Project/Final_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dfeb8bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fbad44e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45b0cbaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5956cc05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e253e0",
   "metadata": {},
   "source": [
    " The Dataset\n",
    "\n",
    "This dataset is a collection of samples obtained from the National Institute of Diabetes and Digestive and Kidney Disease, which you can find @ NYU Classes/Assignments/Final project.\n",
    "\n",
    "\n",
    "The goal is to predict whether an individual has diabetes or not.\n",
    "The attributes are :\n",
    "\n",
    "    Pregnancies: Number of times pregnant\n",
    "\n",
    "    Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "\n",
    "    BloodPressure: Diastolic blood pressure (mm Hg)\n",
    "\n",
    "    SkinThickness: Triceps skin fold thickness (mm)\n",
    "\n",
    "    Insulin: 2-Hour serum insulin (mu U/ml)\n",
    "\n",
    "    BMI: Body mass index (weight in kg/(height in m)^2)\n",
    "\n",
    "    DiabetesPedigreeFunction: Diabetes pedigree function\n",
    "\n",
    "    Age: Age (years)\n",
    "    Outcome: Class variable (0 or 1) where 1 means they have diabetes.\n",
    "\n",
    "\n",
    "Things to do\n",
    "Load the PimaIndianDiabetes.csv dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a55e8d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data = pd.read_csv(\"PimaIndiansDiabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e164715e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98468662",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6ba86524",
   "metadata": {},
   "source": [
    "\n",
    "### Q1: Which variables are numeric and which are categorical? Explain why. (20pts)\n",
    "\n",
    "Make sure to convert the variables to the correct type.\n",
    "\n",
    "All of the variables are numeric except \"Outcome\" which is categorical because it is either 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "befbd244",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data.Outcome = diabetes_data.Outcome.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0473420f",
   "metadata": {},
   "source": [
    "\n",
    "### Q2: Which of the variables ( on their own ) are most helpful in predicting the outcome? (20pts)\n",
    "\n",
    "    For each variable draw an appropriate graph ( boxplot or violinplot for numerical values and barplot for categorical values). Explain your answer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0ddfaeab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='Pregnancies'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.Pregnancies, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "73b0df9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='Glucose'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.Glucose, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70708801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='BloodPressure'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UIUm7sG3bNj7zmc9w+eWX09XVVXU5s95Uj+Pc9Oo/wHPAY8XraYuIjoi4NyK+WbSPiYi7ImIoIv4hIvbfm+NLmh1WrVrF+vXrueaaa6oupS3sNhQi4qSI+G5EfD0i3hER9wP3A09GRP9e9r0UeHBc+8+AL2ZmL/BvwHl7eXxJ+7ht27axZs0aMpM1a9awbdu2qkua9aa6T+FLwGep3aj2beD8zPx3wG8AfzrdTiNiAfAB4KqiHcCpwNeKXVYBH57u8SXNDqtWrWLnzp0A7Nixw9FCE0wVCp2Z+c+ZeQPwRGbeCZCZD+1lv/8T+ENgZ9HuAp7OzNGivRnonuyDEXFBRAxGxODIyMheliGpld16662Mjtb+WxgdHWXt2rUVVzT7TRUKO8e93v6q93Y5K2l3IuKDwNbMXDedz2fmyszsy8y+efPmTecQkvYRixYtorOzNh+ms7OT008/veKKZr+pZh/9akQ8Q20RvLnFa4r266bZ57uBD0XE+4tjHAIsAw6NiM5itLAAGJ7m8fcprbBePbTOmvXtsF696jcwMMCaNWsA6Ojo4Oyzz664otlvqtlHHZl5SGa+PjM7i9dj7WmtfZSZn87MBZnZA5wJfDszPwp8B/hIsdsAcON0jq/pmTt3ruvWq+V0dXXR399PRNDf3++U1Caod5mLZrgYuD4irgDuBb5ScT1N4W/F0u4NDAywceNGRwlNEpnTujTQEvr6+nJw0HX5JGlPRMS6zOyb7L16l86WJLUBQ0GSVDIUJEklQ0GSVDIUJEklQ0GSVDIUBNRWo7zoootchVItx+9mcxkKAlyzXq3L72ZzGQpyzXq1LL+bzWcoyDXr1bL8bjafoSDXrFfL8rvZfIaCXLNeLcvvZvMZCmJgYID99qt9FVyzXq3E72bzGQpyzXq1LL+bzddKz1NQhVyzXq3K72Zz+TwFSWozPk9BU/KuUUlgKKjgXaOSwFAQ3jUq6RWGgrxrVFLJUJB3jUoqGQryrlFJJUNB3jUqqdT0UIiIoyPiOxHx44h4ICKWFtsPj4i1EfFI8fOwZtfWrrxrVNKYKkYKo8B/y8xjgZOACyPiWOAS4LbMXAjcVrTVJAMDAxx33HGOEqQ21/RlLjJzC7CleP1sRDwIdANnAKcUu60Cvgtc3Oz62lVXVxfLly+vugxJFav0mkJE9ADvAO4CjioCA+AJ4Kiq6pKkdlVZKETEwcA/Av8lM58Z/17WFmSadFGmiLggIgYjYnBkZKQJlUpS+6gkFCJiDrVAuDYzv15sfjIi5hfvzwe2TvbZzFyZmX2Z2Tdv3rzmFCxJbaKK2UcBfAV4MDO/MO6tm4CB4vUAcGOza5OkdlfF8xTeDXwMWB8RPyy2XQp8DlgdEecBm4DFFdQmSW2titlH3wdiF2+f1sxaJEkTeUezJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKnUWXUBag2LFy9m69atzJ8/n+uuu67qcqTSOeecw8aNG+nt7eWqq66qupxZr+VGChHRHxEPR8RQRFxSdT3tYuvWrQBs2bKl4kqkiTZu3AjA0NBQtYW0iZYKhYjoAL4MvA84FjgrIo6ttqrZb/HixRPaZ511VkWVSBOdc845E9rnn39+NYW0kZYKBeBEYCgzN2TmS8D1wBkV1zTrjY0SxjhaUKsYGyWMcbTQeK0WCt3A4+Pam4ttpYi4ICIGI2JwZGSkqcVJ0mzXaqEwpcxcmZl9mdk3b968qsuRpFml1UJhGDh6XHtBsU0NdOSRR05oz58/v6JKpIl6enomtHt7e6sppI20Wij8K7AwIo6JiP2BM4GbKq5p1lu9evWEtlNS1SquvvrqCW2npDZeS4VCZo4CfwB8C3gQWJ2ZD1RbVXsYGy04SlCrGRstOEpojsjMqmuYtr6+vhwcHKy6DEnap0TEuszsm+y9lhopSJKqZShIkkqGgiSpZChIkkr79IXmiBgBNlVdxyxyBPCzqouQJuF3c2a9MTMnvft3nw4FzayIGNzVjASpSn43m8fTR5KkkqEgSSoZChpvZdUFSLvgd7NJvKYgSSo5UpAklQwFSVLJUBAR0R8RD0fEUERcUnU90piI+GpEbI2I+6uupV0YCm0uIjqALwPvA44FzoqIY6utSipdDfRXXUQ7MRR0IjCUmRsy8yXgeuCMimuSAMjM24Gnqq6jnRgK6gYeH9feXGyT1IYMBUlSyVDQMHD0uPaCYpukNmQo6F+BhRFxTETsD5wJ3FRxTZIqYii0ucwcBf4A+BbwILA6Mx+otiqpJiKuA34AvCUiNkfEeVXXNNu5zIUkqeRIQZJUMhQkSSVDQZJUMhQkSSVDQZJUMhTU9iJiQUTcGBGPRMSjEbGsuGdjd5+5tFn1Sc1kKKitRUQAXwe+kZkLgTcDBwN/MsVHDQXNSoaC2t2pwAuZ+bcAmbkD+BTw8Yj4ZER8aWzHiPhmRJwSEZ8D5kbEDyPi2uK9syPivoj4UUT8XbGtJyK+XWy/LSLeUGy/OiKujIg7I2JDccyvRsSDEXH1uP7eGxE/iIh7IuKGiDi4af8qaluGgtrd24B14zdk5jPAY0DnZB/IzEuA7Zn59sz8aES8DbgMODUzfxVYWuy6AliVmccD1wLLxx3mMOBkagF0E/DFopbjIuLtEXFEccxFmXkCMAj815n4C0u7M+mXXtIeORW4ITN/BpCZY+v/nwz8bvH674A/H/eZf8rMjIj1wJOZuR4gIh4AeqgtTHgs8C+1M1zsT225B6mhDAW1ux8DHxm/ISIOAd4APM3E0fTrZrDfF4ufO8e9Hmt3AjuAtZl51gz2KU3J00dqd7cBB0bE2VA+nvTz1B4DuQF4e0TsFxFHU3tK3ZiXI2JO8frbwO9FRFdxjMOL7f+X2qqzAB8F7tiDuu4E3h0RvcUxD4qIN+/pX07aU4aC2lrWVoT8HWr/qT8C/AR4gdrson8BfkptNLEcuGfcR1cC90XEtcWqsn8CfC8ifgR8odhnCXBuRNwHfIxXrjXUU9cIcA5wXfH5HwBvne7fU6qXq6RKkkqOFCRJJUNBklQyFCRJJUNBklQyFCRJJUNBklQyFCRJpf8PDmh2ara+9GUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.BloodPressure, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b9033f15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='SkinThickness'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.SkinThickness, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1cac0e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='Insulin'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.Insulin, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cc147c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='BMI'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.BMI, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13862b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='DiabetesPedigreeFunction'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pin9H0o3Ad3GvoVHt3HPPHVCeMWNGRZGYWdXy9hq6B/hf1NYeeBb4g4j4apGBWbE8DbWZ9ak319BpQAfwn4C1wJUR8USeC0u6A3g/8HxE/NchXhcwHzgf2A5cHhGrdy/80WnhwoV0d3dXHQbNzc309vZy2GGHccMNN1QWR1tbG3PmzKns/mapq1cj+Bzw50ALcAvw2d249l3AcO0N5wFTsm028PnduLaNgKamJpqamrxesVni6nUVaYqI5dn+VyXlnp4yIlZKah3mlAuBRRERwA8kHS7pqIh4Nu89RqtG+et33rx5AMyfP7/iSMysSvUSweGSLtpVOSLu24t7TwKe7lfemB17UyKQNJtarYHJkyfvxS3NzGyweongYeCCXZSD2nQThYuIDmrPKpg6dWqUcU8zs1TUm4b6igLvvQk4tl/5mOyYmZmVKO9cQ/MkjVPNFyWtlnTOXt57MTAru+bpwEspPB8wM2s0eecV+HBEzJd0LrUeRJcBdwMP7uoNku4FzgLGS9oIXA/sBxARXwCWUus62k2t+2iRtQ8zM9uFvIlA2dfzqfX0eTwbB7BLEXFpndcD+GjO+5uZWUHyTjGxStKD1BLBtyUdiucaMjPbJ+StEVxJbcH69RGxXVILbsoxM9sn5K0RBHAcMDcrHwwcWEhEZmZWKi9MY2aWuLxNQ6dFxMmSfgK1hWkkeWEaM7N9gBemMTNL3N4sTHNTYVGZmVlpcjUNRcQ9klYB76M2puAPImJdoZGZmVkpciUCSXdHxGXAz4Y4ZmZmo1jepqHj+xey5wXvGvlwzMysbMMmAknXSNoKvEPSFklbs/LzwP2lRGhmZoUaNhFExE0RcSjw9xExLiIOzbaWiMi9WpmZmTWuvE1DfynpQ5L+CkDSsZJOLTAuMzMrSd5E8DlqI4s/kJW34ZHFZmb7BI8sNjNLnEcWm5klLm+NoG9k8cRsZPHFwHWFRWWWoIULF9Ld3V11GA2h799h3rx5FUfSGNra2pgzZ05h19+TkcXgkcVmI667u5snH/8Jkw/ZUXUoldv/9VpjxatPdVUcSfV+ta258HvkrREAHAT0NQ+NLSYcs7RNPmQH1568peowrIF8evW4wu+R6xmBpE8CncARwHjgTkluGjIz2wfkrRF8EDgxIl4BkPQZ4FHgbwqKy8zMSpK319AzDFya8gBg08iHY2ZmZRu2RiBpIbVnAi8Bj0tanpWnAz8qPjwzMytavaahvkf2q6h1H+3z/wqJxszMSjdsIoiIzrICMTOzauRdmGYKtaUpj6Pfs4KI+N2C4jIzs5LkfVh8J/B5oBd4L7AI+FJRQZmZWXnyJoKxEfEQoIh4KiL+Gvj94sIyM7Oy5B1H8KqkJuBJSVdR6zp6SHFhmZlZWfLWCOZRm2JiLrW1ii8D2osKyszMypN30rkfZ7vbgCuKC6d4nuHxDZ7hcaCiZ3g0a1T1BpT9Y0R8TNISsrUI+ouImYVFVpDu7m4e/ek6dhx0RNWhVK7ptdp/6ar1z1UcSfWat79YdQhmlalXI7g7+/oPRQdSph0HHcHLbz+/6jCsgYz92dKqQzCrzLDPCCJiVfb1YeAJ4ImIeLhvq3dxSTMk/VxSt6RPDPH65ZI2S3o02/5oT78RMzPbM3UfFkv6a0kvAD8H/i374P5kjvc1U1vg/jxqA9EulXTcEKd+JSJOyrYv7mb8Zma2l4ZNBJI+DpwJnBIRR0TEW4DTgDMlXV3n2qcC3RGxPiJeA74MXDgSQZuZ2cipVyO4DLg0In7ZdyAi1gMfAmbVee8k4Ol+5Y3ZscH+UNIaSV+TdOxQF5I0W1KXpK7NmzfXua2Zme2Oeolgv4h4YfDBiNgM7DcC918CtEbEO4Dl1FZBe5OI6IiIqRExdcKECSNwWzMz61MvEby2h69BbfRx/7/wj2HQYjYR0RMRr2bFL1IbrGZmZiWq1330RElDraQtBq5YNpQfA1MkvY1aArgE+MCAi0hHRcSzWXEmsK5+yGZmNpLqrUfQvKcXjojebF6ibwPNwB0R8bikG4CuiFgMzJU0k9qspi8Cl+/p/cxGu02bNvEfW5v59OpxVYdiDeSprc0cvKnYlYHzTjq3RyJiKbB00LFP9tu/BrimyBjMzGx4hSYCM8tv0qRJvNr7LNeePFRrrKXq06vHccCkoTpcjpy8s4+amdk+yonAzCxxTgRmZolzIjAzS5wTgZlZ4pwIzMwSl1z30U2bNtG8/SUvRGIDNG/vYdOm3qrDMKuEawRmZolLrkYwadIk/v3VMV6q0gYY+7OlTJo0seowzCrhGoGZWeKcCMzMEudEYGaWuOSeEZg1sl9t8zTUAM9tr/2NOvGgnRVHUr1fbWtmSsH3cCIwaxBtbW1Vh9AwXuvuBuCAt/rfZArF/2w4EZg1iDlz5lQdQsOYN28eAPPnz684kjQkmQiat7/oAWVA0yu1ee93HuimiObtLwLuPmppSi4RuPr9hu7urQC0/a4/AGGifzYsWcklAle/3+Dqt5mBu4+amSXPicDMLHFOBGZmiXMiMDNLnBOBmVninAjMzBLnRGBmljgnAjOzxDkRmJklLrmRxWY2vIULF9Kdzf5Zlb77941+r1JbW9s+PyOBE0EFGuEXDRrnly2FXzTbPWPHjq06hKQ4ESTMv2w2FCfl9DgRVMC/aGbWSPyw2MwscYUmAkkzJP1cUrekTwzx+gGSvpK9/kNJrUXGY2Zmb1ZYIpDUDHwOOA84DrhU0nGDTrsS+HVEtAGfBf62qHjMzGxoRdYITgW6I2J9RLwGfBm4cNA5FwKd2f7XgPdJUoExmZnZIEUmgknA0/3KG7NjQ54TEb3AS0DL4AtJmi2pS1LX5s2bCwrXzCxNo+JhcUR0RMTUiJg6YcKEqsMxM9unFJkINgHH9isfkx0b8hxJY4DDgJ4CYzIzs0GKTAQ/BqZIepuk/YFLgMWDzlkMtGf7FwPfiYgoMCYzMxtERX7uSjof+EegGbgjIm6UdAPQFRGLJR0I3A28E3gRuCQi1te55mbgqcKCTs944IWqgzAbgn82R9ZbI2LItvVCE4E1PkldETG16jjMBvPPZnlGxcNiMzMrjhOBmVninAiso+oAzHbBP5sl8TMCM7PEuUZgZpY4JwIzs8Q5ESSq3hThZlWRdIek5yX9tOpYUuFEkKCcU4SbVeUuYEbVQaTEiSBNeaYIN6tERKykNtOAlcSJIE15pgg3s0Q4EZiZJc6JIE15pgg3s0Q4EaQpzxThZpYIJ4IEZcuCXgV8G1gH/HNEPF5tVGY1ku4Fvg/8Z0kbJV1ZdUz7Ok8xYWaWONcIzMwS50RgZpY4JwIzs8Q5EZiZJc6JwMwscU4EliRJx0i6X9KTkn4haX42pmK491xbVnxmZXIisORIEnAf8I2ImAL8HnAIcGOdtzoR2D7JicBSdDbwSkTcCRARO4CrgQ9L+lNJt/adKOmbks6S9BlgrKRHJd2TvTZL0hpJj0m6OzvWKuk72fGHJE3Ojt8l6fOSfiBpfXbNOyStk3RXv/udI+n7klZL+qqkQ0r7V7FkORFYio4HVvU/EBFbgF8BY4Z6Q0R8Ang5Ik6KiA9KOh64Djg7Ik4E5mWnLgQ6I+IdwD3Agn6XeQtwBrWksxj4bBbLCZJOkjQ+u+a0iDgZ6AI+PhLfsNlwhvyhN7O6zga+GhEvAERE3/z5ZwAXZft3A3/X7z1LIiIkrQWei4i1AJIeB1qpTf53HPCvtdYr9qc21YJZoZwILEVPABf3PyBpHDAZ+A0Da8oHjuB9X82+7uy331ceA+wAlkfEpSN4T7O63DRkKXoIOEjSLPjt0p03U1sicT1wkqQmScdSW82tz+uS9sv2vwP8D0kt2TWOyI5/j9psrgAfBB7Zjbh+AJwpqS275sGSfm93vzmz3eVEYMmJ2kyL/53aB/mTwL8Br1DrFfSvwC+p1RoWAKv7vbUDWCPpnmy21huBhyU9BtySnTMHuELSGuAy3nh2kCeuzcDlwL3Z+78PvH1Pv0+zvDz7qJlZ4lwjMDNLnBOBmVninAjMzBLnRGBmljgnAjOzxDkRmJklzonAzCxx/x/JkLiNLOzgUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.DiabetesPedigreeFunction, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e9b8e355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='Age'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=diabetes_data.Age, x=diabetes_data.Outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d53b151f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outcome=diabetes_data[\"Outcome\"].value_counts()\n",
    "outcome.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ded10ed",
   "metadata": {},
   "source": [
    "\n",
    "### Q3: How do we fix missing values? (20pts)\n",
    "\n",
    "    Notice that some of the values are missing and replaced with 0. For example a blood pressure of 0 doesn’t make sense and it can have an impact on the models. So how do we identify which 0 reoresents the value 0 and which represents NA? Explain your answer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "579044e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   Pregnancies               768 non-null    int64   \n",
      " 1   Glucose                   768 non-null    int64   \n",
      " 2   BloodPressure             768 non-null    int64   \n",
      " 3   SkinThickness             768 non-null    int64   \n",
      " 4   Insulin                   768 non-null    int64   \n",
      " 5   BMI                       768 non-null    float64 \n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64 \n",
      " 7   Age                       768 non-null    int64   \n",
      " 8   Outcome                   768 non-null    category\n",
      "dtypes: category(1), float64(2), int64(6)\n",
      "memory usage: 49.0 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(diabetes_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dc6fb671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(diabetes_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8591a33",
   "metadata": {},
   "source": [
    "Here we can see that the columns in the diabetes data are non-null and does not have any nulls. This is because the null values are replaced with 0 in some values. So to fix it we have to identify and see what kind of value type could be considered could be zero or zero doesn't make sense for it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7ca857",
   "metadata": {},
   "source": [
    "Now to make a judgement, if you see the columns Glucose, Blood Pressure, Skin Thickness, Insulin and BMI, 0 does not make sense for them! If we see 0 in pregnancies, it does make sense that they had some kind of complications. DiabetesPedigreeFunction column does not have a zero, so we can leave that for judgement. And Outcome could be 0 or 1, so its okay.\n",
    "\n",
    "To make the machine observe the columns as null, I will convert those specific columns from 0 to NA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "219e72b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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": [
    "diabetes_data1 = diabetes_data.copy()\n",
    "diabetes_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "783abd5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data1[[\"Glucose\", \"BloodPressure\", \"SkinThickness\", \"Insulin\", \"BMI\"]]=diabetes_data1[[\"Glucose\", \"BloodPressure\", \"SkinThickness\", \"Insulin\", \"BMI\"]].replace(0, np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4faedcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(diabetes_data1.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8ec04a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Glucose</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6    148.0           72.0           35.0      NaN  33.6   \n",
       "1              1     85.0           66.0           29.0      NaN  26.6   \n",
       "2              8    183.0           64.0            NaN      NaN  23.3   \n",
       "3              1     89.0           66.0           23.0     94.0  28.1   \n",
       "4              0    137.0           40.0           35.0    168.0  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10    101.0           76.0           48.0    180.0  32.9   \n",
       "764            2    122.0           70.0           27.0      NaN  36.8   \n",
       "765            5    121.0           72.0           23.0    112.0  26.2   \n",
       "766            1    126.0           60.0            NaN      NaN  30.1   \n",
       "767            1     93.0           70.0           31.0      NaN  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age Outcome  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  \n",
       "..                        ...  ...     ...  \n",
       "763                     0.171   63       0  \n",
       "764                     0.340   27       0  \n",
       "765                     0.245   30       0  \n",
       "766                     0.349   47       1  \n",
       "767                     0.315   23       0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9c96a79f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.0000\n",
       "Glucose                     117.0000\n",
       "BloodPressure                72.0000\n",
       "SkinThickness                29.0000\n",
       "Insulin                     125.0000\n",
       "BMI                          32.3000\n",
       "DiabetesPedigreeFunction      0.3725\n",
       "Age                          29.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data1.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8f4086f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121.6867627785059\n",
      "72.40518417462484\n",
      "29.153419593345657\n",
      "155.5482233502538\n",
      "32.457463672391015\n"
     ]
    }
   ],
   "source": [
    "print(diabetes_data1.Glucose.mean())\n",
    "print(diabetes_data1.BloodPressure.mean())\n",
    "print(diabetes_data1.SkinThickness.mean())\n",
    "print(diabetes_data1.Insulin.mean())\n",
    "print(diabetes_data1.BMI.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b6672e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b25d13c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data2 = diabetes_data1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "259633bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = diabetes_data2.dropna()\n",
    "test_data = test_data.sample(154, random_state=32)\n",
    "\n",
    "\n",
    "whole_index = diabetes_data2.index\n",
    "test_index = test_data.index\n",
    "train_index = whole_index.difference(test_index)\n",
    "\n",
    "train_data = diabetes_data2.loc[train_index,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "36244567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b726d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.000\n",
       "Glucose                     117.000\n",
       "BloodPressure                72.000\n",
       "SkinThickness                30.000\n",
       "Insulin                     125.000\n",
       "BMI                          32.400\n",
       "DiabetesPedigreeFunction      0.353\n",
       "Age                          30.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "77c65acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[(train_data['Glucose'].isnull()), 'Glucose'] = 117\n",
    "train_data.loc[(train_data['BloodPressure'].isnull()), 'BloodPressure'] = 72.00\n",
    "train_data.loc[(train_data['SkinThickness'].isnull()), 'SkinThickness'] = 30.00\n",
    "train_data.loc[(train_data['Insulin'].isnull()), 'Insulin'] = 125.00\n",
    "train_data.loc[(train_data['BMI'].isnull()), 'BMI'] = 32.4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "046a2531",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data2.loc[(diabetes_data2['Glucose'].isnull()), 'Glucose'] = 117\n",
    "diabetes_data2.loc[(diabetes_data2['BloodPressure'].isnull()), 'BloodPressure'] = 72\n",
    "diabetes_data2.loc[(diabetes_data2['SkinThickness'].isnull()), 'SkinThickness'] = 30\n",
    "diabetes_data2.loc[(diabetes_data2['Insulin'].isnull()), 'Insulin'] = 125\n",
    "diabetes_data2.loc[(diabetes_data2['BMI'].isnull()), 'BMI'] = 32.40\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "49d66f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1affbdf",
   "metadata": {},
   "source": [
    "Now I have marked all the NAs in the dataset, so I can go over and fix the nulls by replacing with the median values of each column discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d143228",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a68f8765",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "37ff8e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.array(test_data[[\"Pregnancies\", \"Glucose\", \"BloodPressure\", \"SkinThickness\", \"Insulin\", \"BMI\", \"DiabetesPedigreeFunction\",\"Age\"]])\n",
    "y_test = np.array(test_data[\"Outcome\"])\n",
    "\n",
    "x_train = np.array(train_data[[\"Pregnancies\", \"Glucose\", \"BloodPressure\", \"SkinThickness\", \"Insulin\", \"BMI\", \"DiabetesPedigreeFunction\",\"Age\"]])\n",
    "y_train = np.array(train_data[\"Outcome\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d40cd319",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "28732f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from matplotlib import pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "3238ce6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_knn1 = KNeighborsClassifier(n_neighbors=89).fit(x_train, y_train)\n",
    "\n",
    "predicted1 = data_knn1.predict(x_test)\n",
    "\n",
    "predicted_proba1 = data_knn1.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "158343ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[95  7]\n",
      " [31 21]]\n",
      "accuracy: 0.7532467532467533\n",
      "recall: 0.40384615384615385\n",
      "precision: 0.75\n"
     ]
    }
   ],
   "source": [
    "con_mat1 = confusion_matrix(y_test, predicted1)\n",
    "print(con_mat1)\n",
    "\n",
    "print(\"accuracy:\",accuracy_score(y_test, predicted1))\n",
    "print(\"recall:\", recall_score(y_test, predicted1))\n",
    "print(\"precision:\",precision_score(y_test,predicted1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "8deb529b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC of the model with k-value of  is:  0.8346530920060332\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, threshold = roc_curve(y_test, predicted_proba1[:,1])\n",
    "\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "print(\"AUC of the model with k-value of  is: \", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9fb8bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d891d27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from collections import OrderedDict\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "95691322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# supress warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "# examine different number of trees from 1 to 100\n",
    "min_estimators = 5\n",
    "max_estimators = 100\n",
    "\n",
    "# results list\n",
    "n_trees = []\n",
    "oob_errors = []\n",
    "\n",
    "# iterate from n_estimators = 5 to n_estimators = 50\n",
    "for i in range(min_estimators, max_estimators+1):\n",
    "    clf = RandomForestClassifier(n_estimators=i, oob_score=True, random_state=32)\n",
    "    clf.fit(x_train, y_train)\n",
    "    n_trees.append(i)\n",
    "    oob_errors.append(1 - clf.oob_score_)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "plt.plot(n_trees, oob_errors, '-o')\n",
    "plt.ylabel('Out-of-bag error')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "71f35550",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of bag error: 0.250814332247557\n"
     ]
    }
   ],
   "source": [
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "data_RF1 = RandomForestClassifier(n_estimators=39, oob_score=True, random_state=32)\n",
    "data_RF1.fit(x_train, y_train)\n",
    "print(\"Out of bag error:\" ,1 - data_RF1.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "25d47214",
   "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>importances</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.257664</td>\n",
       "      <td>Glucose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.158899</td>\n",
       "      <td>BMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.142901</td>\n",
       "      <td>DiabetesPedigreeFunction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.122378</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.095443</td>\n",
       "      <td>BloodPressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.077555</td>\n",
       "      <td>Pregnancies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.076790</td>\n",
       "      <td>SkinThickness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.068372</td>\n",
       "      <td>Insulin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   importances                   feature\n",
       "1     0.257664                   Glucose\n",
       "5     0.158899                       BMI\n",
       "6     0.142901  DiabetesPedigreeFunction\n",
       "7     0.122378                       Age\n",
       "2     0.095443             BloodPressure\n",
       "0     0.077555               Pregnancies\n",
       "3     0.076790             SkinThickness\n",
       "4     0.068372                   Insulin"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_imp = {\"importances\": data_RF1.feature_importances_,\n",
    "        \"feature\": diabetes_data.columns[:-1]}\n",
    "data_plt = pd.DataFrame(data_imp)\n",
    "data_plt = data_plt.sort_values(by=['importances'], ascending=False)\n",
    "data_plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "510a562e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='importances', ylabel='feature'>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uAA7v2LDMzMysNSr5O/S3JfUEGiRdRnahXFtuGWtmZmYdpJLE/JlU78vAW8BWwHEdGZSZmZm1zloTerraXcDgiPhWRJyTluCtRLpYsEHSPElzJe2TyuskLaxSH/dLqk/bz0laIGm+pKmSPlSNPszMrGuq5Cr3TwANwN3p9fAmN5qxzDsRMTwihgHfAL5Xgz4PjIg9yO63/x+lO5SpyakRSZWcujEzsw5UyX/4FwGjgb8DREQDsG2HRVQMGwB/a1ooqZekG9LM+jFJB66lvLekX0t6XNJvyP7KoJzpwA5pNWCppJuAhcBWks5Lf3Y4X9K3Urt9Jf0hrSYslHRCKr9U0uJU9wepbJKksSVjWJ5+j5H0YPpyt1hSD0kTSvo6vUrvpZmZVaCSmdXKiFgmqbQsmqvcjfWW1AD0AgYDB5Wp8yUgImJ3SUOBqZJ2bKH8TODtiNhZ0h40/9jaI4EFaXsIcGpEPCLp0PR6NNlpk8mS9gc2AV6KiCMAJA2QNBA4BhgaESFpwwrGPALYLSKelTQeWBYRoyStD8yQNDUini09INUbD7BR/00q6MLMzCpRyQx9kaRPAT0kDUnPR3+og+PqihqX3IeS/VnfTWryLQjYD/gFQEQsIbsT344tlO9fUj6fD95y9770JWID/rXE/3xEPJK2D00/j5F9GRhKluAXAB+V9H1JH4mIZcAy4F3gp5KOBd6uYMwzSxL2ocApKZ5HgYGpr/eJiIkRUR8R9f36DKigCzMzq0RLD2f5eUR8Bnga2BVYAfwKmAJcXJvwuqaIeFjSILKZcEc6MCJea3yRZtVvlewX8L2I+EnTAyWNAD4OfEfStIj4tqTRwMHAWLK/ajgIWEX64pfOyfcsaaZpX2dFxJRqDMzMzFqnpRn6SEmbAyeQPZzlMLJZ2A+BPjWIrctKy+Y9gNeb7HoQODnV2RHYGljaQvl0sifeIWk3YI9WhjIF+JykfqmNLSRtmj7XtyPiF8AEYESqMyAi7gLOBoalNp4DRqbto4D1WujrTEnrNY5DUt9WxmtmZm3U0jn0a4FpwHZkV1E3Etk59O06MK6uqPEcOmTv0akRsbrJqvvVwDWSFpDNfMdFxApJzZVfA9wg6XHgcWBOawKKiKmSdgYeTnEsBz4N7ABMkLQGWEl2rr4/8DtJvVL856Rmrkvl88j+0uEtyrseqAPmplMNrwJHtyZeMzNrO0W0fH2bpGsi4swaxWPdyNYfGhJfP/lHeYdhVebnoZt1LElzIqK+aXklN5ZxMjczM+vkfE92MzOzAnBCNzMzKwAndDMzswJwQjczMysAP1TDcrPplgN8RbSZWZV4hm5mZlYATuhmZmYF4IRuZmZWAE7oZmZmBeCL4iw3Lz/7NJd8emzeYXRbF/zitrxDMLMq8gzdzMysAJzQzczMCsAJ3czMrACc0M3MzArACd3MzKwAnNDNzMwKwAndzMysAJzQa0zSakkNkhZKulVSn7xjqoSkoySdn3ccZmZWnhN67b0TEcMjYjfgPeCM0p2SOuXNfiJickRcmnccZmZWnhN6vh4EdpA0RtKDkiYDiyX1kDRB0ixJ8yWdDiBpHUlXS1oi6R5Jd0kam/Y9J+lbkuZKWiBpaCofLelhSY9JekjSTql8nKQ7JN0t6UlJlzUGJenw1M48SdNK6l+ZtjeRdHuKb5akfVP5AWn1oSH117+Wb6aZWXfWKWeD3UGaiX8MuDsVjQB2i4hnJY0HlkXEKEnrAzMkTQVGAnXALsCmwOPAz0qafS0iRkj6InAu8HlgCfCRiFgl6RDgu8Bxqf5wYE9gBbBU0hXAu8B1wP4plo3LhP9j4L8i4k+StgamADunPr8UETMk9UttNR33eGA8wIA+vVv3ppmZWbOc0Guvt6SGtP0g8FNgH2BmRDybyg8F9micfQMDgCHAfsCtEbEG+Iuk+5q0fUf6PQc4tuTYGyUNAQJYr6T+tIhYBiBpMbANsBEwvTGWiHijzBgOAXaR1Ph6g5TAZwA/knQzcEdEvND0wIiYCEwE2GLgRlGmbTMzawMn9Np7JyKGlxakxPhWaRFwVkRMaVLv42tpe0X6vZp/fbYXA/dFxDGS6oD7y9RveszarAN8OCKazsAvlfQH4ONkqwqHRcSSCts0M7N28Dn0zmkKcKak9QAk7SipL9kM+Lh0Ln0zYEwFbQ0AXkzb4yqo/wiwv6RtU9/lltynAmc1vpA0PP3ePiIWRMT3gVnA0Ar6MzOzKnBC75yuBxYDcyUtBH5CNnu+HXgh7fsFMBdYtpa2LgO+J+kxKpiBR8SrZOe475A0D7ilTLV/A+rTBXuL+deV+l9Nf443H1gJ/HFt/ZmZWXUowqcxuxJJ/SJiuaSBwExg34j4S95xtcUWAzeKL37s4LzD6Lb8PHSzrknSnIiob1ruc+hdz52SNgR6Ahd31WRuZmbV5YTexUTEmLxjMDOzzsfn0M3MzArACd3MzKwAnNDNzMwKwOfQLTeDt93eV1qbmVWJZ+hmZmYF4IRuZmZWAE7oZmZmBeCEbmZmVgC+KM5y8+7Lb/L4JffmHUa3tPMFB+UdgplVmWfoZmZmBeCEbmZmVgBO6GZmZgXghG5mZlYATuhmZmYF4IRuZmZWAE7oZmZmBeCEXkWSLpC0SNJ8SQ2S9pL0nKRBZeo+tJa2fpPaeErSsrTdIGmfFto8StL5LbRZJ2lh20ZnZmadmW8sUyWS9gaOBEZExIqUcHs2Vz8i9mmpvYg4JrU7Bjg3Io4s6au5YyYDk1sbu5mZdX2eoVfPYOC1iFgBEBGvRcRLjTsl9Zb0R0lfSK+Xp99jJN0v6TZJSyTdrOYy9vudJWmupAWShqa2xkm6Mm1vlmb589LP+75ASNpO0mOSRqXj7pB0t6QnJV1WUu9QSQ+nvm6V1C+VXyppcVqN+EEqO17SwtTf9Pa8mWZm1jpO6NUzFdhK0hOSrpZ0QMm+fsDvgV9FxHVljt0T+CqwC7AdsG8F/b0WESOAa4Bzy+y/HHggIoYBI4BFjTsk7QTcDoyLiFmpeDhwArA7cIKkrdIqwzeBQ1Jfs4FzJA0EjgF2jYg9gO+kNi4EDkt9HlUuaEnjJc2WNPuNt/5ewTDNzKwSTuhVEhHLgZHAeOBV4BZJ49Lu3wE3RMRNzRw+MyJeiIg1QANQV0GXd6Tfc5qpfxBZsiciVkfEslS+SYrn5IiYV1J/WkQsi4h3gcXANsCHyb5kzJDUAJyaypcB7wI/lXQs8HZqYwYwKa1C9CgXdERMjIj6iKjfuO+GFQzTzMwq4XPoVRQRq4H7gfslLSBLgJAlusMl/TIiosyhK0q2V1PZ59J4TKX1Gy0D/gzsR5a4W4pBwD0RcVLTRiSNBg4GxgJfBg6KiDMk7QUcAcyRNDIiXm9FbGZm1kaeoVeJpJ0kDSkpGg48n7YvBP4GXFXDkKYBZ6bYekgakMrfI1suP0XSp9bSxiPAvpJ2SO30lbRjOo8+ICLuAs4GhqX920fEoxFxIdkqxVZVH5WZmZXlhF49/YAbGy8UI1uqvqhk/1eA3qUXnHWwrwAHppWCOSkeACLiLbIr8s+WVPZcd6r3KjAO+FUa08PAUKA/cGcq+xNwTjpkQrpIbyHwEDDvg62amVlHUPkVYLOOt9sWO8WtX7wm7zC6JT8P3azrkjQnIuqblnuGbmZmVgBO6GZmZgXghG5mZlYATuhmZmYF4IRuZmZWAL6xjOWm1+D+vtrazKxKPEM3MzMrACd0MzOzAnBCNzMzKwAndDMzswLwRXGWm5deeomLLroo7zAKw++lWffmGbqZmVkBOKGbmZkVgBO6mZlZATihm5mZFYATupmZWQE4oZuZmRWAE7qZmVkBOKEXnKTlVW6vTtLCtF0v6fJqtm9mZm3jG8tYm0XEbGB23nGYmZln6N2GpDGS7pd0m6Qlkm6WpLTvUkmLJc2X9INUNknS2JLjPzDTT23embYvkvSz1Mczkv6tVmMzMzPP0LubPYFdgZeAGcC+kh4HjgGGRkRI2rAd7Q8FDgT6A0slXRMRK0srSBoPjAcYMGBAO7oyM7NSnqF3LzMj4oWIWAM0AHXAMuBd4KeSjgXebkf7f4iIFRHxGvAKsFnTChExMSLqI6K+T58+7ejKzMxKOaF3LytKtlcD60bEKmA0cBtwJHB32r+K9O9D0jpAz7a0396AzcysMk7o3ZykfsCAiLgLOBsYlnY9B4xM20cB69U+OjMzq5RnUNYf+J2kXoCAc1L5dal8Htms/a2c4jMzswooIvKOwbqpzTffPMaPH593GIXh56GbdQ+S5kREfdNyL7mbmZkVgBO6mZlZATihm5mZFYATupmZWQE4oZuZmRWAr3K33NTX18fs2X62i5lZa/gqdzMzswJzQjczMysAL7lbbiS9CSzNO44cDAJeyzuIGuuOYwaPu7up1bi3iYhNmhb61q+Wp6XlzgMVnaTZ3W3c3XHM4HHnHUet5T1uL7mbmZkVgBO6mZlZATihW54m5h1ATrrjuLvjmMHj7m5yHbcvijMzMysAz9DNzMwKwAndzMysAJzQreokHS5pqaSnJJ1fZv/6km5J+x+VVFey7xupfKmkw2oaeDu1ddyS6iS9I6kh/Vxb8+DboYJx7y9prqRVksY22XeqpCfTz6m1i7r92jnu1SWf9+TaRd1+FYz7HEmLJc2XNE3SNiX7ivx5tzTu2nzeEeEf/1TtB+gBPA1sB/QE5gG7NKnzReDatH0icEva3iXVXx/YNrXTI+8x1WDcdcDCvMfQgeOuA/YAbgLGlpRvDDyTfm+UtjfKe0wdPe60b3neY+jAcR8I9EnbZ5b8Oy/651123LX8vD1Dt2obDTwVEc9ExHvAr4FPNqnzSeDGtH0bcLAkpfJfR8SKiHgWeCq11xW0Z9xd2VrHHRHPRcR8YE2TYw8D7omINyLib8A9wOG1CLoK2jPurqyScd8XEW+nl48AW6bton/ezY27ZpzQrdq2AP635PULqaxsnYhYBSwDBlZ4bGfVnnEDbCvpMUkPSPpIRwdbRe35zIr+ebekl6TZkh6RdHRVI+tYrR33acAf23hsZ9KecUONPm/f+tUsfy8DW0fE65JGAr+VtGtE/CPvwKzDbBMRL0raDrhX0oKIeDrvoKpJ0qeBeuCAvGOppWbGXZPP2zN0q7YXga1KXm+ZysrWkbQuMAB4vcJjO6s2jzudYngdICLmkJ2r27HDI66O9nxmRf+8mxURL6bfzwD3A3tWM7gOVNG4JR0CXAAcFRErWnNsJ9Wecdfs83ZCt2qbBQyRtK2knmQXfzW9qnMy0HiF61jg3siuHJkMnJiuBt8WGALMrFHc7dXmcUvaRFIPgPQNfgjZBUNdQSXjbs4U4FBJG0naCDg0lXUFbR53Gu/6aXsQsC+wuMMira61jlvSnsBPyJLaKyW7Cv15Nzfumn7eeV896J/i/QAfB54gm2lekMq+nf6hA/QCbiW76G0msF3JsRek45YCH8t7LLUYN3AcsAhoAOYCn8h7LFUe9yiyc45vka3ELCo59nPp/XgK+GzeY6nFuIF9gAVkV0ovAE7LeyxVHvf/B/6a/j03AJO7yedddty1/Lx961czM7MC8JK7mZlZATihm5mZFYATupmZWQE4oZuZmRWAE7qZmVkBOKGbWacl6aEa91cn6VO17NOsWpzQzazTioh9atVXuntfHeCEbl2SE7qZdVqSlqffY9KDa34n6RlJl0o6WdJMSQskbZ/qTZJ0bXoQxhOSjkzlvSTdkOo+JunAVD5O0mRJ9wLTgEuBj6TnVp+dZuwPKnuu+VxJ+5TEc7+k2yQtkXRz45PzJI2S9JCkeSm+/pJ6SJogaVZ6Xvbpqe5gSdNTfwu72IN5rJPxw1nMrKsYBuwMvEF2a9zrI2K0pK8AZwFfTfXqyB53uT1wn6QdgC8BERG7SxoKTJXUeL/8EcAeEfGGpDHAuRHR+EWgD/DRiHhX0hDgV2QP3oDsfty7Ai8BM4B9Jc0EbgFOiIhZkjYA3iF7+tayiBiVbgM6Q9JU4FhgSkRckm7/26fK75l1I07oZtZVzIqIlwEkPQ1MTeULgANL6v1PRKwBnpT0DDAU2A+4AiAilkh6nn89AOeeiHijmT7XA66UNBxYzfsfmjMzIl5I8TSQfZFYBrwcEbNSX/9I+w8F9pA0Nh07gOye/bOAn0laD/htRDS05g0xK+WEbmZdxYqS7TUlr9fw/v/Lmt7Pem33t36rhX1nk92fexjZKcp3m4lnNS3/fyrgrIj4wMNIJO0PHAFMkvSjiLhpLfGaleVz6GZWNMdLWiedV9+O7EE/DwInA6Sl9q1TeVNvAv1LXg8gm3GvAT4D9FhL30uBwZJGpb76p4vtpgBnppk4knaU1FfSNsBfI+I64Hqy5X+zNvEM3cyK5s9kT7PbADgjnf++GrhG0gJgFTAuIlak69hKzQdWS5oHTAKuBm6XdApwNy3P5omI9ySdAFwhqTfZ+fNDyJJ1HTA3XTz3KnA0MAY4T9JKYDlwSvuGbt2Zn7ZmZoUhaRJwZ0TclncsZrXmJXczM7MC8AzdzMysADxDNzMzKwAndDMzswJwQjczMysAJ3QzM7MCcEI3MzMrgP8DJHXa5zTjDLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"importances\", y=\"feature\", data=data_plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "f0330fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC for the best model of Random Forest is:  0.8574660633484162\n"
     ]
    }
   ],
   "source": [
    "predict_test = data_RF1.predict_proba(x_test) [:, 1]\n",
    "\n",
    "auc = roc_auc_score(y_test, predict_test)\n",
    "\n",
    "print(\"The AUC for the best model of Random Forest is: \", auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67243775",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f0e25e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "a655143d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "563f91ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[91 11]\n",
      " [23 29]]\n",
      "accuracy: 0.7792207792207793\n",
      "recall: 0.5576923076923077\n",
      "precision: 0.725\n"
     ]
    }
   ],
   "source": [
    "svm_model1 = SVC(C=2, kernel=\"linear\", probability=True, random_state=42)\n",
    "\n",
    "svm_model1.fit(x_train, y_train)\n",
    "\n",
    "pred1 = svm_model1.predict(x_test)\n",
    "\n",
    "x1 = confusion_matrix(y_test, svm_model1.predict(x_test))\n",
    "print(x1)\n",
    "\n",
    "print(\"accuracy:\",accuracy_score(y_test, pred1))\n",
    "print(\"recall:\", recall_score(y_test, pred1))\n",
    "print(\"precision:\",precision_score(y_test,pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "53b3f30a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for the test_set at cost of is:  0.865761689291101\n"
     ]
    }
   ],
   "source": [
    "print(\"AUC for the test_set at cost of is: \", roc_auc_score(y_test, svm_model1.predict_proba(x_test)[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e5fddf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "71f80886",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "9e6e9075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for all 4 variables together:  0.8470965309200603\n"
     ]
    }
   ],
   "source": [
    "#logistic_regression_data\n",
    "logistic_reg_all = LogisticRegression(penalty = \"none\").fit(x_train, y_train)\n",
    "\n",
    "#logistic regression all predict probabilty\n",
    "predict_logistic_reg_all = logistic_reg_all.predict_proba(x_test)[:, 1]\n",
    "\n",
    "#auc score for all\n",
    "e = roc_auc_score(y_test, predict_logistic_reg_all)\n",
    "print(\"AUC for all 4 variables together: \", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8ac1c2",
   "metadata": {},
   "source": [
    "## "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}