCCEQIS/1QVuL3/2qZXWZqtmqP7+jhCQCAcCIQISwtRJkJ/rBzNPFOu+5aU2glAgBEEoEIIVXSFIha0kJks9kkwSysZqQZACCCCEQIS5dZS1qIggqrqSMCAEQQgQhhaSHKTGjZr1Zg6D0tRAAAqwaipUuXyqWXXir5+fm6+2T+/PlBxw3DkKlTp0qHDh0kMTFRhg0bJlu3bg06p6ysTMaPHy9paWmSkZEhEydOlOrq6qBz1q1bJ+eff74kJCRI586dZcaMGa3y81nRXrOFKLFlLURJzEUEALB6IKqpqZE+ffrIzJkzD3tcBZdnnnlGZs2aJcuXL5fk5GQZMWKE1Nf7P3QVFYY2btwoixYtkgULFuiQdf311weOV1ZWyvDhw6WwsFBWr14tjz32mDz44IPy/PPPt8rPaDXmpIwtbiFitmoAQBTwLyYVIaNGjdLb4ajWoaeeekruu+8+ufzyy/Vjf/3rXyUvL0+3JI0bN06Kiopk4cKFsnLlShkwYIA+59lnn5XRo0fL448/rlue5syZI42NjfLiiy9KXFycnH766bJ27Vp54okngoITQqOstvG7eYiOIRCxnhkAIJKitoZo27ZtUlxcrLvJTOnp6TJo0CBZtmyZvq/2qpvMDEOKOt9ut+sWJfOcIUOG6DBkUq1MW7ZskQMHjry0RENDg25dar7h6GobPVLv9h1bIKLLDAAQBaI2EKkwpKgWoebUffOY2ufm5gYddzqdkpWVFXTO4b5H8+c4nOnTp+sAZm6q9ghHt7/a3zoU57RLgrNpgqHvQZcZACAaRG0girQpU6ZIRUVFYNu5c2ekLynqHWjqLstOjtNF8i2R5PL32jLsHgAQSVEbiNq3b6/3JSUlQY+r++YxtS8tLQ067vF49Miz5ucc7ns0f47DiY+P1yPXmm84uv01/kCUmfRd9+T3oYUIABANojYQde3aVQeWxYsXBx5TdTyqNmjw4MH6vtqXl5fr0WOmJUuWiM/n07VG5jlq5Jnb7Q6co0aknXrqqZKZmdmqP1NbV9bUZZadcgyBqKmGqMHjE6/PCNu1AQAQtYFIzRekRnypzSykVrd37Nihu1xuv/12+e1vfyuvv/66rF+/Xn72s5/pkWNjxozR5/fs2VNGjhwpkyZNkhUrVsjHH38sN998sx6Bps5Trr76al1QreYnUsPz//nPf8rTTz8tkydPjuSP3qa7zLKSWx6IElx2MTvX6pmcEQBgxWH3q1atkgsvvDBw3wwpEyZMkNmzZ8vdd9+t5ypSw+NVS9B5552nh9mrCRZNali9CkFDhw7Vo8vGjh2r5y4yqYLod955R2666Sbp37+/5OTk6MkeGXIf7i4z/3xELV3PTM1UrYbeJ8dH9FcSAGBREf30ueCCC/R8Q0f7sJw2bZrejkSNKJs7d+5Rn6d3797y4YcfntC14hi6zJJbHoiUpDh/IGL5DgBApERtDRFid1LGrGOoIVKYiwgAEGkEIoRMWVOXWdYxjDJTGGkGAIg0AhFCH4iOoahaoYUIABBpBCKEPBAdy7D7oPXM3B7eDQBARBCIEBJur08q6tzHPDFjUJdZo5d3AwAQEQQihHQOIrViR8YxBqIkc4FX5iECAEQIgQghcaDG3zqUkegSh71l65iZaCECAEQagQghsb+m4bgKqpV453fLdwAAEAkEIoS2oDo5/pi/Vi3fYS7dcbSJOgEACBcCEULigLlsR7LrmL9WLd2hqLVd3V4CEQCg9RGIENJ1zLKOo4XI5bAH6o5Y4BUAEAkEIoS4y+zYa4iCus08DL0HALQ+AhFCGogyjzcQNRVW17sprAYAtD4CEaKkhahppBlzEQEAIoBAhIiuY3ZwlxmTMwIAIoFAhCgJRE1dZsxFBACIAAIRTpiaO8hcuuO4A5E5OSNdZgCAWAlEJ510kuzfv/+Qx8vLy/UxWEtlvScwf9DxBqL4wOSMFFUDAGIkEG3fvl283kOHRzc0NMiuXbtCcV2IwUkZE12OQNfXcXeZ0UIEAIgA57Gc/Prrrwduv/3225Kenh64rwLS4sWLpUuXLqG9QkS9E+0uUxKczEMEAIiRQDRmzBi9t9lsMmHChKBjLpdLh6E//OEPob1CxEwgOp5lOw5tIaLLDAAQ5YHI5/N/WHXt2lVWrlwpOTk54bouxJCyGrfeZyadQAsRXWYAgFgJRKZt27aF/koQs8rNFqITCkT+LrMGt0+PWlOtkAAARHUgUlS9kNpKS0sDLUemF198MRTXBovMQdS8hchrGOLxGeJyEIgAAFEeiB566CGZNm2aDBgwQDp06MC/5i3uQO2Jd5k57TZx2Gw6EKmRZi4HU2QBAKI8EM2aNUtmz54t1157beivCDE77P5EiqpVF5mai6i20asLq1MTQniBAAB8j+P6Z3hjY6Occ845x/OlaMujzE6ghUihsBoAEFOB6H/+539k7ty5ob8aWDwQmbNVHzrpJwAAUddlVl9fL88//7y8++670rt3bz0HUXNPPPFEqK4PsVRDdAJdZs3XM2OBVwBATASidevWSd++ffXtDRs2BB1juLQFF3Y1a4joMgMAWCkQvffee6G/EsSkqgaPHiav0GUGAIhVjG3GCSlvmqVaLeyaGHd8C7ua4lm+AwAQSy1EF1544VG7xpYsWXIi14QYUhYoqD6x+qHmC7w2eCiqBgDEQCAy64dMbrdb1q5dq+uJDl70FVZZ2PXERpiZrUwKC7wCAGIiED355JOHffzBBx+U6urqE70mxJBQFVQHd5nRQgQAiOEaomuuuSak65h5vV65//77pWvXrpKYmCjdunWThx9+WI9sMqnbU6dO1UuIqHOGDRsmW7duDfo+ZWVlMn78eElLS5OMjAyZOHEiwS3kQ+5PPBAF5iGiywwAEMuBaNmyZZKQELo1Fx599FF57rnn5I9//KMUFRXp+zNmzJBnn302cI66/8wzz+jlRJYvXy7JyckyYsQIPVeSSYWhjRs3yqJFi2TBggWydOlSuf7660N2nVb2XQtRKGqIvusyax56AQCIyi6zK6+8Mui++vDas2ePrFq1SrfohMonn3wil19+uVx88cX6fpcuXeQf//iHrFixIvC8Tz31lNx33336POWvf/2r5OXlyfz582XcuHE6SC1cuFBWrlypF6NVVKAaPXq0PP7445Kfn3/Y525oaNCbqbKyMmQ/V9ssqg5FC1HTivc+VrwHAMRAC1F6enrQlpWVJRdccIG8+eab8sADD4Ts4tR6aYsXL5YvvvhC3//888/lo48+klGjRun727Ztk+LiYt1N1vzaBg0apFurFLVX3WRmGFLU+Xa7XbcoHcn06dODfsbOnTuH7OdqS8pDOMrM5bCJvWnwInVEAICobyF66aWXpDXce++9umWmR48e4nA4dE3R7373O90FpqgwpKgWoebUffOY2ufm5gYddzqdOsSZ5xzOlClTZPLkyYH76joIRYcqC6x0f+ItRGoqB9VKxIr3AICYCESm1atX6y4p5fTTT5d+/fpJKP3rX/+SOXPm6IVk1fdXQ/tvv/123c0V7uH98fHxesPRlTcVVWeFIBCZdUQqEDEXEQAg6gNRaWmprs95//33dXeUUl5eridsfOWVV6Rdu3Yhubi77rpLtxKp51J69eol33zzje7OUoGoffv2+vGSkhI9ysyk7ptzJalz1PU25/F49Mgz8+sRghaiENQQKfFNI83qGHoPAIj2GqJbbrlFqqqq9MgtFSzUpiZlVN1Kt956a8gurra2Vtf6NKe6znw+n76thuOrUKPqjEzqGlRt0ODBg/V9tVdhTbVmNZ9JW30PVWuE46eK2s0WolB0mTUvrG5w+99jAACitoVIjdp69913pWfPnoHHTjvtNJk5c6YMHz48ZBd36aWX6pqhgoIC3WX22WefyRNPPCG/+MUvAjUnqgvtt7/9rXTv3l0HJDXKTXWpjRkzRp+jrnHkyJEyadIkPTRfzap9880361anI40wQ8vUNHql0esLWVF10FxEtBABAKI9EKnWFZfr0A9A9ZjZehMKani8Cjg33nij7vZSAeaXv/ylnojRdPfdd0tNTY2eV0i1BJ133nk6sDWfD0nVIakQNHToUN3iNHbsWD13EUIzB1G80x5YdiNULUT1HlqIAABRHoguuugiue222/ScQGYry65du+SOO+7QoSNUUlNT9TxDajsS1Uo0bdo0vR2JGlGmCrMRpnXMkuKOutjv8U3OyPIdAIAoryFSM0erWh01UaJaTkNtqrtKPdZ8Fmm0baEccm+iywwAEDMtRGo+njVr1ug6os2bNwdqdZpPkAgrDbkPTf1QUJcZRdUAgGhtIVKjs1TxtGoJUl0kP/zhD/WIM7UNHDhQFz5/+OGH4btaRJX9TS1EGSEacm/WIyks8AoAiNpApGp51GgttWr8wdTyFqrgWY0Cg7WKqrND2mXGsHsAQJQHIrWWmBrCfiRqyH3z+X5gjRaiUM1SrZij1SiqBgBEbSBSM0Afbrh98zXC9u7dG4rrQgwoq2kIeQuROVO1R6143zTHEQAAURWIOnbsqGekPpJ169YFLaGBti0co8ziHHYxR/BTWA0AiMpANHr0aD1RYn19/SHH6urq5IEHHpBLLrkklNeHGAhEoewy0yvem3MReZiLCAAQhcPu77vvPnnttdfklFNO0TM/n3rqqfpxNfReLdvh9XrlN7/5TbiuFVEaiLKT40P6fdVcRGpxV+qIAABRGYjy8vLkk08+kRtuuEGmTJmiF/c0/1U/YsQIHYrUOWj7vD5DyuvcIW8h+m6kmVt3mX23AAsAAFE0MWNhYaG8+eabcuDAAfnyyy91KFILq2ZmZobnChG1y3Y05eGQLex66HpmXgIRACB6Z6pWVABSkzHC2t1l6YkucTqOawWYI0owJ2dkPTMAQCsJ7ScZLFg/FNruMiWe5TsAAK2MQISoGWF28AKvDbQQAQBaCYEIUTNLtSkw7J4FXgEArYRAhONSVh3OFiLmIQIAtC4CEY57lFm4u8woqgYAtBYCEaKvy4yiagBAKyMQ4cQWdk0JZyBi6Q4AQOsgEOG47G+qIcpMCkdR9Xcr3nubJn8EACCcCESIqnXMlDinXZoWvJdGX8i/PQAAhyAQ4Zip5VoCRdVh6DLTK943dZu5vWY0AgAgfAhEOGZVDR5xN/VlZYWhy0yJNydnpIUIANAKCEQ47jmIEl0OSYzzt+SEa3JGuswAAK2BQISoGnJ/8FxEjT66zAAA4UcgwjE7YBZUh6F+yKRanxRaiAAArYFAhKha2NWU0NQV10hRNQCgFRCIcPxdZmEqqFbMUWYUVQMAWgOBCMc9S3U4W4i+6zKjhggAEH4EIhx/C1FKKxRVs3oHAKAVEIhw3Mt25KSEfpbqg1uIGmghAgC0AgIRjtneKn+XWbvU8AciRpkBAFoDgQjHrNQMRGFsIUpoHohs/JoCAMKLTxocE6/PCBRV56aGPxCJ2MQenxy25wEAICYC0a5du+Saa66R7OxsSUxMlF69esmqVauCFhqdOnWqdOjQQR8fNmyYbN26Neh7lJWVyfjx4yUtLU0yMjJk4sSJUl1dHYGfJvbtr2kQnyFit6mJGcMXiBx2m8Q5/L+e9qS0sD0PAAD6syaaX4YDBw7IueeeKy6XS9566y3ZtGmT/OEPf5DMzMzAOTNmzJBnnnlGZs2aJcuXL5fk5GQZMWKE1NfXB85RYWjjxo2yaNEiWbBggSxdulSuv/76CP1UbaN+KCs5XoeWcDLXSXMkEogAAOHllCj26KOPSufOneWll14KPNa1a9eg1qGnnnpK7rvvPrn88sv1Y3/9618lLy9P5s+fL+PGjZOioiJZuHChrFy5UgYMGKDPefbZZ2X06NHy+OOPS35+fgR+stjVGgXVzYfeV9SJ2BNTw/5cAABri+oWotdff12HmKuuukpyc3OlX79+8sILLwSOb9u2TYqLi3U3mSk9PV0GDRoky5Yt0/fVXnWTmWFIUefb7XbdonQkDQ0NUllZGbShtQORv4XInpjOSw8AsG4g+vrrr+W5556T7t27y9tvvy033HCD3HrrrfLyyy/r4yoMKapFqDl13zym9ipMNed0OiUrKytwzuFMnz5dhytzUy1VaJ0RZgcPvafLDABg6UDk8/nkzDPPlN///ve6dUjV/UyaNEnXC4XblClTpKKiIrDt3Lkz7M8ZSy1EuWmtF4joMgMAWDoQqZFjp512WtBjPXv2lB07dujb7du31/uSkpKgc9R985jal5aWBh33eDx65Jl5zuHEx8frUWnNN4jsrW69FiKzy8zBKDMAgJUDkRphtmXLlqDHvvjiCyksLAwUWKtQs3jx4sBxVeujaoMGDx6s76t9eXm5rF69OnDOkiVLdOuTqjVC9NYQBVqIEiiqBgBYeJTZHXfcIeecc47uMvvxj38sK1askOeff15vis1mk9tvv11++9vf6jojFZDuv/9+PXJszJgxgRalkSNHBrra3G633HzzzXoEGiPMjt2+Vh5lpjAPEQDA0oFo4MCBMm/ePF3PM23aNB141DB7Na+Q6e6775aamhpdX6Rags477zw9zD4hISFwzpw5c3QIGjp0qB5dNnbsWD13EaK8hYh5iAAArSSqA5FyySWX6O1IVCuRCktqOxI1omzu3LlhukLrqGv0SlWDJ+zLdhw67J76LQCAhWuIEJ2tQ6orKyXe2Yo1RCl6DTUAAMKFQIQW21tdH+guUy1zrdVCZLM7pNZNIAIAhA+BCMdeP9QKQ+4VtVaa0+YPQlWNvlZ5TgCANRGIcOyTMqZ+V7AebnFNv6GVDQQiAED4EIgQlSPMTPEOfwsRgQgAEE4EIhz7OmatGIjMFiK6zAAA4UQgQnS3ENmbaojoMgMAhBGBCFG5jpmpaW5GWogAAGFFIEJUtxDFNbUQUUMEAAgnAhFaxOP1SUmlfx6iDumtN8osvqmFqKKeUWYAgPAhEKFFiivrRU0WHeewS04rdpklNLUQlVNDBAAIIwIRWmR3eVPrUEaC2O3hn6XalNDUQlRe72215wQAWA+BCC2yq7xW7ztmJLbqK2bOQ1Re7xPDYPkOAEB4EIjQIrsO1Ol9fisHooSm31CPT6Sy3tOqzw0AsA4CEVpkV1OXWWu3EDnsIr6GGn17X9OwfwAAQo1AhBbZVV4XkUCkeGvK9X5f07B/AABCjUCEFtltBqLMyAUic2JIAABCjUCE76WKmc0aosi0EB3Qe1qIAADhQiDC9zpQ65Y6t3/Ye/tWnJTxkC6z6sZWf24AgDUQiNDi7jK1ZEeCq2lioFbkqzUDEV1mAIDwIBDhe30boSH3B3eZmWupAQAQagQitLiFqFOEAxEtRACAcCEQoeVD7iMwwkyhhggAEG4EIrR8luoIFFQfPOye5TsAAOFAIML32l1hthAlReTVMouqGz0+lu8AAIQFgQjHsI5ZZFqIDE+jJLls+jZ1RACAcCAQ4ajq3V7ZX+Of/6dTRmRaiJT0eP+vKpMzAgDCgUCEFhVUJ8c5JC3RGbFXK6Np2XsmZwQAhAOBCEf19V7/SvNdcpLFZvN3W0VCRoJ/Qsi9VfURuwYAQNtFIMJRbS2t0vvuuSkRfaVoIQIAhBOBCEf1ZUm13p8c4UAUqCFi+Q4AQBgQiHBUW0vNQJQa0VfK7DIjEAEAwoFAhCPy+Qz5aq8/EHXPi44uM9YzAwCEA4EIR52QsbbRKy6HTQqzIjfkXiEQAQDCKaYC0SOPPKJHOt1+++2Bx+rr6+Wmm26S7OxsSUlJkbFjx0pJSUnQ1+3YsUMuvvhiSUpKktzcXLnrrrvE4/FE4CeIze6yrjnJ4nRE9lclO9HfZVZS1SBenxHRawEAtD0xE4hWrlwpf/7zn6V3795Bj99xxx3yxhtvyKuvvioffPCB7N69W6688srAca/Xq8NQY2OjfPLJJ/Lyyy/L7NmzZerUqRH4KWLLV02BqHuE64fMFiKn3abDUClD7wEAVgxE1dXVMn78eHnhhRckMzMz8HhFRYX85S9/kSeeeEIuuugi6d+/v7z00ks6+Hz66af6nHfeeUc2bdokf//736Vv374yatQoefjhh2XmzJk6JOHItjaNMOsW4RFmisNuk/ZNi8vubposEgAASwUi1SWmWnmGDRsW9Pjq1avF7XYHPd6jRw8pKCiQZcuW6ftq36tXL8nLywucM2LECKmsrJSNGzce8TkbGhr0Oc03q4mWOYhM+RmJer+7nMkZAQChFbm1GFrolVdekTVr1ugus4MVFxdLXFycZGRkBD2uwo86Zp7TPAyZx81jRzJ9+nR56KGHxKoMw2g25D5KAhEtRAAAK7YQ7dy5U2677TaZM2eOJCS07krrU6ZM0V1y5qauxUrU8Paqeo/Ybf6i6uhqIaLLDABgoUCkusRKS0vlzDPPFKfTqTdVOP3MM8/o26qlR9UBlZeXB32dGmXWvn17fVvtDx51Zt43zzmc+Ph4SUtLC9qsxGwdKsxOlgSXf4RXtASiXXSZAQCsFIiGDh0q69evl7Vr1wa2AQMG6AJr87bL5ZLFixcHvmbLli16mP3gwYP1fbVX30MFK9OiRYt0wDnttNMi8nPFgi3F/vqhbu2io7tM6dgUiPZU0EIEALBQDVFqaqqcccYZQY8lJyfrOYfMxydOnCiTJ0+WrKwsHXJuueUWHYLOPvtsfXz48OE6+Fx77bUyY8YMXTd033336UJt1QqEw1u944De9+6UHjUvUYcMRpkBACwYiFriySefFLvdridkVCPD1AiyP/3pT4HjDodDFixYIDfccIMOSipQTZgwQaZNmxbR6472guqV28r07YFdsiRamF1mB2rdUtvokaS4mP/1BQBEiZj7RHn//feD7qtiazWnkNqOpLCwUN58881WuLq2YWdZnZRWNeglO/oVBI/gi6S0BJekxjulqsGjh95Hy+g3AEDsi+oaIkTGiu3+1qFeHdOjpqD64FYi6ogAAKFEIMIhAt1lXaOnu8yUTx0RACAMCEQ4xMpvmgJRYfQFog4MvQcAhAGBCEH2VTfI13tr9O0BXb5bNy7aht4zOSMAIJQIRAiyqql+6NS8VMlIiou6V4cuMwBAOBCIEGTldv/8QwO7Rl/rkJKfbhZVs8ArACB0CEQImn9ocZF/WZNBXbOj8pX5bvmOOn29AACEAoEIAWt2lMv2/bWS6HLIRT1yo/KVyUtLEJtNpNHjk33VjZG+HABAG0EgQsC8z77V+5FntJfk+OicszPOaZcOaf4lPHaU+Yu/AQA4UQQiaKrFZcG6Pfr2Ff06RvWr0q1phuqvSglEAIDQIBBBe29LqZTXuiU3NV7OPTknql+Vbu38gejLvdWRvhQAQBtBIII2b80uvb+8b7447LaoflXMNcy+LCUQAQBCg0AE2b6vRhZv9o8uu6Jfp6h/RQItRAQiAECIEIggj7y1WdxeQ4ac0k5Oy0+L+lfEbCHaeaBW6t3eSF8OAKANiM6hRDguO3bskH379h3T12za2ygLN+4X1Ut2RVdD1qxZIzk5OVJQUBC170JOSpykJ7qkos6tlxmJhRAHAIhuBKI2FIZ69OwpdbW1x/BVNmn/sz9IfIdTpGLNW3LlIzP1o4lJSbK5qChqQ5HNZtOtRKu/OaALqwlEAIATRSBqI1TLkApD4+95TPIKurXoa4oq7LKpwilOmyHjLhkqCZcPlZIdX8mcR+/S3y9aA5FycrumQEQdEQAgBAhEbYwKQ526n/695329t1o27fDPO/SDHnlycn66xJJuucl6/xWBCAAQAhRVW9D+6gZ5e6N/VFnvjulyRoyFoeaF1V8xFxEAIAQIRBajCpHnrd0ljV6f5Gck6JFlsejkdql6//W+GvH6WOQVAHBiCEQWUlXvltfWfCs1DV7JSo6TS3pF/ySMR9IxM1HinXa95MjOsmMpJAcA4FAEIouobfTIvM92SWW9Rw9Zv7JfR0mMc0isUkHuJCZoBACECIHIAtTkhSoMHah1S0q8U4ehaF3N/lickuevI9q0pzLSlwIAiHEEojbO4/XJf9buln3VjZIU55Arz+woaYkuaQvOLMjU+1XfHIj0pQAAYhyBqA0zDEOWbC6V4sp6SXDa5Yp+HSUzKU7aiv6F/kC05psDFFYDAE4IgagN+/zbCikqrhKbTWR0rw6SkxIvbUmP9qm6C7C6wSNbiqsifTkAgBhGIGqj9lTUydKte/Xt807Okc5ZSdLWOB126VeQoW+v+qYs0pcDAIhhBKI2Wje0aFOJGIbIKbkp0q+zPzS0RQMKs/R+5XbqiAAAx49A1AZ9uq1MjyhLjnPIhT1y9WKobdXALv46otXbaSECABy/2B97jSBlDTZZU+JvLbmoR64kuI5vrqGioqITemVP9Otbqm9Bhp6TaHdFvewqr5OOGYmt8rwAgLaFQNSm2GTtAYeohSxObZ8amLjwWFSW+euOrrnmmpBcUXV1tYRTUpxTTs9Pk3XfVsiq7WXSsW/HsD4fAKBtIhC1Icmn/UAONNolzmGX80/OOa7vUVftn+Tw4l/+Rk7t3f+4r6VoxQfy1stPS319vbRGHZEKRCu3l8nlBCIAwHEgELURDR5DMn4wQd8e0CXzhGeizs4vlE7dTz/ury/Z8ZW0lnO6ZcuLH2+TdzaWyEOXnRGz67MBACKHouo24vUvqsWZ1k6SHEabHlV2OENOaafXZyutapBPv94f6csBAMQgAlEbUF7bKP/ZUqNvn5Hh1fPzWEmc064nnlTmf7Yr0pcDAIhBUf/JOX36dBk4cKCkpqZKbm6ujBkzRrZs2RJ0jqpTuemmmyQ7O1tSUlJk7NixUlJSEnTOjh075OKLL5akpCT9fe666y7xeDzSFrz40TapdRvSWLpNOiX5xIrG9M3X+4UbivVitgAAtKlA9MEHH+iw8+mnn8qiRYvE7XbL8OHDpabG3yKi3HHHHfLGG2/Iq6++qs/fvXu3XHnllYHjXq9Xh6HGxkb55JNP5OWXX5bZs2fL1KlTJdZV1LrlpY+3+29//A+9TIcVDeySJfnpCVLV4NHrtwEA0KaKqhcuXBh0XwUZ1cKzevVqGTJkiFRUVMhf/vIXmTt3rlx00UX6nJdeekl69uypQ9TZZ58t77zzjmzatEneffddycvLk759+8rDDz8s99xzjzz44IMSF3fogqcNDQ16M1VW+kdfRZu/fLxNh4DCdKd888UysSq73SaX9e0osz74SnebmV1oAAC0iRaig6kApGRl+ZdsUMFItRoNGzYscE6PHj2koKBAli3zBwS179Wrlw5DphEjRuiQs3HjxiN21aWnpwe2zp07S7SprHfLSx9t07evOk3NOaRmILKuK/r55yBavLlUviwN7/xHAIC2JaYCkc/nk9tvv13OPfdcOeOMM/RjxcXFuoUnIyN4ZJUKP+qYeU7zMGQeN48dzpQpU3T4MredO3dKtPnH8h26dah7boqc3SlBrE5NRjmsZ654fYbMWLg50pcDAIghMRWIVC3Rhg0b5JVXXgn7c8XHx0taWlrQFk0aPb5A7dCkISeJ3arFQwe5d1QPUdMQvbOpRFZsY30zAEAbC0Q333yzLFiwQN577z3p1KlT4PH27dvrYuny8vKg89UoM3XMPOfgUWfmffOcWLNg3W4prqyXdqnxcnnTCCuInJybKj8ZWKBfit+/WSSGYe1uRABAGwlE6gNNhaF58+bJkiVLpGvXrkHH+/fvLy6XSxYvXhx4TA3LV8PsBw8erO+r/fr166W09LvRR2rEmmr1Oe200yTWqNfkhQ/9tUPXndNF4p3Ht4BrW3XHsO6SFOeQtTvLZdYHX0f6cgAAMcAZC91kagTZf/7zHz0XkVnzowqdExMT9X7ixIkyefJkXWitQs4tt9yiQ5AaYaaoYfoq+Fx77bUyY8YM/T3uu+8+/b1V11is+fjL/VK0p1ISXQ4ZP8jfGtLWFRUVHdP5156RLH9eU6lriVw1JXJmhwTJycnRxfYAAMRcIHruuef0/oILLgh6XA2tv+666/TtJ598Uux2u56QUQ2VVyPI/vSnPwXOdTgcurvthhtu0EEpOTlZJkyYINOmTZNY9PyH/laPnwzsLBlJh04Z0JZUlu3V+2uuueaYvzZr5C2S2meEPLRopxT/7U5x1R+QzUVFhCIAQOwFopbUgCQkJMjMmTP1diSFhYXy5ptvSqxTLUNLv9irC4d/cW5w92FbVFftn//p4l/+Rk7t3f+YvtZriCwt8UmZpEiXSTPlm/+7Rfbt20cgAgDEXiBCsP9rqh0adUYHKchOsszLk51fKJ26n37MXze2i0de+2yX7K8Wybv6EdlW7pYzw3KFAIBYFvVF1fhOcUW9vP65f/HS/zm/7bcOhUJSnFPGntlJMuJ84khKl/vf2y8ff7kv0pcFAIgyBKIYMvuT7eL2GnJWlyzpV5AZ6cuJGar4/Pxcj9TvWK8Xwb3upRXy2ppvI31ZAIAoQiCKEVX1bpnz6Tf6Nq1Dxy7OLlLyr6lyTucEHSon/+tzmfnel8xTBADQCEQx4pUVO/UyHd3aJcuwnsHLkKCFvG6ZfHaGXD/kJH33sbe3yG/mbxCP18dLCAAWRyCKAWqZjhc/9hdTqw9ztbI7jo9a4uTXo3vKg5eeJmq1k7nLd8gv/7Zaahs9vKQAYGEEohjwxue7ZU+Ff5mOMU0ruuPEXHduV3lufH+Jd9pl8eZSGff8p7K3qoGXFQAsikAUA/MwPb/UPxHjz89lmY5QGnlGe5k76WzJTHLJum8r5MrnPpav9laH9DkAALGBQBTl3v9ir2wpqZLkOLVMR2GkL6fN6V+YKf++4RwpyEqSnWV1Mva5T2TV9rJIXxYAoJURiKLc802Lk/70rAJJT3RF+nLapJPapchrN54jfTqlS3mtW8b/33JZuGFPpC8LANCKCERRbN235bLs6/3itNvkF+cxEWM45aTEyz+uP1uG9siVBo9PbpizRl5qKmQHALR9LN0Rxf7cVDt0WZ98yc9IjPTltAlFRUVHPf6rM+zidCfJ21/VykNvbJLPtnwjP+uTqkenmXJyclgPDQDaGAJRFNixY4dedLS5PVUeeWu9f6X389o1yJo1a07og97qKsv8r+U111zTovPTBv1IMi+4Tl7/okb+8Z+3ZP+bT4nhadTHEpOSZHNREaEIANoQAlEUhKEePXtKXW1t0OPZo++QlF5DpfarlTJ26EMt/n7V1YySOpy66kq9v/iXv5FTe/dv2XtT45FV+x2S3HOIdOpznpyd45Gq3V/JnEfv0gG2oKCgxe8LACC6EYgiTH2wqjA0/p7HJK+gm36syi3yzh5/AfUl5/SVzAtf+97vU7TiA3nr5aelvr4+7Nccy7LzC6VT99NbdG4nEelcVitvbtgjBxpF3t+bKANzTw77NQIAWh+BKEqoMGR+UC/cUKxikZyUkyy9zshv0deX7PgqzFdoTZ2zkuSnAwtkwbo9sre6QT4sdUrqmZewBhoAtDGMMosy+6sb9LxDyqCTsiJ9OVD1RIkuuWpAJzk1L1UMsUnWD38lTy8vl8p6N68PALQRBKIo8/FX+/VeLeKam5oQ6ctBE5fDLiNOz5NeGR4xfF5ZuqNeRj31oXz6tf/9AgDENgJRFNlRVivb9tWIWrv13G45kb4cHMRms8kpaT4pmXuP5CU7ZFd5nfz0hU9l+ptF0uDx8noBQAwjEEUJwxBZutU/NLx3xwzJTI6L9CXhCBp2bZYnhufIuIGd9fum5ou6/I8fy+c7y3nNACBGEYiixPYau+yvbtSrr1M7FP0SXXZ5ZGxveeFnAyQ7OU42F1fJmD99LPfP3yAVtdQWAUCsIRBFAXtShqwvd+jbg7pmSYLLfxvR74en5cnbdwyRK/p11K1Ff/v0G/nB4+/Jix9tk0aPL9KXBwBoIQJRFMgadr24fTZplxovfTplRPpycBzroD35k74y938GSffcFL1A7LQFm2ToE+/L3OU7qC8CgBjAPEQRtmJXvZ4J2SaGDOuZK3ZVUY2od7ilUtSYwN8PSZHF2xzyysYq2VlWJ7+et14eX7hRhp+UJMNOSpKsxO9a/1gTDQCiB4Eogirq3PL8mgp9W41eYph921kTzeaMl5Q+IyRt0Fgpk2x5ZWO1/GN9hdTvWCe1mz+W+u2fiaOhQv79//6fdOjQ4Xuft9FryN4ar+yt9Uqp3ntkX61Pqhp94hGHOFxx4rLbJd5ll4ykOF3XVJidJN1zU+Xk3BTJS4vXo+QAAIdHIIogVWPSJcMlJbu+lp6d2kXyUhCmNdG8hsiuWo98XW2X/Q0OSezST2+Kp7pMfjF7lXgqisVTtV8Mr1vE5xWbK0Hs8UniSM0RZ3qef0vN/p5nCl4L72Cp8U45pX2qnFmQIf0LM+XMgkzJTWOeKwAwEYgiSNUM/ea8TDnr13eLo+9L/Fa20TXRCkXkHBEpr22UL0ur5et9NVJcUSfOlCy9tZTTZkiS05Akh/j3TkPqy0tl2X/+Krffcot06NhRtyRVNfjkQL1Pdld75NtKjxRXe6WqwSOrvzmgtxc+3Ka/X26yQ05vFyd98uKld16cnNwpjwVrAVgWgSjCVDeGr9bfbYa2TXVlDeiSpbcVi9+QeX9/UYb84teSnt9Vahu94vEZ4vMZelbsOKddkuMdkp7g0kuHqC3BaT+k22vT8hJZvPkj+f1NHx35iR1OcWXmS1xeN4nv2FNvrnaFUlojUlpTJ+9tr9OnufetkGuGDpDR/U+Ss7pkSWIcox0BWAeBCIgAhxjSsGuTdHDWSt8TmJX8WLvwTG6fR/Y32GRvvV1K6m1S4baLK6eL/PPzfXpTgezsk7LlolPbydCeeXqRWwBoywhEgMW68Exdm93+cvNGmf3cUzLutgekqMyQ3RX1svSLvXp78I1NejqBi3rk6k3VIDkdzNgBoG0hEAGQBIdIbdFSuShlj9w4oIfsqvLIqt0NsnpPgxTta5StpdV6U8uUJLts0q99vPTPT5Az28dLanxwOGI6AQCxiEAE4KjTCdjjkyWh65mS2G2gJJ7UX2qS0uWjnfV6M3xeadi9Weq+WiV1X64Q975vJDEpSTYXFVGgDSCmEIgAtLgWSS1PUtbolj11dimuU7VHDknodLreMn8wQVzikYov18gf398mIwbGS6+O6Xo0pZXnQGrweGVvVYOed6yq3iNen6FfRzVnVGqCU7KS4iz/GgHRgEAE4JhqkTqLSJ+m25X1btm+r0a27auRnQfqxO1zStLJZ8m/NlXLvzat0uckxzmkU2aSdM5K1PsO6QmSmRyng4DaZya5JCXeqUe1JcU5xRHFs7Xv2LFD9u3bd9hjDR5Ddla6ZXu5R76pcMs35R499cGBOp8Y3/N91aLOnTITdfF658wk6dYuWbrnperaLasHSqC1WCoQzZw5Ux577DEpLi6WPn36yLPPPitnnXVWpC8LiFlpCS7p3SlDbx6vTzYUbZZ5c16UoT/6uezzJelapJpGr2wpqdJbS7jsKiDYdDhKTYyTOKdD4hw2PfLNnJJA7wO3g4+px9XXqtm6dfBqtqUlOI87XKgw1KNnT6mrbxBnZgeJyy4QV45/i8vtKs7MfLHZDz9VgeFxi7e+SoyGGt3NqK4hv1OhNBp2Pdt4g8cnX+2t0dvBUuJs0inNKZ3TnNIx1Sk5SQ69BExWol2SbB5JSTryBJuGYYhaY9jtM/QcVW5v89uGNPr8jyUkp0q7du30VA/JcU7/Pt6pX0cVaCmiD3+gPhbU6YWHZQLRP//5T5k8ebLMmjVLBg0aJE899ZSMGDFCtmzZIrm5uZG+PCDmqQ/NuJpSqVr9hsxf/YZ+zOaME0dau+9m3E7PFUdKtjgS08SutqQ0fdsWlyA2m784260+wBsNqW50S2m1O7TXaLfp+aAykly6ZSo90d9Cpe6rxw9unap3e2V/daPsr2mQ7cVlknn1E5KX3VEMOXyoirMbku4yJCPOv091GZLsNCTOruYcS1HxRr7esErmP/d72W1+kd0hTjUreUb7wObK6qSDlrpd3eiQzfvcejscn7texOfTQUvNdG4YPrE5nGJzxInN6TpiSAtWJiLfHLUFS7Xi+UOSI3D7uwDl1I8lxfuPJbr8rX3qflLz202tgGqvAqyVBAJ1bdOs8g6nOBLT/X8DSeliT0wXR5L6m0j/7vFE836aruUzDPUe+0SMrZKVuUnSEuODAr/6R4BabFq1Kga2lHhJT3SxTmYLWCYQPfHEEzJp0iT5+c9/ru+rYPTf//5XXnzxRbn33nsjfXlAm3C88yIZhkd8hoinadv+xUZ5799/FZsrXmwOl/6AVx8g5m3/3nXIY/q+3amXPlGBy/9B07SPT9KTX+6rbtDb8XBld9LdX6pVqvkHkfrQUR9E6oP++1qgSnZ81eLXyGt4pdrtk0q3TW9VHpF6r03qPGrziWFziN3V8iVY7GKIwyaicp+5NzwNUrZ7u3TrdrJeNqbOY0i9xyf1Hn/rkqJasBo8Khg2Sqg47SIJTpvEO2x6ryYeVS2DCQ6bpCcnSHZGalCAMkOUzqw2m97bpGmvN3XbJj7DP8GpnuhUtZB5m/Y+Q9dvNd/Mc8z75u3vHhN9u6q6WrcMHs7B7/bBb7/6Huo1LauskYyfPCqdczuK1+YSj3HsLZU2cQVul9d5pbyuVnaU1bbgtbYFglJ2Spw/2Oqg6pDEODPAOiTBZdfByXxd1ev53Wvrv+//mQzxNr3Oar9vf5lUVlb7XzdDvWbmpl5L9XvctPd9d9t/v/n5hjhd8fJ/E8+NWLe5JQJRY2OjrF69WqZMmRJ4zG63y7Bhw2TZsmWH/ZqGhga9mSoq/LNJV1b6/4cfKtXV1Xr/7daN0lD3/b/Y3/c/2eLtX8hXycc/iR7fh9cnFL8/7saG4/59Vv9T8pbtksbirTJw1E+kU9fux3k16tP8gOzZvlw+ffNfuiVGt0olpIo9MUUcap+QErhvj0859NPM5xVvbbmeTd6rt0q54NKr5ORu3f2netT/HEQ8FSLFYXqNVNxJsIvkxqummu8e37L6I1n82t/lgmtulcLup+mf1jBsOrDZbf7go9qG/B9kKgwd+uMp24s+k01/+8Phr9/mEFtcotji4sXuStIteXZ13xWv9/7bap+gw5T/3ESxu+L8950JYlehVh13qu+R4A+36v/LTduR7ZG2RrWS1jWoXxpPU8Dxtx7GOQxRs1eoFkZ1P77ZYy5zbzf0e7tvz7fy5stPy7RpD0u7jl2kutEnlQ0+qW7wSWWjT8rrvVLR4JOKep/eVzca+nXeXVcju/2DSaPa+g0b5KQuasGj0DE/t1UX8lEZFrBr1y71KhiffPJJ0ON33XWXcdZZZx32ax544AH9NWy8BvwO8DvA7wC/A/wOSMy/Bjt37jxqVrBEC9HxUK1JqubI5PP5pKysTLKzs4+pKFMl086dO8vOnTslLS0tTFeLUOI9iy28X7GH9yy2VMb455hqGaqqqpL8/PyjnmeJQKQq8h0Oh5SUlAQ9ru63b9/+sF8THx+vt+YyMjKO+xrUL1Es/iJZGe9ZbOH9ij28Z7ElLYY/x9LT07/3HEuU+cfFxUn//v1l8eLFQS0+6v7gwYMjem0AACDyLNFCpKjurwkTJsiAAQP03ENq2H1NTU1g1BkAALAuywSin/zkJ7J3716ZOnWqnpixb9++snDhQsnLywvr86putwceeOCQ7jdEL96z2ML7FXt4z2JLvEU+x2yqsjrSFwEAABBJlqghAgAAOBoCEQAAsDwCEQAAsDwCEQAAsDwCUZjNnDlTunTpIgkJCTJo0CBZsWKF5X/pImH69OkycOBASU1NldzcXBkzZoxs2bIl6Jz6+nq56aab9GzkKSkpMnbs2EMm81QrVl988cWSlJSkv89dd90lHo9/XSKEzyOPPKJniL/99tt5v6LUrl275JprrtF/P4mJidKrVy9ZtWpV4Lgav6NG+Xbo0EEfV2tJbt26Neh7qNUAxo8fryf/UxPhTpw4MbDeI0LL6/XK/fffL127dtXvR7du3eThhx8OWu/Lcu9ZKNcMQ7BXXnnFiIuLM1588UVj48aNxqRJk4yMjAyjpKSEl6qVjRgxwnjppZeMDRs2GGvXrjVGjx5tFBQUGNXV1YFzfvWrXxmdO3c2Fi9ebKxatco4++yzjXPOOSdw3OPxGGeccYYxbNgw47PPPjPefPNNIycnx5gyZQrvZxitWLHC6NKli9G7d2/jtttu4/2KQmVlZUZhYaFx3XXXGcuXLze+/vpr4+233za+/PLLwDmPPPKIkZ6ebsyfP9/4/PPPjcsuu8zo2rWrUVdXFzhn5MiRRp8+fYxPP/3U+PDDD42TTz7Z+OlPfxqhn6pt+93vfmdkZ2cbCxYsMLZt22a8+uqrRkpKivH0009b9j0jEIWRWjj2pptuCtz3er1Gfn6+MX369HA+LVqgtLRUL/b3wQcf6Pvl5eWGy+XS/1MwFRUV6XOWLVum76sAZLfbjeLi4sA5zz33nJGWlmY0NDTwuodBVVWV0b17d2PRokXGD37wg0Ag4v2KLvfcc49x3nnnHfG4z+cz2rdvbzz22GOBx9R7GB8fb/zjH//Q9zdt2qT/3lauXBk456233jJsNpteoBuhdfHFFxu/+MUvgh678sorjfHjx1v2PaPLLEwaGxtl9erVuonRZLfb9f1ly5aF62nRQhUVFXqflZWl9+q9crvdQe9Xjx49pKCgIPB+qb3qBmg+meeIESP0wocbN27ktQ8D1YWpuiibvy+8X9Hn9ddf16sAXHXVVboruV+/fvLCCy8Ejm/btk1PiNv8fVRrS6kyguZ/X6rLRX0fkzpf/X9z+fLlrfwTtX3nnHOOXr7qiy++0Pc///xz+eijj2TUqFGWfc8sM1N1a9u3b5/uoz14Jmx1f/PmzRG7LvjXsVO1KOeee66cccYZ+iVRf/hqzbuDF/BV75c6Zp5zuPfTPIbQeuWVV2TNmjWycuXKQ47xfkWXr7/+Wp577jm9RNKvf/1r/Z7deuut+m9KLZlk/n0c7u+n+d+XClPNOZ1O/Y8W/r5C795779X/mFP/8FOLn3u9Xvnd736n64HM98Nq7xmBCJZsddiwYYP+1xCi086dO+W2226TRYsW6QEJiP5/ZKhWgt///vf6vmohUn9js2bN0oEI0edf//qXzJkzR+bOnSunn366rF27Vv9DMT8/37LvGV1mYZKTk6NT98GjlNT99u3bh+tp8T1uvvlmWbBggbz33nvSqVOnwOPqPVHdnOXl5Ud8v9T+cO+neQyho7owS0tL5cwzz9T/4lTbBx98IM8884y+rf6VyvsVPdQopNNOOy3osZ49e+pRmc3/Po72/0O1V+95c2oEpxrFxN9X6KkRsqqVaNy4cboU4Nprr5U77rhDj8i16ntGIAoT1VTcv39/3Ufb/F9R6v7gwYPD9bQ4AjWAQIWhefPmyZIlS/RQ0+bUe+VyuYLeLzUsX/0P3Xy/1H79+vVB/wNQLRhquOnBHwY4MUOHDtWvtfpXq7mpFgjVnG/e5v2KHqr7+eBpLFRtSmFhob6t/t7UB2Tzvy/VXaPqTJr/fal/kKgwbFJ/q+r/m6puBaFVW1ura32aczgc+vW27HsW6arutj7sXlXkz549W1fjX3/99XrYffNRSmgdN9xwgx4++v777xt79uwJbLW1tUHD7tVQ/CVLluhh94MHD9bbwcPuhw8frofuL1y40GjXrh3D7ltJ81FmvF/RNzWC0+nUQ7m3bt1qzJkzx0hKSjL+/ve/Bw3hVv//+89//mOsW7fOuPzyyw87hLtfv3566P5HH32kRxjG6hDuaDdhwgSjY8eOgWH3r732mp5G5O6777bse0YgCrNnn31Wf8iq+YjUMHw1VwNan8r+h9vU3EQm9Ud+4403GpmZmfp/5ldccYUOTc1t377dGDVqlJGYmKj/53HnnXcabrc7Aj+R9RwciHi/ossbb7yh/8Gg/hHYo0cP4/nnnw86roZx33///UZeXp4+Z+jQocaWLVuCztm/f7/+MFXz4ajpLH7+85/rqRcQepWVlfrvSX0+JSQkGCeddJLxm9/8JmgKEau9Zzb1n0i3UgEAAEQSNUQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAcATbt28Xm82m109T3n//fX3/4EWAAcQ+AhGAmHLdddfJmDFjIvLc55xzjuzZs0fS09Mj8vwAwscZxu8NAG1KXFycXgEcQNtDCxGAmHXBBRfIrbfeKnfffbdkZWXpsPLggw8GjqulGtX9goICiY+Pl/z8fH2+SXV/zZ8/P+h7ZmRkyOzZsw/7fAd3manz1Plvv/229OzZU1JSUmTkyJG6FQlAbCEQAYhpL7/8siQnJ8vy5ctlxowZMm3aNFm0aJE+9u9//1uefPJJ+fOf/yxbt27V4adXr14hff7a2lp5/PHH5W9/+5ssXbpUduzYIf/7v/8b0ucAEH50mQGIab1795YHHnhA3+7evbv88Y9/lMWLF8sPf/hDHU5Uq9GwYcPE5XLplqKzzjorpM/vdrtl1qxZ0q1bN33/5ptv1qEMQGyhhQhAzAei5jp06CClpaX69lVXXSV1dXVy0kknyaRJk2TevHni8XhC+vxJSUmBMHTw8wOIHQQiADFNtfw0p2p8fD6fvt25c2fZsmWL/OlPf5LExES58cYbZciQIbpVxzxX1Rk1Zx47kec/+HsCiH4EIgBtmgpCl156qTzzzDO6KHrZsmWyfv16faxdu3ZBBdCqzkjVBAGwHmqIALRZahSY1+uVQYMG6a6tv//97zogFRYW6uMXXXSRrjkaPHiwPu+ee+45pMUHgDXQQgSgzVJD4l944QU599xzda3Ru+++K2+88YZkZ2fr43/4wx90t9r5558vV199tR4dpoITAOuxGXR2AwAAi6OFCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAiNX9fwLW7XEKgB21AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_cols:\n",
    "    sns.histplot(df,x=i,bins=25,kde=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0cda5036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_cols:\n",
    "    sns.boxplot(df,x=i,y=\"Outcome\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ad9c4df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2768 entries, 0 to 2767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               2768 non-null   int64  \n",
      " 1   Glucose                   2768 non-null   float64\n",
      " 2   BloodPressure             2768 non-null   float64\n",
      " 3   SkinThickness             2768 non-null   float64\n",
      " 4   Insulin                   2768 non-null   float64\n",
      " 5   BMI                       2768 non-null   float64\n",
      " 6   DiabetesPedigreeFunction  2768 non-null   float64\n",
      " 7   Age                       2768 non-null   int64  \n",
      " 8   Outcome                   2768 non-null   int64  \n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 194.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c4f4632b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f087f060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2768, 9)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dbdeb286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Outcome\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "628e8b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,PowerTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split,RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87da5450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;skew&#x27;, PowerTransformer()), (&#x27;scaler&#x27;, StandardScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;skew&#x27;, ...), (&#x27;scaler&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>PowerTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"skew__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=method,-%7B%27yeo-johnson%27%2C%20%27box-cox%27%7D%2C%20default%3D%27yeo-johnson%27\">\n",
       "            method\n",
       "            <span class=\"param-doc-description\">method: {'yeo-johnson', 'box-cox'}, default='yeo-johnson'<br><br>The power transform method. Available methods are:<br><br>- 'yeo-johnson' [1]_, works with positive and negative values<br>- 'box-cox' [2]_, only works with strictly positive values</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;yeo-johnson&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('standardize',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=standardize,-bool%2C%20default%3DTrue\">\n",
       "            standardize\n",
       "            <span class=\"param-doc-description\">standardize: bool, default=True<br><br>Set to True to apply zero-mean, unit-variance normalization to the<br>transformed output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>Set to False to perform inplace computation during transformation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
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       "                element.innerHTML = originalHTML;\n",
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       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "Pipeline(steps=[('skew', PowerTransformer()), ('scaler', StandardScaler())])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline = Pipeline(steps=[\n",
    "    (\"skew\",PowerTransformer(method=\"yeo-johnson\")),\n",
    "    (\"scaler\",StandardScaler())\n",
    "])\n",
    "\n",
    "num_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "075e8981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-2.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-2.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;skew&#x27;, PowerTransformer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Pregnancies&#x27;, &#x27;Glucose&#x27;, &#x27;BloodPressure&#x27;,\n",
       "                                  &#x27;SkinThickness&#x27;, &#x27;Insulin&#x27;, &#x27;BMI&#x27;,\n",
       "                                  &#x27;DiabetesPedigreeFunction&#x27;, &#x27;Age&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
       "            transformers\n",
       "            <span class=\"param-doc-description\">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;num&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
       "            remainder\n",
       "            <span class=\"param-doc-description\">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
       "            sparse_threshold\n",
       "            <span class=\"param-doc-description\">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
       "            transformer_weights\n",
       "            <span class=\"param-doc-description\">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
       "            verbose_feature_names_out\n",
       "            <span class=\"param-doc-description\">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=\"{transformer_name}__{feature_name}\"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``\"{feature_name}__{transformer_name}\"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse\">\n",
       "            force_int_remainder_cols\n",
       "            <span class=\"param-doc-description\">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__\"><pre>[&#x27;Pregnancies&#x27;, &#x27;Glucose&#x27;, &#x27;BloodPressure&#x27;, &#x27;SkinThickness&#x27;, &#x27;Insulin&#x27;, &#x27;BMI&#x27;, &#x27;DiabetesPedigreeFunction&#x27;, &#x27;Age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>PowerTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__skew__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=method,-%7B%27yeo-johnson%27%2C%20%27box-cox%27%7D%2C%20default%3D%27yeo-johnson%27\">\n",
       "            method\n",
       "            <span class=\"param-doc-description\">method: {'yeo-johnson', 'box-cox'}, default='yeo-johnson'<br><br>The power transform method. Available methods are:<br><br>- 'yeo-johnson' [1]_, works with positive and negative values<br>- 'box-cox' [2]_, only works with strictly positive values</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;yeo-johnson&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('standardize',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=standardize,-bool%2C%20default%3DTrue\">\n",
       "            standardize\n",
       "            <span class=\"param-doc-description\">standardize: bool, default=True<br><br>Set to True to apply zero-mean, unit-variance normalization to the<br>transformed output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>Set to False to perform inplace computation during transformation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
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       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-2');</script></body>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('skew', PowerTransformer()),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['Pregnancies', 'Glucose', 'BloodPressure',\n",
       "                                  'SkinThickness', 'Insulin', 'BMI',\n",
       "                                  'DiabetesPedigreeFunction', 'Age'])])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing = ColumnTransformer(transformers=[\n",
    "    (\"num\",num_pipeline,num_cols)\n",
    "])\n",
    "\n",
    "preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d7f51edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Outcome\",axis=1)\n",
    "y = df[\"Outcome\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "78e94881",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pre = preprocessing.fit_transform(X_train)\n",
    "X_test_pre = preprocessing.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fbc26c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout,BatchNormalization,Input\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "63168f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Input(shape=(X_train_pre.shape[1],)),\n",
    "    Dense(128,activation=\"relu\"),\n",
    "    Dense(64,activation=\"relu\"),\n",
    "    Dense(32,activation=\"relu\"),\n",
    "    Dense(64,activation=\"relu\"),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.1),\n",
    "    Dense(32,activation=\"relu\"),\n",
    "    Dense(16,activation=\"relu\"),\n",
    "    Dense(8,activation=\"relu\"),\n",
    "    Dropout(0.1),\n",
    "    Dense(16,activation=\"relu\"),\n",
    "    Dense(8,activation=\"relu\"),\n",
    "    BatchNormalization(),\n",
    "    Dense(1,activation=\"sigmoid\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "59aa4cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5c88c309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 20ms/step - accuracy: 0.6247 - loss: 0.6198 - val_accuracy: 0.6227 - val_loss: 0.6589\n",
      "Epoch 2/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7543 - loss: 0.4951 - val_accuracy: 0.7112 - val_loss: 0.6464\n",
      "Epoch 3/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8053 - loss: 0.4267 - val_accuracy: 0.7852 - val_loss: 0.6282\n",
      "Epoch 4/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8288 - loss: 0.3847 - val_accuracy: 0.7798 - val_loss: 0.6234\n",
      "Epoch 5/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8519 - loss: 0.3503 - val_accuracy: 0.8159 - val_loss: 0.5882\n",
      "Epoch 6/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8771 - loss: 0.3024 - val_accuracy: 0.7563 - val_loss: 0.5641\n",
      "Epoch 7/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8911 - loss: 0.2847 - val_accuracy: 0.7455 - val_loss: 0.5478\n",
      "Epoch 8/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8925 - loss: 0.2757 - val_accuracy: 0.8087 - val_loss: 0.5041\n",
      "Epoch 9/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9115 - loss: 0.2323 - val_accuracy: 0.8375 - val_loss: 0.4399\n",
      "Epoch 10/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9151 - loss: 0.2274 - val_accuracy: 0.8502 - val_loss: 0.3952\n",
      "Epoch 11/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9273 - loss: 0.2017 - val_accuracy: 0.8646 - val_loss: 0.3750\n",
      "Epoch 12/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9282 - loss: 0.2126 - val_accuracy: 0.9097 - val_loss: 0.2952\n",
      "Epoch 13/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9381 - loss: 0.1742 - val_accuracy: 0.8899 - val_loss: 0.2877\n",
      "Epoch 14/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9517 - loss: 0.1500 - val_accuracy: 0.9061 - val_loss: 0.2721\n",
      "Epoch 15/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9535 - loss: 0.1280 - val_accuracy: 0.9350 - val_loss: 0.2217\n",
      "Epoch 16/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9548 - loss: 0.1338 - val_accuracy: 0.9495 - val_loss: 0.1908\n",
      "Epoch 17/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9485 - loss: 0.1497 - val_accuracy: 0.9116 - val_loss: 0.2385\n",
      "Epoch 18/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9584 - loss: 0.1208 - val_accuracy: 0.9386 - val_loss: 0.1745\n",
      "Epoch 19/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9584 - loss: 0.1207 - val_accuracy: 0.9260 - val_loss: 0.2595\n",
      "Epoch 20/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9503 - loss: 0.1389 - val_accuracy: 0.9404 - val_loss: 0.2265\n",
      "Epoch 21/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9630 - loss: 0.1150 - val_accuracy: 0.9278 - val_loss: 0.2068\n",
      "Epoch 22/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9566 - loss: 0.1240 - val_accuracy: 0.9296 - val_loss: 0.2036\n",
      "Epoch 23/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9463 - loss: 0.1592 - val_accuracy: 0.9242 - val_loss: 0.2321\n",
      "Epoch 24/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9661 - loss: 0.1033 - val_accuracy: 0.9477 - val_loss: 0.1935\n",
      "Epoch 25/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9761 - loss: 0.0917 - val_accuracy: 0.9477 - val_loss: 0.1960\n",
      "Epoch 26/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9612 - loss: 0.1051 - val_accuracy: 0.9368 - val_loss: 0.2047\n",
      "Epoch 27/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9634 - loss: 0.1191 - val_accuracy: 0.9404 - val_loss: 0.2274\n",
      "Epoch 28/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9747 - loss: 0.0840 - val_accuracy: 0.9531 - val_loss: 0.1816\n",
      "Epoch 29/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9675 - loss: 0.0991 - val_accuracy: 0.9458 - val_loss: 0.1868\n",
      "Epoch 30/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9657 - loss: 0.1052 - val_accuracy: 0.9314 - val_loss: 0.2208\n",
      "Epoch 31/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9715 - loss: 0.0871 - val_accuracy: 0.9585 - val_loss: 0.1748\n",
      "Epoch 32/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9711 - loss: 0.0884 - val_accuracy: 0.9477 - val_loss: 0.1459\n",
      "Epoch 33/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9819 - loss: 0.0599 - val_accuracy: 0.9639 - val_loss: 0.1371\n",
      "Epoch 34/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9788 - loss: 0.0756 - val_accuracy: 0.9386 - val_loss: 0.2067\n",
      "Epoch 35/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9779 - loss: 0.0692 - val_accuracy: 0.9386 - val_loss: 0.2196\n",
      "Epoch 36/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9697 - loss: 0.0945 - val_accuracy: 0.9422 - val_loss: 0.1737\n",
      "Epoch 37/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9797 - loss: 0.0706 - val_accuracy: 0.9440 - val_loss: 0.1671\n",
      "Epoch 38/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.0548 - val_accuracy: 0.9657 - val_loss: 0.1267\n",
      "Epoch 39/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9779 - loss: 0.0620 - val_accuracy: 0.9422 - val_loss: 0.1871\n",
      "Epoch 40/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9734 - loss: 0.0790 - val_accuracy: 0.9404 - val_loss: 0.1703\n",
      "Epoch 41/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9779 - loss: 0.0668 - val_accuracy: 0.9585 - val_loss: 0.1522\n",
      "Epoch 42/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9788 - loss: 0.0603 - val_accuracy: 0.9657 - val_loss: 0.1424\n",
      "Epoch 43/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9783 - loss: 0.0685 - val_accuracy: 0.9495 - val_loss: 0.1783\n",
      "Epoch 44/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9806 - loss: 0.0612 - val_accuracy: 0.9675 - val_loss: 0.1429\n",
      "Epoch 45/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0445 - val_accuracy: 0.9675 - val_loss: 0.1416\n",
      "Epoch 46/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9765 - loss: 0.0614 - val_accuracy: 0.9621 - val_loss: 0.1482\n",
      "Epoch 47/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9864 - loss: 0.0474 - val_accuracy: 0.9585 - val_loss: 0.1486\n",
      "Epoch 48/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9878 - loss: 0.0456 - val_accuracy: 0.9621 - val_loss: 0.1673\n",
      "Epoch 49/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9887 - loss: 0.0408 - val_accuracy: 0.9495 - val_loss: 0.1625\n",
      "Epoch 50/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0338 - val_accuracy: 0.9621 - val_loss: 0.1610\n",
      "Epoch 51/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9901 - loss: 0.0338 - val_accuracy: 0.9585 - val_loss: 0.1700\n",
      "Epoch 52/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9878 - loss: 0.0399 - val_accuracy: 0.9603 - val_loss: 0.1597\n",
      "Epoch 53/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9774 - loss: 0.0705 - val_accuracy: 0.9422 - val_loss: 0.1816\n",
      "Epoch 54/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9715 - loss: 0.0854 - val_accuracy: 0.9567 - val_loss: 0.1994\n",
      "Epoch 55/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9810 - loss: 0.0641 - val_accuracy: 0.9531 - val_loss: 0.1983\n",
      "Epoch 56/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9869 - loss: 0.0403 - val_accuracy: 0.9621 - val_loss: 0.1832\n",
      "Epoch 57/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9919 - loss: 0.0272 - val_accuracy: 0.9567 - val_loss: 0.1884\n",
      "Epoch 58/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9851 - loss: 0.0484 - val_accuracy: 0.9639 - val_loss: 0.1619\n",
      "Epoch 59/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9851 - loss: 0.0423 - val_accuracy: 0.9549 - val_loss: 0.1793\n",
      "Epoch 60/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9892 - loss: 0.0467 - val_accuracy: 0.9603 - val_loss: 0.1811\n",
      "Epoch 61/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9910 - loss: 0.0368 - val_accuracy: 0.9657 - val_loss: 0.1629\n",
      "Epoch 62/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9892 - loss: 0.0331 - val_accuracy: 0.9585 - val_loss: 0.1780\n",
      "Epoch 63/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9864 - loss: 0.0440 - val_accuracy: 0.9422 - val_loss: 0.2284\n",
      "Epoch 64/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9837 - loss: 0.0532 - val_accuracy: 0.9621 - val_loss: 0.1913\n",
      "Epoch 65/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9851 - loss: 0.0490 - val_accuracy: 0.9603 - val_loss: 0.1939\n",
      "Epoch 66/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9883 - loss: 0.0460 - val_accuracy: 0.9603 - val_loss: 0.1662\n",
      "Epoch 67/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9874 - loss: 0.0346 - val_accuracy: 0.9675 - val_loss: 0.1581\n",
      "Epoch 68/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9855 - loss: 0.0381 - val_accuracy: 0.9603 - val_loss: 0.1751\n",
      "Epoch 69/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9855 - loss: 0.0426 - val_accuracy: 0.9639 - val_loss: 0.1716\n",
      "Epoch 70/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9883 - loss: 0.0409 - val_accuracy: 0.9404 - val_loss: 0.2878\n",
      "Epoch 71/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9770 - loss: 0.0717 - val_accuracy: 0.9549 - val_loss: 0.1844\n",
      "Epoch 72/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9797 - loss: 0.0659 - val_accuracy: 0.9495 - val_loss: 0.2118\n",
      "Epoch 73/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9851 - loss: 0.0580 - val_accuracy: 0.9549 - val_loss: 0.1813\n",
      "Epoch 74/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9878 - loss: 0.0380 - val_accuracy: 0.9585 - val_loss: 0.1916\n",
      "Epoch 75/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9950 - loss: 0.0199 - val_accuracy: 0.9639 - val_loss: 0.1685\n",
      "Epoch 76/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9914 - loss: 0.0249 - val_accuracy: 0.9621 - val_loss: 0.1678\n",
      "Epoch 77/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9896 - loss: 0.0375 - val_accuracy: 0.9621 - val_loss: 0.1825\n",
      "Epoch 78/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 0.0309 - val_accuracy: 0.9657 - val_loss: 0.1679\n",
      "Epoch 79/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9919 - loss: 0.0285 - val_accuracy: 0.9549 - val_loss: 0.2090\n",
      "Epoch 80/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9905 - loss: 0.0283 - val_accuracy: 0.9639 - val_loss: 0.1987\n",
      "Epoch 81/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9932 - loss: 0.0257 - val_accuracy: 0.9675 - val_loss: 0.1904\n",
      "Epoch 82/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9928 - loss: 0.0287 - val_accuracy: 0.9675 - val_loss: 0.1759\n",
      "Epoch 83/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9914 - loss: 0.0269 - val_accuracy: 0.9657 - val_loss: 0.1668\n",
      "Epoch 84/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9923 - loss: 0.0279 - val_accuracy: 0.9639 - val_loss: 0.1617\n",
      "Epoch 85/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9887 - loss: 0.0354 - val_accuracy: 0.9675 - val_loss: 0.1762\n",
      "Epoch 86/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9864 - loss: 0.0508 - val_accuracy: 0.9657 - val_loss: 0.1567\n",
      "Epoch 87/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9878 - loss: 0.0344 - val_accuracy: 0.9567 - val_loss: 0.1939\n",
      "Epoch 88/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0326 - val_accuracy: 0.9567 - val_loss: 0.2138\n",
      "Epoch 89/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9914 - loss: 0.0275 - val_accuracy: 0.9549 - val_loss: 0.2385\n",
      "Epoch 90/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9855 - loss: 0.0393 - val_accuracy: 0.9458 - val_loss: 0.2482\n",
      "Epoch 91/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0383 - val_accuracy: 0.9621 - val_loss: 0.1847\n",
      "Epoch 92/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0423 - val_accuracy: 0.9206 - val_loss: 0.3280\n",
      "Epoch 93/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9887 - loss: 0.0354 - val_accuracy: 0.9567 - val_loss: 0.2089\n",
      "Epoch 94/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9837 - loss: 0.0488 - val_accuracy: 0.9513 - val_loss: 0.2328\n",
      "Epoch 95/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9919 - loss: 0.0272 - val_accuracy: 0.9639 - val_loss: 0.2093\n",
      "Epoch 96/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9878 - loss: 0.0389 - val_accuracy: 0.9657 - val_loss: 0.1915\n",
      "Epoch 97/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9919 - loss: 0.0283 - val_accuracy: 0.9621 - val_loss: 0.1961\n",
      "Epoch 98/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9923 - loss: 0.0272 - val_accuracy: 0.9404 - val_loss: 0.3179\n",
      "Epoch 99/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9752 - loss: 0.0790 - val_accuracy: 0.9477 - val_loss: 0.2185\n",
      "Epoch 100/100\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9901 - loss: 0.0392 - val_accuracy: 0.9675 - val_loss: 0.1687\n"
     ]
    }
   ],
   "source": [
    "his = model.fit(X_train_pre,y_train,validation_data=(X_test_pre,y_test),epochs=100,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "55499db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['columns.pkl']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "model.save(\"model.h5\")\n",
    "dump(preprocessing,\"preprocessing.pkl\")\n",
    "dump(X.columns.tolist(),\"columns.pkl\")"
   ]
  }
 ],
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