pima-classification-baseline-04.ipynb

{
 "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-04.ipynb)\n",
    "\n",
    "\n",
    "* 강좌 보기 : https://www.boostcourse.org/ds214/\n",
    "\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, 16)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/diabetes_feature.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터셋을 미리보기 합니다.\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습과 예측에 사용할 데이터셋 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Pregnancies_high',\n",
       "       'Age_low', 'Age_middle', 'Age_high', 'Insulin_nan', 'Insulin_log',\n",
       "       'low_glu_insulin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Glucose', 'BloodPressure', 'SkinThickness',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Pregnancies_high',\n",
    "       'Insulin_nan', 'low_glu_insulin']]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Outcome']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 사이킷런에서 제공하는 model_selection 의 train_test_split 으로 만듭니다.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 9), (614,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train 세트의 문제와 정답의 데이터 수를 확인해 주세요.\n",
    "\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((154, 9), (154,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test 세트의 문제와 정답의 데이터 수를 확인해 주세요.\n",
    "\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 머신러닝 알고리즘 사용하기\n",
    "* [결정 트리 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EA%B2%B0%EC%A0%95_%ED%8A%B8%EB%A6%AC)\n",
    "* [결정 트리 학습법 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EA%B2%B0%EC%A0%95_%ED%8A%B8%EB%A6%AC_%ED%95%99%EC%8A%B5%EB%B2%95)\n",
    "* [랜덤 포레스트 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%9E%9C%EB%8D%A4_%ED%8F%AC%EB%A0%88%EC%8A%A4%ED%8A%B8)\n",
    "* [배깅 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%B0%B0%EA%B9%85)\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/83/0_jW2hAGmYEFH0RP9W.png\">\n",
    "\n",
    "이미지 출처 : https://upload.wikimedia.org/wikipedia/commons/8/83/0_jW2hAGmYEFH0RP9W.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Single tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# model = DecisionTreeClassifier(random_state=42)\n",
    "# model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "# model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting \n",
    "* https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/\n",
    "\n",
    "<img src=\"https://quantdare.com/wp-content/uploads/2016/04/bb3.png\" width=\"600\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           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, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=42, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model = GradientBoostingClassifier(random_state=42)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습과 예측하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           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, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=42, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습을 시킵니다.\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.06409948e-01, 7.04053663e-03, 6.57236021e-02, 3.73742490e-02,\n",
       "       2.62954875e-02, 9.47259928e-02, 1.56708089e-04, 6.62273475e-01,\n",
       "       0.00000000e+00])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = X_train.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121c1e470>"
      ]
     },
     "execution_count": 43,
     "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.barplot(x=model.feature_importances_, y=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측을 하고 결과를 y_predict에 담습니다.\n",
    "y_predict = model.predict(X_test)\n",
    "y_predict[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정확도(Accuracy) 측정하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다르게 예측한 갯수를 구해서 diff_count 에 할당해 줍니다.\n",
    "# DT : 28\n",
    "# RF : 20\n",
    "# GB : 24\n",
    "(y_predict != y_test).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8441558441558441"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy score를 구합니다.\n",
    "# DT: 0.818\n",
    "# RF: 0.870\n",
    "# GBL 0.844\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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