pima-classification-baseline-02.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/boostcourse-ds-511/blob/master/pima-classification-baseline-02.ipynb)\n",
    "\n",
    "* [부스트코스] 프로젝트로 배우는 데이터사이언스 > 3. 탐색한 데이터로 모델성능 개선 의 파일입니다.\n",
    "\n",
    "https://www.boostcourse.org/ds214/joinLectures/40944\n",
    "\n",
    "## 데이터셋 출처\n",
    "* [Pima Indians Diabetes Database | Kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html\n",
    "\n",
    "\n",
    "### 데이터 구성\n",
    "\n",
    "* Pregnancies : 임신 횟수\n",
    "* Glucose : 2시간 동안의 경구 포도당 내성 검사에서 혈장 포도당 농도 \n",
    "* BloodPressure : 이완기 혈압 (mm Hg)\n",
    "* SkinThickness : 삼두근 피부 주름 두께 (mm), 체지방을 추정하는데 사용되는 값\n",
    "* Insulin : 2시간 혈청 인슐린 (mu U / ml)\n",
    "* BMI : 체질량 지수 (체중kg / 키(m)^2)\n",
    "* DiabetesPedigreeFunction : 당뇨병 혈통 기능\n",
    "* Age : 나이\n",
    "* Outcome : 768개 중에 268개의 결과 클래스 변수(0 또는 1)는 1이고 나머지는 0입니다.\n",
    "\n",
    "\n",
    "## 필요한 라이브러리 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 분석을 위한 pandas, 수치계산을 위한 numpy\n",
    "# 시각화를 위한 seaborn, matplotlib.pyplot 을 로드합니다. \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"http://bit.ly/data-diabetes-csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>85</td>\n",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "### 수치형 변수를 범주형 변수로 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Pregnancies_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Pregnancies  Pregnancies_high\n",
       "0            6             False\n",
       "1            1             False\n",
       "2            8              True\n",
       "3            1             False\n",
       "4            0             False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Pregnancies_high\"] = df[\"Pregnancies\"] > 6\n",
    "df[[\"Pregnancies\", \"Pregnancies_high\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_low</th>\n",
       "      <th>Age_middle</th>\n",
       "      <th>Age_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Age_low  Age_middle  Age_high\n",
       "0   50    False        True     False\n",
       "1   31    False        True     False\n",
       "2   32    False        True     False\n",
       "3   21     True       False     False\n",
       "4   33    False        True     False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-Hot-Encoding\n",
    "# 수치 => 범주 => 수치\n",
    "df[\"Age_low\"] = df[\"Age\"] < 30\n",
    "df[\"Age_middle\"] = (df[\"Age\"] >= 30) & (df[\"Age\"] <= 60)\n",
    "df[\"Age_high\"] = df[\"Age\"] > 60\n",
    "df[[\"Age\", \"Age_low\", \"Age_middle\", \"Age_high\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda3c1d6510>"
      ]
     },
     "execution_count": 6,
     "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.countplot(data=df, x=\"Age_high\", hue=\"Outcome\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결측치 다루기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "Pregnancies_high            0\n",
       "Age_low                     0\n",
       "Age_middle                  0\n",
       "Age_high                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Insulin</th>\n",
       "      <th>Insulin_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Insulin  Insulin_nan\n",
       "0        0          NaN\n",
       "1        0          NaN\n",
       "2        0          NaN\n",
       "3       94         94.0\n",
       "4      168        168.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Insulin_nan\"] = df[\"Insulin\"].replace(0, np.nan)\n",
    "df[[\"Insulin\", \"Insulin_nan\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "374"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Insulin_nan\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4869791666666667"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치 비율\n",
    "df[\"Insulin_nan\"].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Insulin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Insulin_nan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.792000</td>\n",
       "      <td>39</td>\n",
       "      <td>130.287879</td>\n",
       "      <td>102.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.335821</td>\n",
       "      <td>0</td>\n",
       "      <td>206.846154</td>\n",
       "      <td>169.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Insulin        Insulin_nan       \n",
       "               mean median        mean median\n",
       "Outcome                                      \n",
       "0         68.792000     39  130.287879  102.5\n",
       "1        100.335821      0  206.846154  169.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Outcome\"])[[\"Insulin\", \"Insulin_nan\"]].agg([\"mean\", \"median\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 채우기\n",
    "df.loc[(df[\"Outcome\"] == 0) & (df[\"Insulin_nan\"].isnull()), \"Insulin_nan\"] = 102.5\n",
    "df.loc[(df[\"Outcome\"] == 1) & (df[\"Insulin_nan\"].isnull()), \"Insulin_nan\"] = 169.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 정규분포 만들기\n",
    "* 왜도, 첨도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fda3cb911d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.distplot(df.loc[df[\"Insulin\"] > 0, \"Insulin\"])\n",
    "# seaborn 0.11.0 이상\n",
    "sns.displot(df.loc[df[\"Insulin\"] > 0, \"Insulin\"], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fda3cd10450>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Insulin_log = np.log(df.loc[df[\"Insulin\"] > 0, \"Insulin\"] + 1)\n",
    "# sns.distplot(Insulin_log)\n",
    "# seaborn 0.11.0 이상\n",
    "sns.displot(Insulin_log, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fda394941d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df, x=\"Insulin_nan\", kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fda3cb235d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Insulin_log\"] = np.log(df[\"Insulin_nan\"] + 1)\n",
    "sns.displot(df, x=\"Insulin_log\", kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 파생변수 만들기\n",
    "* EDA에서 해본 상관분석을 바탕으로 파생변수를 생성합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fda3d221410>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 413.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=df, x=\"Insulin_nan\", y=\"Glucose\", hue=\"Outcome\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: low_glu_insulin, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"low_glu_insulin\"] = (df[\"Glucose\"] < 100) & (df[\"Insulin_nan\"] <= 102.5)\n",
    "df[\"low_glu_insulin\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>low_glu_insulin</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>332</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>263</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "low_glu_insulin  False  True \n",
       "Outcome                      \n",
       "0                  332    168\n",
       "1                  263      5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"Outcome\"], df[\"low_glu_insulin\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 이상치(outlier) 다루기\n",
    "* https://ko.wikipedia.org/wiki/%EC%83%81%EC%9E%90_%EC%88%98%EC%97%BC_%EA%B7%B8%EB%A6%BC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda3d63e650>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 2))\n",
    "sns.boxplot(data=df, x=\"Insulin_nan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean     141.753906\n",
       "std       89.100847\n",
       "min       14.000000\n",
       "25%      102.500000\n",
       "50%      102.500000\n",
       "75%      169.500000\n",
       "max      846.000000\n",
       "Name: Insulin_nan, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Insulin_nan\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR3 = df[\"Insulin_nan\"].quantile(0.75)\n",
    "IQR1 = df[\"Insulin_nan\"].quantile(0.25)\n",
    "IQR = IQR3 - IQR1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "270.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OUT = IQR3 + (IQR * 1.5)\n",
    "OUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 16)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Insulin_nan\"] > OUT].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Insulin_nan\"] > 600].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling\n",
    "* https://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.84832379,  0.46849198],\n",
       "       [-1.12339636, -0.36506078],\n",
       "       [ 1.94372388,  0.60439732],\n",
       "       ...,\n",
       "       [ 0.00330087, -0.68519336],\n",
       "       [ 0.1597866 , -0.37110101],\n",
       "       [-0.8730192 , -0.47378505]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df[[\"Glucose\", \"DiabetesPedigreeFunction\"]])\n",
    "scale = scaler.transform(df[[\"Glucose\", \"DiabetesPedigreeFunction\"]])\n",
    "scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[[\"Glucose\", \"DiabetesPedigreeFunction\"]] = scale\n",
    "# df[[\"Glucose\", \"DiabetesPedigreeFunction\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = df[[\"Glucose\", \"DiabetesPedigreeFunction\"]].hist(figsize=(15, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV 파일로 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/diabetes_feature.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "      <th>Pregnancies_high</th>\n",
       "      <th>Age_low</th>\n",
       "      <th>Age_middle</th>\n",
       "      <th>Age_high</th>\n",
       "      <th>Insulin_nan</th>\n",
       "      <th>Insulin_log</th>\n",
       "      <th>low_glu_insulin</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>169.5</td>\n",
       "      <td>5.138735</td>\n",
       "      <td>False</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>102.5</td>\n",
       "      <td>4.639572</td>\n",
       "      <td>True</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",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>169.5</td>\n",
       "      <td>5.138735</td>\n",
       "      <td>False</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",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>True</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>168.0</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>False</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  Pregnancies_high  Age_low  \\\n",
       "0                     0.627   50        1             False    False   \n",
       "1                     0.351   31        0             False    False   \n",
       "2                     0.672   32        1              True    False   \n",
       "3                     0.167   21        0             False     True   \n",
       "4                     2.288   33        1             False    False   \n",
       "\n",
       "   Age_middle  Age_high  Insulin_nan  Insulin_log  low_glu_insulin  \n",
       "0        True     False        169.5     5.138735            False  \n",
       "1        True     False        102.5     4.639572             True  \n",
       "2        True     False        169.5     5.138735            False  \n",
       "3       False     False         94.0     4.553877             True  \n",
       "4        True     False        168.0     5.129899            False  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"data/diabetes_feature.csv\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습, 예측 데이터셋 나누기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "614"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8:2 의 비율로 구하기 위해 전체 데이터의 행에서 80% 위치에 해당되는 값을 구해서 split_count 라는 변수에 담습니다.\n",
    "split_count = int(df.shape[0] * 0.8)\n",
    "split_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 16)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train, test로 슬라이싱을 통해 데이터를 나눕니다.\n",
    "train = df[:split_count].copy()\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(610, 16)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"Insulin_nan\"] < 600].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(610, 16)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train[train[\"Insulin_nan\"] < 600]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(154, 16)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df[split_count:].copy()\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습, 예측에 사용할 컬럼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Glucose',\n",
       " 'BloodPressure',\n",
       " 'SkinThickness',\n",
       " 'BMI',\n",
       " 'DiabetesPedigreeFunction',\n",
       " 'Age',\n",
       " 'Pregnancies_high',\n",
       " 'Insulin_nan',\n",
       " 'low_glu_insulin']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_names 라는 변수에 학습과 예측에 사용할 컬럼명을 가져옵니다.\n",
    "feature_names = train.columns.tolist()\n",
    "feature_names.remove(\"Pregnancies\")\n",
    "feature_names.remove(\"Outcome\")\n",
    "feature_names.remove(\"Age_low\")\n",
    "feature_names.remove(\"Age_middle\")\n",
    "feature_names.remove(\"Age_high\")\n",
    "feature_names.remove(\"Insulin\")\n",
    "feature_names.remove(\"Insulin_log\")\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정답값이자 예측해야 될 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Outcome'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label_name 이라는 변수에 예측할 컬럼의 이름을 담습니다.\n",
    "\n",
    "label_name = \"Outcome\"\n",
    "label_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습, 예측 데이터셋 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(610, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pregnancies_high</th>\n",
       "      <th>Insulin_nan</th>\n",
       "      <th>low_glu_insulin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>169.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>102.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "      <td>169.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>94.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>168.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness   BMI  DiabetesPedigreeFunction  Age  \\\n",
       "0      148             72             35  33.6                     0.627   50   \n",
       "1       85             66             29  26.6                     0.351   31   \n",
       "2      183             64              0  23.3                     0.672   32   \n",
       "3       89             66             23  28.1                     0.167   21   \n",
       "4      137             40             35  43.1                     2.288   33   \n",
       "\n",
       "   Pregnancies_high  Insulin_nan  low_glu_insulin  \n",
       "0             False        169.5            False  \n",
       "1             False        102.5             True  \n",
       "2              True        169.5            False  \n",
       "3             False         94.0             True  \n",
       "4             False        168.0            False  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습 세트 만들기 예) 시험의 기출문제\n",
    "\n",
    "X_train = train[feature_names]\n",
    "print(X_train.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(610,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정답 값을 만들어 줍니다. 예) 기출문제의 정답\n",
    "y_train = train[label_name]\n",
    "print(y_train.shape)\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(154, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pregnancies_high</th>\n",
       "      <th>Insulin_nan</th>\n",
       "      <th>low_glu_insulin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>138</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.557</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>144.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.207</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "      <td>102.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>117</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>0.157</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>102.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>68</td>\n",
       "      <td>62</td>\n",
       "      <td>13</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.257</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>112</td>\n",
       "      <td>82</td>\n",
       "      <td>24</td>\n",
       "      <td>28.2</td>\n",
       "      <td>1.282</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>169.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  BloodPressure  SkinThickness   BMI  DiabetesPedigreeFunction  \\\n",
       "614      138             74             26  36.1                     0.557   \n",
       "615      106             72              0  25.8                     0.207   \n",
       "616      117             96              0  28.7                     0.157   \n",
       "617       68             62             13  20.1                     0.257   \n",
       "618      112             82             24  28.2                     1.282   \n",
       "\n",
       "     Age  Pregnancies_high  Insulin_nan  low_glu_insulin  \n",
       "614   50              True        144.0            False  \n",
       "615   27             False        102.5            False  \n",
       "616   30             False        102.5            False  \n",
       "617   23             False         15.0             True  \n",
       "618   50              True        169.5            False  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측에 사용할 데이터세트를 만듭니다. 예) 실전 시험 문제\n",
    "\n",
    "X_test = test[feature_names]\n",
    "print(X_test.shape)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(154,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "614    1\n",
       "615    0\n",
       "616    0\n",
       "617    0\n",
       "618    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측의 정답값 예) 실전 시험 문제의 정답\n",
    "y_test = test[label_name]\n",
    "\n",
    "print(y_test.shape)\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 머신러닝 알고리즘 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=42, splitter='best')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습(훈련)\n",
    "* 시험을 볼 때 기출문제(X_train)와 정답(y_train)을 보고 공부하는 과정과 유사합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=42, splitter='best')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예측\n",
    "* 실전 시험문제(X_test)라고 보면 됩니다. 우리가 정답을 직접 예측합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model.predict(X_test)\n",
    "y_predict[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 트리 알고리즘 분석하기\n",
    "* 의사결정나무를 시각화 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "tree = plot_tree(model,\n",
    "                 feature_names=feature_names,\n",
    "                 max_depth=4,\n",
    "                 filled=True,\n",
    "                 fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# graphviz 를 통해 시각화 합니다.\n",
    "# graphviz 는 별도의 설치가 필요합니다. \n",
    "# graphviz 와 파이썬에서 graphviz 를 사용할 수 있게 해주는 도구 2가지를 설치해 주셔야 합니다.\n",
    "# import graphviz\n",
    "# from sklearn.tree import export_graphviz\n",
    "\n",
    "# dot_tree = export_graphviz(model,\n",
    "#                            feature_names = feature_names,\n",
    "#                            filled=True)\n",
    "# graphviz.Source(dot_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10720708, 0.03829317, 0.02739544, 0.08008031, 0.02662991,\n",
       "       0.08272508, 0.        , 0.63283861, 0.0048304 ])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 피처의 중요도를 추출하기\n",
    "\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda3e9f49d0>"
      ]
     },
     "execution_count": 49,
     "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": [
    "# 피처의 중요도 시각화 하기\n",
    "\n",
    "sns.barplot(x=model.feature_importances_, y=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정확도(Accuracy) 측정하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 실제값 - 예측값을 빼주면 같은 값은 0으로 나오게 됩니다. \n",
    "# 여기에서 절대값을 씌운 값이 1인 값이 다르게 예측한 값이 됩니다.\n",
    "# 44 => 39 => 49(나이25세 기준) => 55(나이30세 기준) \n",
    "# => 23(인슐린 결측치를 평균으로 대체) => 16(인슐린 결측치를 중앙값으로 대체)\n",
    "# => 15(인슐린&글루코스 파생수변수 추가)\n",
    "# => 15(인슐린 수치 600이상 이상치제거 )\n",
    "diff_count = abs(y_test - y_predict).sum()\n",
    "diff_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.25974025974025"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측의 정확도를 구합니다. 100점 만점 중에 몇 점을 맞았는지 구한다고 보면 됩니다.\n",
    "# 71 => => 85(인슐린 결측치를 평균으로 대체) => 89(인슐린의 결측치를 중앙값으로 대체)\n",
    "# => 90(인슐린&글루코스 상관계수로 파생변수를 생성)\n",
    "(len(y_test) - diff_count) / len(y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.25974025974025"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 위에서 처럼 직접 구할 수도 있지만 미리 구현된 알고리즘을 가져와 사용합니다.\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_predict) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.25974025974025"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model 의 score 로 점수를 계산합니다.\n",
    "model.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "types_to_exclude": [
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