Python활용AI모델링_02_머신러닝_딥러닝_퀴즈_easy_v3_20221010.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/Jangchanhi/pyAICE/blob/master/Python%ED%99%9C%EC%9A%A9AI%EB%AA%A8%EB%8D%B8%EB%A7%81_02_%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D_%EB%94%A5%EB%9F%AC%EB%8B%9D_%ED%80%B4%EC%A6%88_easy_v3_20221010.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-8SbOMdy0FT4"
   },
   "source": [
    "# [실습-퀴즈] Python을 활용한 AI 모델링 - 머신러닝 파트\n",
    "+ 이번시간에는 Python을 활용한 AI 모델링에서 머신러닝에 대해 실습해 보겠습니다.\n",
    "+ 머신러닝 모델에는 아래와 같이 모델들이 있습니다.\n",
    " + 단일 분류예측 모델 : LogisticRegression, KNN, DecisionTree\n",
    " + 앙상블(Ensemble) 모델 : RandomForest, XGBoost, LGBM\n",
    "+ 솔직히, 머신러닝이 딥러닝보다 코딩하기 쉽습니다. 4줄 템플릿에 맞쳐 코딩하면 되기 때문입니다.\n",
    "+ 한가지 당부 드리고 싶은 말은 \"백문이불여일타\" 입니다. \n",
    "+ 이론보다 실습이 더 많은 시간과 노력이 투자 되어야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CB9Zcdvv0FT9"
   },
   "source": [
    "## 학습목차\n",
    "1. 머신러닝 모델 프로세스\n",
    " - 데이터 가져오기\n",
    " - 데이터 전처리\n",
    " - Train, Test 데이터셋 분할\n",
    " - 데이터 정규화\n",
    " - 단일 분류예측 모델 : LogisticRegression, KNN, DecisionTree\n",
    " - 앙상블(Ensemble) 모델 : RandomForest, XGBoost, LGBM \n",
    "2. 재현율 성능이 너무 안나온다. 어떻게 해결할수 있을까?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V085LH_00FT-"
   },
   "source": [
    "#  \n",
    "# 1. 머신러닝 모델 프로세스\n",
    "① 라이브러리 임포트(import)  \n",
    "② 데이터 가져오기(Loading the data)  \n",
    "③ 탐색적 데이터 분석(Exploratory Data Analysis)  \n",
    "④ 데이터 전처리(Data PreProcessing) : 데이터타입 변환, Null 데이터 처리, 누락데이터 처리, \n",
    "더미특성 생성, 특성 추출 (feature engineering) 등  \n",
    "⑤ Train, Test  데이터셋 분할  \n",
    "⑥ 데이터 정규화(Normalizing the Data)  \n",
    "⑦ 모델 개발(Creating the Model)  \n",
    "⑧ 모델 성능 평가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AY9RWMZE0FT_"
   },
   "source": [
    "## ① 라이브러리 임포트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZARhtnLw0FUA"
   },
   "source": [
    "##### 필요 라이브러리 임포트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "VY4TxgQj0FUA"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WLHB9VMx0FUC"
   },
   "source": [
    "## ② 데이터 로드"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UVuC6FaG0FUD"
   },
   "source": [
    "#### data_v1_save.csv 파일 읽어오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "HvNm0L830FUD"
   },
   "outputs": [],
   "source": [
    "# 앞쪽 전처리에서 저장한 cust_data.csv 파일 읽기\n",
    "df = pd.read_csv('pyAice_data/data_v1_save.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4V5lNvqe0FUE"
   },
   "source": [
    "## ③ 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "_6v_Ai8e0FUF",
    "outputId": "4ef8484c-c3c4-4465-cefb-152fb2740e59",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   gender Partner Dependents  tenure     MultipleLines InternetService  \\\n0    Male      No         No      34                No             DSL   \n1    Male      No         No       2                No             DSL   \n2    Male      No         No      45  No phone service             DSL   \n3  Female      No         No       2                No     Fiber optic   \n4  Female      No         No       8               Yes     Fiber optic   \n\n  OnlineSecurity OnlineBackup TechSupport StreamingTV StreamingMovies  \\\n0            Yes           No          No          No              No   \n1            Yes          Yes          No          No              No   \n2            Yes           No         Yes          No              No   \n3             No           No          No          No              No   \n4             No           No          No         Yes             Yes   \n\n         Contract PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n0        One year               No               Mailed check           56.95   \n1  Month-to-month              Yes               Mailed check           53.85   \n2        One year               No  Bank transfer (automatic)           42.30   \n3  Month-to-month              Yes           Electronic check           70.70   \n4  Month-to-month              Yes           Electronic check           99.65   \n\n   TotalCharges  Churn  \n0       1889.50      0  \n1        108.15      1  \n2       1840.75      0  \n3        151.65      1  \n4        820.50      1  ",
      "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>gender</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>56.95</td>\n      <td>1889.50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>8</td>\n      <td>Yes</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>99.65</td>\n      <td>820.50</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 17컬럼, 7,027 라인\n",
    "# df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "IwQH_hhO0FUG",
    "outputId": "f12f5dbf-63b5-4bc5-fd14-741348d0118d",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      gender Partner Dependents  tenure     MultipleLines InternetService  \\\n7022  Female      No         No      72                No              No   \n7023    Male     Yes        Yes      24               Yes             DSL   \n7024  Female     Yes        Yes      72               Yes     Fiber optic   \n7025  Female     Yes        Yes      11  No phone service             DSL   \n7026    Male     Yes         No       4               Yes     Fiber optic   \n\n           OnlineSecurity         OnlineBackup          TechSupport  \\\n7022  No internet service  No internet service  No internet service   \n7023                  Yes                   No                  Yes   \n7024                   No                  Yes                   No   \n7025                  Yes                   No                   No   \n7026                   No                   No                   No   \n\n              StreamingTV      StreamingMovies        Contract  \\\n7022  No internet service  No internet service        Two year   \n7023                  Yes                  Yes        One year   \n7024                  Yes                  Yes        One year   \n7025                   No                   No  Month-to-month   \n7026                   No                   No  Month-to-month   \n\n     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n7022              Yes  Bank transfer (automatic)           21.15   \n7023              Yes               Mailed check           84.80   \n7024              Yes    Credit card (automatic)          103.20   \n7025              Yes           Electronic check           29.60   \n7026              Yes               Mailed check           74.40   \n\n      TotalCharges  Churn  \n7022       1419.40      0  \n7023       1990.50      0  \n7024       7362.90      0  \n7025        346.45      0  \n7026        306.60      1  ",
      "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>gender</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7022</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>72</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>21.15</td>\n      <td>1419.40</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7023</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>24</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>84.80</td>\n      <td>1990.50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7024</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>72</td>\n      <td>Yes</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Credit card (automatic)</td>\n      <td>103.20</td>\n      <td>7362.90</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7025</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>11</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.60</td>\n      <td>346.45</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7026</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>4</td>\n      <td>Yes</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>74.40</td>\n      <td>306.60</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Qp-Kap2r0FUG",
    "outputId": "70a93a42-ae63-4a98-bb3e-d657bdde6499",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Churn 레이블 불균형\n",
    "df['Churn'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jIh8NmeQ0FUH"
   },
   "source": [
    "## ④ 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q-nhs1qF0FUH"
   },
   "source": [
    "+ 모든 데이터값들은 숫자형으로 되어야 한다. 즉, Ojbect 타입을 모든 숫자형 변경 필요\n",
    "+ 순서없는 범주형 데이터를 숫자형을 변환시 LabelEncoder 활용 가능\n",
    "+ Object 컬럼에 대해 Pandas get_dummies 함수 활용하여 One-Hot-Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Blq556Xf0FUH",
    "outputId": "ea98947c-4726-49fb-f8f4-4e52c948d300",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      MultipleLines\n0                No\n1                No\n2  No phone service\n3                No\n4               Yes",
      "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>MultipleLines</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>No phone service</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultipleLines 컬럼 내용 보기\n",
    "df[['MultipleLines']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "dpzTPy9o0FUI",
    "outputId": "d1fbc8a9-b3d6-44b1-889e-4d67b4cc62c7",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "No                  3380\nYes                 2966\nNo phone service     681\nName: MultipleLines, dtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultipleLines 컬럼에 대한 분포 확인 : 3가지 되어 있음 확인\n",
    "# one-hot-encoding 봄/ 여름 /가을 /겨울\n",
    "df['MultipleLines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "CvSKdzeQ0FUI",
    "outputId": "7488b657-2ef6-4f65-e159-7ea874b1aab3",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 644
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      gender Partner Dependents  tenure InternetService       OnlineSecurity  \\\n0       Male      No         No      34             DSL                  Yes   \n1       Male      No         No       2             DSL                  Yes   \n2       Male      No         No      45             DSL                  Yes   \n3     Female      No         No       2     Fiber optic                   No   \n4     Female      No         No       8     Fiber optic                   No   \n...      ...     ...        ...     ...             ...                  ...   \n7022  Female      No         No      72              No  No internet service   \n7023    Male     Yes        Yes      24             DSL                  Yes   \n7024  Female     Yes        Yes      72     Fiber optic                   No   \n7025  Female     Yes        Yes      11             DSL                  Yes   \n7026    Male     Yes         No       4     Fiber optic                   No   \n\n             OnlineBackup          TechSupport          StreamingTV  \\\n0                      No                   No                   No   \n1                     Yes                   No                   No   \n2                      No                  Yes                   No   \n3                      No                   No                   No   \n4                      No                   No                  Yes   \n...                   ...                  ...                  ...   \n7022  No internet service  No internet service  No internet service   \n7023                   No                  Yes                  Yes   \n7024                  Yes                   No                  Yes   \n7025                   No                   No                   No   \n7026                   No                   No                   No   \n\n          StreamingMovies        Contract PaperlessBilling  \\\n0                      No        One year               No   \n1                      No  Month-to-month              Yes   \n2                      No        One year               No   \n3                      No  Month-to-month              Yes   \n4                     Yes  Month-to-month              Yes   \n...                   ...             ...              ...   \n7022  No internet service        Two year              Yes   \n7023                  Yes        One year              Yes   \n7024                  Yes        One year              Yes   \n7025                   No  Month-to-month              Yes   \n7026                   No  Month-to-month              Yes   \n\n                  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \\\n0                  Mailed check           56.95       1889.50      0   \n1                  Mailed check           53.85        108.15      1   \n2     Bank transfer (automatic)           42.30       1840.75      0   \n3              Electronic check           70.70        151.65      1   \n4              Electronic check           99.65        820.50      1   \n...                         ...             ...           ...    ...   \n7022  Bank transfer (automatic)           21.15       1419.40      0   \n7023               Mailed check           84.80       1990.50      0   \n7024    Credit card (automatic)          103.20       7362.90      0   \n7025           Electronic check           29.60        346.45      0   \n7026               Mailed check           74.40        306.60      1   \n\n      MultipleLines_No  MultipleLines_No phone service  MultipleLines_Yes  \n0                    1                               0                  0  \n1                    1                               0                  0  \n2                    0                               1                  0  \n3                    1                               0                  0  \n4                    0                               0                  1  \n...                ...                             ...                ...  \n7022                 1                               0                  0  \n7023                 0                               0                  1  \n7024                 0                               0                  1  \n7025                 0                               1                  0  \n7026                 0                               0                  1  \n\n[7027 rows x 19 columns]",
      "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>gender</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n      <th>MultipleLines_No</th>\n      <th>MultipleLines_No phone service</th>\n      <th>MultipleLines_Yes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>56.95</td>\n      <td>1889.50</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>8</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>99.65</td>\n      <td>820.50</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>7022</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>No</td>\n      <td>72</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>21.15</td>\n      <td>1419.40</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7023</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>24</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>84.80</td>\n      <td>1990.50</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7024</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>72</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Credit card (automatic)</td>\n      <td>103.20</td>\n      <td>7362.90</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7025</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>11</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.60</td>\n      <td>346.45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7026</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>4</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>74.40</td>\n      <td>306.60</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>7027 rows × 19 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultipleLines 컬럼의 값들이 문자열로 되어 있어 숫자로 변환해야 함. 컴퓨터가 이해할수 있도록\n",
    "# Object 컬럼의 데이터를 원-핫-인코딩해서 숫자로 변경해 주는 함수 : Pandas get_dummies()\n",
    "\n",
    "pd.get_dummies(data=df, columns=['MultipleLines'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LabelEncoder()\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([0, 0, 1, 2], dtype=int64)"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn import preprocessing\n",
    "# le = preprocessing.LabelEncoder()\n",
    "# le.fit([1,2,2,6])\n",
    "# print(le)\n",
    "# le.classes_ #array([1,2,6])\n",
    "# le.transform([1,1,2,6])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "0       1\n1       1\n2       1\n3       0\n4       0\n       ..\n7022    0\n7023    1\n7024    0\n7025    0\n7026    1\nName: gender, Length: 7027, dtype: int32"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "le.fit(df['gender'])\n",
    "df['gender'] = le.transform(df['gender'])\n",
    "df['gender'] # 남자는 1, 여자는 0이 되도록 나오게 됨"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "2I33ocSP0FUJ",
    "outputId": "20b97dae-8203-42a9-936f-0b65a2b24d7c",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  Partner Dependents     MultipleLines InternetService OnlineSecurity  \\\n0      No         No                No             DSL            Yes   \n1      No         No                No             DSL            Yes   \n2      No         No  No phone service             DSL            Yes   \n\n  OnlineBackup TechSupport StreamingTV StreamingMovies        Contract  \\\n0           No          No          No              No        One year   \n1          Yes          No          No              No  Month-to-month   \n2           No         Yes          No              No        One year   \n\n  PaperlessBilling              PaymentMethod  \n0               No               Mailed check  \n1              Yes               Mailed check  \n2               No  Bank transfer (automatic)  ",
      "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>Partner</th>\n      <th>Dependents</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>No</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Object 컬럼 확인\n",
    "df.select_dtypes('object').head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "fRuWYDVt0FUJ",
    "outputId": "1bcc193d-3cf8-4c86-8fb5-9b43767fc1a0",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Partner', 'Dependents', 'MultipleLines', 'InternetService',\n       'OnlineSecurity', 'OnlineBackup', 'TechSupport', 'StreamingTV',\n       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod'],\n      dtype=object)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Object 컬럼명 수집\n",
    "\n",
    "cal_cols = df.select_dtypes('object').columns.values\n",
    "cal_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4ZK1acm0FUK"
   },
   "source": [
    "##### <font color=blue> **[문제] Object 컬럼에 대해 One-Hot-Encoding 수행하고 그 결과를 df1 변수에 저장하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "nrO_PFhQ0FUK"
   },
   "outputs": [],
   "source": [
    "# Pandas get_dummies() 함수 이용\n",
    "# 원-핫-인코딩 결과를 df1 저장\n",
    "# df1 = pd.get_dummies(data=df, columns=cal_cols)\n",
    "\n",
    "df1 = pd.get_dummies(data=df, columns=cal_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "LKHLRV220FUK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7027 entries, 0 to 7026\n",
      "Data columns (total 39 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   gender                                   7027 non-null   int32  \n",
      " 1   tenure                                   7027 non-null   int64  \n",
      " 2   MonthlyCharges                           7027 non-null   float64\n",
      " 3   TotalCharges                             7027 non-null   float64\n",
      " 4   Churn                                    7027 non-null   int64  \n",
      " 5   Partner_No                               7027 non-null   uint8  \n",
      " 6   Partner_Yes                              7027 non-null   uint8  \n",
      " 7   Dependents_No                            7027 non-null   uint8  \n",
      " 8   Dependents_Yes                           7027 non-null   uint8  \n",
      " 9   MultipleLines_No                         7027 non-null   uint8  \n",
      " 10  MultipleLines_No phone service           7027 non-null   uint8  \n",
      " 11  MultipleLines_Yes                        7027 non-null   uint8  \n",
      " 12  InternetService_DSL                      7027 non-null   uint8  \n",
      " 13  InternetService_Fiber optic              7027 non-null   uint8  \n",
      " 14  InternetService_No                       7027 non-null   uint8  \n",
      " 15  OnlineSecurity_No                        7027 non-null   uint8  \n",
      " 16  OnlineSecurity_No internet service       7027 non-null   uint8  \n",
      " 17  OnlineSecurity_Yes                       7027 non-null   uint8  \n",
      " 18  OnlineBackup_No                          7027 non-null   uint8  \n",
      " 19  OnlineBackup_No internet service         7027 non-null   uint8  \n",
      " 20  OnlineBackup_Yes                         7027 non-null   uint8  \n",
      " 21  TechSupport_No                           7027 non-null   uint8  \n",
      " 22  TechSupport_No internet service          7027 non-null   uint8  \n",
      " 23  TechSupport_Yes                          7027 non-null   uint8  \n",
      " 24  StreamingTV_No                           7027 non-null   uint8  \n",
      " 25  StreamingTV_No internet service          7027 non-null   uint8  \n",
      " 26  StreamingTV_Yes                          7027 non-null   uint8  \n",
      " 27  StreamingMovies_No                       7027 non-null   uint8  \n",
      " 28  StreamingMovies_No internet service      7027 non-null   uint8  \n",
      " 29  StreamingMovies_Yes                      7027 non-null   uint8  \n",
      " 30  Contract_Month-to-month                  7027 non-null   uint8  \n",
      " 31  Contract_One year                        7027 non-null   uint8  \n",
      " 32  Contract_Two year                        7027 non-null   uint8  \n",
      " 33  PaperlessBilling_No                      7027 non-null   uint8  \n",
      " 34  PaperlessBilling_Yes                     7027 non-null   uint8  \n",
      " 35  PaymentMethod_Bank transfer (automatic)  7027 non-null   uint8  \n",
      " 36  PaymentMethod_Credit card (automatic)    7027 non-null   uint8  \n",
      " 37  PaymentMethod_Electronic check           7027 non-null   uint8  \n",
      " 38  PaymentMethod_Mailed check               7027 non-null   uint8  \n",
      "dtypes: float64(2), int32(1), int64(2), uint8(34)\n",
      "memory usage: 480.5 KB\n"
     ]
    }
   ],
   "source": [
    "# 모든 컬럼 데이터가 숫자되어 있음을 확인\n",
    "# 40컬럼, 7,027 라인\n",
    "df1.info() # columns 수가 39개로 19개 증가함?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "jQV1XuM50FUL",
    "outputId": "2bc7170d-fa08-4681-87f3-b3b297e23105",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   gender  tenure  MonthlyCharges  TotalCharges  Churn  Partner_No  \\\n0       1      34           56.95       1889.50      0           1   \n1       1       2           53.85        108.15      1           1   \n2       1      45           42.30       1840.75      0           1   \n3       0       2           70.70        151.65      1           1   \n4       0       8           99.65        820.50      1           1   \n5       1      22           89.10       1949.40      0           1   \n6       0      10           29.75        301.90      0           1   \n7       0      28          104.80       3046.05      1           0   \n8       1      62           56.15       3487.95      0           1   \n9       1      13           49.95        587.45      0           0   \n\n   Partner_Yes  Dependents_No  Dependents_Yes  MultipleLines_No  ...  \\\n0            0              1               0                 1  ...   \n1            0              1               0                 1  ...   \n2            0              1               0                 0  ...   \n3            0              1               0                 1  ...   \n4            0              1               0                 0  ...   \n5            0              0               1                 0  ...   \n6            0              1               0                 0  ...   \n7            1              1               0                 0  ...   \n8            0              0               1                 1  ...   \n9            1              0               1                 1  ...   \n\n   StreamingMovies_Yes  Contract_Month-to-month  Contract_One year  \\\n0                    0                        0                  1   \n1                    0                        1                  0   \n2                    0                        0                  1   \n3                    0                        1                  0   \n4                    1                        1                  0   \n5                    0                        1                  0   \n6                    0                        1                  0   \n7                    1                        1                  0   \n8                    0                        0                  1   \n9                    0                        1                  0   \n\n   Contract_Two year  PaperlessBilling_No  PaperlessBilling_Yes  \\\n0                  0                    1                     0   \n1                  0                    0                     1   \n2                  0                    1                     0   \n3                  0                    0                     1   \n4                  0                    0                     1   \n5                  0                    0                     1   \n6                  0                    1                     0   \n7                  0                    0                     1   \n8                  0                    1                     0   \n9                  0                    0                     1   \n\n   PaymentMethod_Bank transfer (automatic)  \\\n0                                        0   \n1                                        0   \n2                                        1   \n3                                        0   \n4                                        0   \n5                                        0   \n6                                        0   \n7                                        0   \n8                                        1   \n9                                        0   \n\n   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n0                                      0                               0   \n1                                      0                               0   \n2                                      0                               0   \n3                                      0                               1   \n4                                      0                               1   \n5                                      1                               0   \n6                                      0                               0   \n7                                      0                               1   \n8                                      0                               0   \n9                                      0                               0   \n\n   PaymentMethod_Mailed check  \n0                           1  \n1                           1  \n2                           0  \n3                           0  \n4                           0  \n5                           0  \n6                           1  \n7                           0  \n8                           0  \n9                           1  \n\n[10 rows x 39 columns]",
      "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>gender</th>\n      <th>tenure</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n      <th>Partner_No</th>\n      <th>Partner_Yes</th>\n      <th>Dependents_No</th>\n      <th>Dependents_Yes</th>\n      <th>MultipleLines_No</th>\n      <th>...</th>\n      <th>StreamingMovies_Yes</th>\n      <th>Contract_Month-to-month</th>\n      <th>Contract_One year</th>\n      <th>Contract_Two year</th>\n      <th>PaperlessBilling_No</th>\n      <th>PaperlessBilling_Yes</th>\n      <th>PaymentMethod_Bank transfer (automatic)</th>\n      <th>PaymentMethod_Credit card (automatic)</th>\n      <th>PaymentMethod_Electronic check</th>\n      <th>PaymentMethod_Mailed check</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>34</td>\n      <td>56.95</td>\n      <td>1889.50</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>45</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>2</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>8</td>\n      <td>99.65</td>\n      <td>820.50</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>22</td>\n      <td>89.10</td>\n      <td>1949.40</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>10</td>\n      <td>29.75</td>\n      <td>301.90</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>28</td>\n      <td>104.80</td>\n      <td>3046.05</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>62</td>\n      <td>56.15</td>\n      <td>3487.95</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>13</td>\n      <td>49.95</td>\n      <td>587.45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S_vS5S6s0FUL"
   },
   "source": [
    "## ⑤ Train, Test  데이터셋 분할"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6-OtRis00FUL"
   },
   "source": [
    "#### 입력(X)과 레이블(y) 나누기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E3uUWsZA0FUM"
   },
   "source": [
    "##### <font color=blue> **[문제] df1 DataFrame에서 'Churn' 컬럼을 제외한 나머지 정보를 X에 저장하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "sOUAhknI0FUM"
   },
   "outputs": [],
   "source": [
    "# DataFrame drop 함수 활용\n",
    "# 'Churn' 컬럼 삭제\n",
    "# DataFrame에서 values만 X에 저장\n",
    "# df1.head()\n",
    "# a = df1.drop('Churn', axis=1).values\n",
    "\n",
    "X = df1.drop('Churn', axis=1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZaSeXul50FUM"
   },
   "source": [
    "##### <font color=blue> **[문제] df DataFrame에서 'Churn' 컬럼을 y로 저장하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "VHOezJtl0FUM"
   },
   "outputs": [],
   "source": [
    "# DataFrame 'Churn' 컬럼 사용\n",
    "# # DataFrame에서 values만 y에 저장\n",
    "# Y = df1['Churn'].values\n",
    "\n",
    "Y = df1['Churn'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "YjmW-mzK0FUM",
    "outputId": "32aa0f24-1497-4160-ebcf-f7ce3a71ee46",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "((7027, 38), (7027,))"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfmrbAUZ0FUN"
   },
   "source": [
    "#### Train , Test dataset 나누기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "IzvqcVmx0FUN"
   },
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5-pMRPi50FUN"
   },
   "source": [
    "##### <font color=blue> **[문제] Train dataset, Test dataset 나누세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "J9Tpju4m0FUN"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "((4918, 38), (2109, 38))"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train dataset, Test dataset 나누기 : train_test_split 함수 사용\n",
    "# 입력 : X, y         분류모델이 꼭 들어가야 함\n",
    "# Train : Test 비율 = 7: 3  --> test_size=0.3\n",
    "# y Class 비율에 맞게 나누기 : stratify=y\n",
    "# 여러번 수행해도 같은 결과 나오게 고정하기 : random_state=42 \n",
    "# 결과 : X_train, X_test, y_train, y_test\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, stratify=Y, random_state=42)\n",
    "\n",
    "X_train.shape, X_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "8ylQ-3700FUO",
    "outputId": "a04d9749-fb3c-4ed9-feda-cc18eda784ff",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(4918, 38)"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "rg0_4PEk0FUO",
    "outputId": "3abd3937-ba2b-4116-96c2-9f28a679ffa3",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(4918,)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6dcPNr_E0FUO"
   },
   "source": [
    "## ⑥ 데이터 정규화/스케일링(Normalizing/Scaling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "mHLW-CRf0FUP",
    "outputId": "9fbcc30f-41cf-4e84-d0e6-0b730d4400c9",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      gender  tenure  MonthlyCharges  TotalCharges  Churn  Partner_No  \\\n7017       1       1           75.75         75.75      1           1   \n7018       1      38           69.50       2625.25      0           1   \n7019       0      67          102.95       6886.25      1           1   \n7020       1      19           78.70       1495.10      0           1   \n7021       0      12           60.65        743.30      0           1   \n7022       0      72           21.15       1419.40      0           1   \n7023       1      24           84.80       1990.50      0           0   \n7024       0      72          103.20       7362.90      0           0   \n7025       0      11           29.60        346.45      0           0   \n7026       1       4           74.40        306.60      1           0   \n\n      Partner_Yes  Dependents_No  Dependents_Yes  MultipleLines_No  ...  \\\n7017            0              1               0                 0  ...   \n7018            0              1               0                 1  ...   \n7019            0              1               0                 0  ...   \n7020            0              1               0                 1  ...   \n7021            0              1               0                 0  ...   \n7022            0              1               0                 1  ...   \n7023            1              0               1                 0  ...   \n7024            1              0               1                 0  ...   \n7025            1              0               1                 0  ...   \n7026            1              1               0                 0  ...   \n\n      StreamingMovies_Yes  Contract_Month-to-month  Contract_One year  \\\n7017                    0                        1                  0   \n7018                    0                        1                  0   \n7019                    0                        1                  0   \n7020                    0                        1                  0   \n7021                    1                        0                  1   \n7022                    0                        0                  0   \n7023                    1                        0                  1   \n7024                    1                        0                  1   \n7025                    0                        1                  0   \n7026                    0                        1                  0   \n\n      Contract_Two year  PaperlessBilling_No  PaperlessBilling_Yes  \\\n7017                  0                    0                     1   \n7018                  0                    0                     1   \n7019                  0                    0                     1   \n7020                  0                    0                     1   \n7021                  0                    1                     0   \n7022                  1                    0                     1   \n7023                  0                    0                     1   \n7024                  0                    0                     1   \n7025                  0                    0                     1   \n7026                  0                    0                     1   \n\n      PaymentMethod_Bank transfer (automatic)  \\\n7017                                        0   \n7018                                        0   \n7019                                        0   \n7020                                        1   \n7021                                        0   \n7022                                        1   \n7023                                        0   \n7024                                        0   \n7025                                        0   \n7026                                        0   \n\n      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n7017                                      0                               1   \n7018                                      1                               0   \n7019                                      1                               0   \n7020                                      0                               0   \n7021                                      0                               1   \n7022                                      0                               0   \n7023                                      0                               0   \n7024                                      1                               0   \n7025                                      0                               1   \n7026                                      0                               0   \n\n      PaymentMethod_Mailed check  \n7017                           0  \n7018                           0  \n7019                           0  \n7020                           0  \n7021                           0  \n7022                           0  \n7023                           1  \n7024                           0  \n7025                           0  \n7026                           1  \n\n[10 rows x 39 columns]",
      "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>gender</th>\n      <th>tenure</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n      <th>Partner_No</th>\n      <th>Partner_Yes</th>\n      <th>Dependents_No</th>\n      <th>Dependents_Yes</th>\n      <th>MultipleLines_No</th>\n      <th>...</th>\n      <th>StreamingMovies_Yes</th>\n      <th>Contract_Month-to-month</th>\n      <th>Contract_One year</th>\n      <th>Contract_Two year</th>\n      <th>PaperlessBilling_No</th>\n      <th>PaperlessBilling_Yes</th>\n      <th>PaymentMethod_Bank transfer (automatic)</th>\n      <th>PaymentMethod_Credit card (automatic)</th>\n      <th>PaymentMethod_Electronic check</th>\n      <th>PaymentMethod_Mailed check</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7017</th>\n      <td>1</td>\n      <td>1</td>\n      <td>75.75</td>\n      <td>75.75</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7018</th>\n      <td>1</td>\n      <td>38</td>\n      <td>69.50</td>\n      <td>2625.25</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7019</th>\n      <td>0</td>\n      <td>67</td>\n      <td>102.95</td>\n      <td>6886.25</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7020</th>\n      <td>1</td>\n      <td>19</td>\n      <td>78.70</td>\n      <td>1495.10</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7021</th>\n      <td>0</td>\n      <td>12</td>\n      <td>60.65</td>\n      <td>743.30</td>\n      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<td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7024</th>\n      <td>0</td>\n      <td>72</td>\n      <td>103.20</td>\n      <td>7362.90</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7025</th>\n      <td>0</td>\n      <td>11</td>\n      <td>29.60</td>\n      <td>346.45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7026</th>\n      <td>1</td>\n      <td>4</td>\n      <td>74.40</td>\n      <td>306.60</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 숫자 분포 이루어진 컬럼 확인\n",
    "df1.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "Q-orGOfi0FUP"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YYL7jH_z0FUP"
   },
   "source": [
    "##### <font color=blue> **[문제] MinMaxScaler 함수를 'scaler'로 정의 하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "ml2_SprW0FUQ"
   },
   "outputs": [],
   "source": [
    "# 사이키런의 MinMaxScaler() 함수 활용\n",
    "# 정의할 결과를 'scaler'로 매핑\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# scaler = MinMaxScaler()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "fPZhJv7J0FUQ"
   },
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train) # 데이터를 통해 학습도 하고 X train에 저장하겠다\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "tiM5HomM0FUQ",
    "outputId": "e70a43fc-b274-4f7b-93f9-6e32e595bbc2",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(array([[0.        , 0.65277778, 0.56851021, 0.40877722, 1.        ,\n         0.        , 1.        , 0.        , 1.        , 0.        ,\n         0.        , 0.        , 1.        , 0.        , 0.        ,\n         0.        , 1.        , 1.        , 0.        , 0.        ,\n         1.        , 0.        , 0.        , 1.        , 0.        ,\n         0.        , 1.        , 0.        , 0.        , 1.        ,\n         0.        , 0.        , 1.        , 0.        , 0.        ,\n         1.        , 0.        , 0.        ],\n        [0.        , 0.27777778, 0.00498256, 0.04008671, 1.        ,\n         0.        , 1.        , 0.        , 1.        , 0.        ,\n         0.        , 0.        , 0.        , 1.        , 0.        ,\n         1.        , 0.        , 0.        , 1.        , 0.        ,\n         0.        , 1.        , 0.        , 0.        , 1.        ,\n         0.        , 0.        , 1.        , 0.        , 1.        ,\n         0.        , 0.        , 0.        , 1.        , 0.        ,\n         1.        , 0.        , 0.        ]]),\n array([0, 0], dtype=int64))"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:2], Y_train[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v5XRNq100FUQ"
   },
   "source": [
    "## ⑦ 모델 개발"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yt9pqvyOGW0N"
   },
   "source": [
    "#### (참고) 모델별 바차트 그려주고 성능 확인을 위한 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "gwA1iqfDGWkw"
   },
   "outputs": [],
   "source": [
    "# 모델별로 Recall 점수 저장\n",
    "# 모델 Recall 점수 순서대로 바차트를 그려 모델별로 성능 확인 가능\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "my_predictions = {}\n",
    "\n",
    "colors = ['r', 'c', 'm', 'y', 'k', 'khaki', 'teal', 'orchid', 'sandybrown',\n",
    "          'greenyellow', 'dodgerblue', 'deepskyblue', 'rosybrown', 'firebrick',\n",
    "          'deeppink', 'crimson', 'salmon', 'darkred', 'olivedrab', 'olive', \n",
    "          'forestgreen', 'royalblue', 'indigo', 'navy', 'mediumpurple', 'chocolate',\n",
    "          'gold', 'darkorange', 'seagreen', 'turquoise', 'steelblue', 'slategray', \n",
    "          'peru', 'midnightblue', 'slateblue', 'dimgray', 'cadetblue', 'tomato'\n",
    "         ]\n",
    "\n",
    "# 모델명, 예측값, 실제값을 주면 위의 plot_predictions 함수 호출하여 Scatter 그래프 그리며\n",
    "# 모델별 MSE값을 Bar chart로 그려줌\n",
    "def recall_eval(name_, pred, actual):\n",
    "    global predictions\n",
    "    global colors\n",
    "\n",
    "    plt.figure(figsize=(12, 9))\n",
    "\n",
    "    #acc = accuracy_score(actual, pred)\n",
    "    acc = recall_score(actual, pred)\n",
    "    my_predictions[name_] = acc * 100\n",
    "\n",
    "    y_value = sorted(my_predictions.items(), key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    df = pd.DataFrame(y_value, columns=['model', 'recall'])\n",
    "    print(df)\n",
    "   \n",
    "    length = len(df)\n",
    "    \n",
    "    plt.figure(figsize=(10, length))\n",
    "    ax = plt.subplot()\n",
    "    ax.set_yticks(np.arange(len(df)))\n",
    "    ax.set_yticklabels(df['model'], fontsize=15)\n",
    "    bars = ax.barh(np.arange(len(df)), df['recall'])\n",
    "    \n",
    "    for i, v in enumerate(df['recall']):\n",
    "        idx = np.random.choice(len(colors))\n",
    "        bars[i].set_color(colors[idx])\n",
    "        ax.text(v + 2, i, str(round(v, 3)), color='k', fontsize=15, fontweight='bold')\n",
    "        \n",
    "    plt.title('recall', fontsize=18)\n",
    "    plt.xlim(0, 100)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aWIdyWeg0FUR"
   },
   "source": [
    "###  \n",
    "### 1) 로지스틱 회귀 (LogisticRegression, 분류)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "L3x-qaHh0FUR"
   },
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.metrics import confusion_matrix\n",
    "# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "# from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2shWVpg00FUS"
   },
   "source": [
    "##### <font color=blue> **[문제] LogisticRegression 모델 정의하고 학습시키세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "qz_VjrEf0FUS",
    "outputId": "b24a00bb-8300-4b2e-b9d6-21ba486c7730",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression()",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LogisticRegression 함수 사용 및 정의 : lg 저장\n",
    "# 정의된 LogisticRegression 학습 fit() : 입력값으로 X_train, y_train 준다.\n",
    "# lg = LogisticRegression()\n",
    "# lg.fit(X_train, Y_train)\n",
    "\n",
    "lg = LogisticRegression()\n",
    "lg.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "mW7KXzVL0FUS",
    "outputId": "6f17a240-d5db-4fe2-f0ca-b59ac4395733",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.8060692271218587"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 분류기 성능 평가(score)\n",
    "lg.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WjYV0iRS0FUS"
   },
   "source": [
    "- 분류기 성능 평가 지표"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "Cu1wy0Wt0FUS"
   },
   "outputs": [],
   "source": [
    "lg_pred = lg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "2DaZFSRr0FUT",
    "outputId": "ea84ed8c-db49-4f99-9744-39e3f4327789",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1386,  163],\n       [ 246,  314]], dtype=int64)"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오차행렬\n",
    "# TN  FP\n",
    "# FN  TP\n",
    "\n",
    "confusion_matrix(Y_test, lg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "GYM_m9JA0FUT",
    "outputId": "1e530416-548e-4417-b901-93270a1f0bde",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.8060692271218587"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정확도\n",
    "accuracy_score(Y_test, lg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "mNorqDIl0FUT",
    "outputId": "ab079fd0-9dc8-4528-dff9-dca5e99c4560",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.6582809224318659"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정밀도\n",
    "precision_score(Y_test, lg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "XQ-B-7U40FUT",
    "outputId": "f9c18954-57fa-4992-f8cf-25c5c5edafe5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.5607142857142857"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 재현율 : 낮다. \n",
    "recall_score(Y_test, lg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "KLMt1bRZ0FUU",
    "outputId": "05b01987-1c85-4471-bad4-0925163923bd",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.6055930568948892"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정밀도 + 재현율\n",
    "f1_score(Y_test, lg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "Nw8sNOr90FUU",
    "outputId": "1db6cd44-1c7e-448d-960a-a43be76cb9ce",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.89      0.87      1549\n",
      "           1       0.66      0.56      0.61       560\n",
      "\n",
      "    accuracy                           0.81      2109\n",
      "   macro avg       0.75      0.73      0.74      2109\n",
      "weighted avg       0.80      0.81      0.80      2109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, lg_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "gV4EhffS0FUU",
    "outputId": "c0ea60e6-825a-4eb1-a4c0-da48a4b639c0",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                model     recall\n",
      "0  LogisticRegression  56.071429\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x100 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recall_eval('LogisticRegression', lg_pred, Y_test)\n",
    "recall_eval('LogisticRegression', lg_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sjZAmlXp0FUU"
   },
   "source": [
    "###   \n",
    "### 2) KNN (K-Nearest Neighbor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "dTLMf6Og0FUU"
   },
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "JCAJy-pT0FUV",
    "outputId": "675ba845-6511-4cab-f235-71a33d8c37e2",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier()",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn = KNeighborsClassifier(n_neighbors=5)\n",
    "# knn.fit(X_train, Y_train)\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "RVPSryWO0FUV"
   },
   "outputs": [],
   "source": [
    "# knn_pred = knn.predict(X_test)\n",
    "knn_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "5LcFquWj0FUV",
    "outputId": "56d60c35-5b56-487d-d647-f090b69c1d72",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                model     recall\n",
      "0  LogisticRegression  56.071429\n",
      "1  K-Nearest Neighbor  51.071429\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recall_eval('K-Nearest Neighbor', knn_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GkXh4Tgu0FUV"
   },
   "source": [
    "###  \n",
    "### 3) 결정트리(DecisionTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "PEPkbbz80FUV"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "AfIDKCCL0FUV",
    "outputId": "1d0b38b2-2220-4074-9968-d46c59888024",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(max_depth=10, random_state=42)",
      "text/html": "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=10, random_state=42)</pre></div></div></div></div></div>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=10, random_state=42)\n",
    "dt.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qEMaN520FUW"
   },
   "source": [
    "##### <font color=blue> **[문제] 학습된 DecisionTreeClassifier 모델로 예측해 보기** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "_Csk9MqD0FUW"
   },
   "outputs": [],
   "source": [
    "# DecisionTreeClassifier 학습 모델 : dt\n",
    "# DecisionTreeClassifier 모델의 predict() 활용 : 입력값으로 X_test\n",
    "# # 결과 : dt_pred 저장\n",
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# dt = DecisionTreeClassifier(random_state=11)\n",
    "# dt.fit((X_train, Y_train))\n",
    "\n",
    "dt_pred = dt.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true,
    "id": "qhgIeDvs0FUW",
    "outputId": "0df600cc-8607-46f9-8198-effe2b63daa1",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                model     recall\n",
      "0  LogisticRegression  56.071429\n",
      "1        DecisionTree  54.642857\n",
      "2  K-Nearest Neighbor  51.071429\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x300 with 1 Axes>",
      "image/png": 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qweffIyMjNWPGDDVv3lwuLi4qVqyYKlWqpN69e+vkyZNGZeOejd+/f7+aNm2qYsWKqVq1arp2LfaX1KNHj+Tj46O6deuqePHiKl++vLp166ZDhw7Fq/fq1avq16+foY1ubm7q1auXgoKCjOpr27atJGnlypVydnaWn5/fK12DMWPGyMrKSlOmTEnSUPezZ8/q66+/lpubm4oVK6bq1atr3Lhxun//vlG5xJ5BP3DggDp16iRXV1eVKVNGnp6eOnfunGrWrJnoM+/Lli1T48aNVaJECVWoUEF9+vTR1asJf/t/7949DRgwQOXLl1fp0qXVunVrbdu2LcGy//zzjzw9PeXq6qpixYqpZs2aGjduXLzJbOI+y+bNm9W+fXsVK1ZMlStXTvDPDQCAlNSwYW21atVMXbu2140b/44wXL/+32Htrq4fJ1t95cq5Gt5v27bZ8H7Hjn9/9z5fX0REhGG2+SJFiip16tTJ1h4A+C96o4A+cuRIzZ49WwUKFNCcOXPk6Oj42udKnTq1xowZI3Nz81ce6n7r1i01bdpUvr6+cnJyUtu2beXm5qb169eradOm2r9/v6FsdHS0OnfurLFjx8rc3FxNmzZVy5Yt5ejoqNWrV6tly5a6efNmvDp69eolGxsbtWnTRsWLF5eTk5PCw8PVunVrTZo0STY2NnJ3d1fNmjW1b98+tWrVSmvXrjUcf/fuXbVt21Zr1qxRqVKl1KFDB1WoUEGbNm1Sq1atdOLECUlSjRo11LhxY0lSwYIF5eXlpcKFC7/StcuXL5969uyZpKHuu3fvVtOmTbV27VqVK1dOHh4eyp07t3x9ffXFF1/o7t27Lzx+y5YtatOmjQIDA1WpUiU1b95cp0+fVsuWLROd4XXJkiUaPHiwcuXKpdatWytbtmzy9/dX69atFR4eHq+8l5eXduzYoUaNGqlmzZoKCgpSly5dtHz5cqNyS5culbu7u3bs2KGPP/5Y7u7usrOzk6+vrz7//PME/1wHDRqk+/fvq23btipUqNArX2sAAN4Fc3NzNWvWXFLsDYamTRto7txZmjjxRw0cGLucmaWlpbp180ryuYODg+Xvv0r+/qu0f/9ew/amTZsbZm///vuhmjdvtoYPH6ytW2PDeqVKnyhHjpxG5zp37qzhGfXixUsluS0AAGOvvczaqFGjNHv2bElSoUKFlClTpjduTJkyZdSuXTvNmDFDgwYN0h9//PHC8sOHD9fly5f1/fff64svvjBs79Chg5o3b65vvvlG69atk6WlpdavX6/AwEA1adJEY8aMMTpPnz595O/vrw0bNsjd3d1oX86cOTVz5kyj5UYmTpyoI0eOqFu3burTp49he48ePdS0aVMNGDBArq6usrOzk7+/v65cuaKRI0eqWbNmhrKffPKJ+vXrp3nz5mnEiBGqUaOG0qdPr2XLlsnZ2Vk9e/ZM0rXr2LGj1q9fr8OHD+v3339X165dEy0bGRmpvn37SpIWLVpkNPnb/PnzNWzYMI0aNUre3t4JHh8REaHBgwdLkubOnasSJUpIiv0yo23btjp8+LDSp08f77iQkBDNmjVLZcuWlRT7pUnr1q21f/9+bd++XbVq1TIqnzZtWi1cuFB2dnaSpPbt26tVq1b6/vvvVaNGDaVLl07Xrl3TsGHDZGtrq5kzZ6pQoUKSYlcUmDhxon799VcNGjRI06dPNzq3tbW15s+fzzf9AACTNWDAYAUE7NTx48d06tRJ9enzbxg3MzPTmDE/qmjRYkk+74kTx+Th0UqS9NlndTVr1nxJUqFChdWnzzfy9h6nGzeuq3fvHoZj0qe31ahRP8Q715Urlw3vs2XLFm8/ACBpXusO+sKFCzVr1iyVKFFCGTNm1MqVK+Pd1XxdvXv3fqWh7nfu3NHGjRtVvHhxo3AuSYULF1bDhg119epVw4zozs7OGj16tHr16hXvXBUqVJCkBO8a16pVyyicP336VEuXLlXmzJnjnSvuLn5YWJj8/f0lyTDJyqFDh/T48WND2Tp16mjDhg0aMmTIC6/Hq7KwsDAMdX/ZrO6bNm3S7du31bJly3gzs7ds2VK5c+fWmjVrjJ5xe9bOnTt169Ytw1D1ODY2NurXr1+i9VauXNkQzqXYuwNxQ+ETWlLtyy+/NIRzKfbPtUWLFgoNDdXGjbHPu/3111+KiopSx44dDeFciv2HS8+ePZUrVy5t27Yt3oiMatWqEc4BAG+VmSQzs9d/xX7Rv0G9e/dVvnz5ZWVlpQwZ7FS1anUtW7ZKHh4dEj3WqB0v2P/89v79B8rHZ5pKliwta2trZchgp9q168rff72KFCkSr3xY2EPDuRwcHF74eZ6v70Xt48XrTV70K17v6vU2vNYd9Nu3b6tMmTKaPn269u7dq27dumn48OEqVarUa83e/ixra2uNGTNG7u7uGjt2rCpVqqSsWbPGK3f06FHFxMQoKioqwXXZb9y4IUk6duyYqlSpoty5cyt37tx6/Pixjh07pgsXLujKlSs6deqUdu/eLSn+siKS4k14d/78eYWGhsrW1lY+Pj7xyl+4cMFQryR99tln8vHx0eLFi7Vu3TpVqFBBlSpVUuXKlV9rMr0XyZ8/v3r27Clvb2/1799fixYtireWqSQdOXLE8FkSunYWFhZ68uSJTp48aRSo4xw+fFiSVKpUqXj7SpcunWCdkhLsGw4ODpKU4BB3FxeXeNvi6jx27JgaNmxouM7ly5ePV9bS0lKlS5fWxYsXdezYMaN+lNzXHgCA59nZp1WmTPFHlCVFpkzpNWHCeE2YMD5Jxz07C/vzGjas88L9np6d5enZ+ZXq6dq1o7p27fjK7cqYMf0rtQF4U3F9DXjfvFZAL126tH7//XelTZtWVatWVfPmzbVw4UJ99dVXWrBggaysrAxlAwMDtWfPnnjnaNeunWxtbRM8f5kyZdS2bVv9+eefiQ51j5vI7OTJk/EmeEuoXExMjGbMmKE//vhDd+7ckSSlSZNGRYoUUeHChXX79u0Ef1E8v5Zn3PmuXbumKVOmvLTezJkzy8/PT7/99ps2bNigtWvXGp5Rr1ChgoYNG6Y8efIkep6k6tixo9atW6cjR45o+vTp6tatW7wyDx48kBR7J33Tpk0v/QzPu3fvnqTYz/Y8S0tLZcyYMcHjXnTHOqFrn9BjE2nTppX0b6B/+DD2m/uEhtRLMsyLEBERYbSdNVoBAG9byL0wWdx5+PKC/wFmZrGBKTj4ocjleJvoa3hXzM0lB4fk/yLotQK6m5ubIShJsTOQBwYG6ujRoxo/frwGDhxo2Ldnz54Eg2zjxo0TDehS7HPhW7ZsSXSoe1z9LVu21LBhw17a5pkzZ2rcuHEqWrSohg8frkKFCilbtmwyMzPT/PnzE50dPLF6P/nkk3jPNScma9asGjZsmIYOHapTp04pICBAq1at0u7du9WtWzetWbNGZsk0RsLCwkJjx45V48aNNWXKFFWvXj3RzzBhwgTVqVMnyXXEHZ/YEPiwsDCjoemv6+HDh8qQIYPRtrgJ3+K2xy0vc/PmTeXLly/eOeK+ZEiO9gAAkBQxEgHhOTExXBO8G/Q1vG1vq38lyzroadKk0Q8//CBLS0vNmjVLmzf/uzRHz549DXe5n31lz579hee0trbW6NGjDbO6P7sepyTDrNtxw62f9/fff2vixImGWdLjnpGfNGmSatSooezZsxtC8ZkzZ175s+bNm1fW1tY6fvy4oqKi4u3fuXOnvL29tW/fPkmSv7+/hg4dqocPH8rMzEzOzs7y8PDQwoULlTt3bl24cMHw2ZIrpOfPn19eXl56/Pix+vfvH2//y66dj4+Ppk6dmuhs7MWLF5cUu8za806dOqWwsLDXbLmxuKH4z4q7rnFtiHuGfu/evfHKSrFfEJmZmalAgfjrtgIAAACAKUmWgC5JJUuWNAynHjBgQIJLWyVV2bJl1bZtW4WGhhqe7Y7j5OQkNzc3HT16VDNnzjTad+7cOQ0fPlzTpk0z3GG1traWFLu0yLP27NljuEP/7CRuibGyslKDBg10+/Zt/fTTT4qOjjbsu3PnjoYMGaJp06YZtp04cUILFizQnDlzjM7z4MEDhYSEKG3atLK3t5cUOzxckmG5kjfRqVMnFS9e3Git9Tg1atSQnZ2d5s6dG2/9b39/f02aNEnLly9PdIRDtWrV5ODgoCVLlhieAZdi14b/4Yf4M7y+rl9++UWPHj0y/Lxv3z799ddfypIli6pWrSpJatCggVKlSqWZM2cavoyJ4+Pjo3PnzsnNze2NlgAEAAAAgHfhtZdZS4inp6e2bdumw4cPq2/fvvGWJ3sdcUPdnw/okvT999/L3d1do0eP1tq1a1WyZEmFhIRozZo1Cg8P18CBAw136ps0aaIDBw6oc+fOql27tmxtbXXixAnt2rVL9vb2ioyMTPSO8fP69eungwcPasaMGdq9e7fKlSunyMhIrVu3Tvfu3VP79u0NE5y1a9dOq1at0sSJExUYGKgiRYooIiJC69evV0hIiAYPHmx4Zj9uErNt27Zp3Lhxql69eoITpb2KuFndmzRpEu9Of7p06fTDDz/Iy8tLLVu2VLVq1ZQrVy6dPXtWW7duVZo0aQzrxSfExsZGI0aMUK9evdSiRQvVrFlT9vb22r59u+H59jf9c5diZ9Vv2LChqlWrplu3bmnt2rWytLTU+PHjDdcsW7ZsGjp0qIYMGaJmzZqpevXqcnR01IEDB3To0CFlz55dI0eOfOO2AAAAAMDblmx30CUZwlOaNGm0Z8+eBGc5T6pnh7o/L1u2bPLz85OHh4du3bql2bNna9u2bSpTpox8fX3Vtm1bQ9kvvvhCo0aNkpOTk1auXKnFixfr7t278vLy0urVq5UmTRpt27YtwZncn5c+fXotWLBAXl5eioqK0oIFC7R27Vrly5dPEyZMMBpWnjFjRs2bN0/u7u66fv265s6dq1WrVil//vyaOnWqWrdubSibNWtWff3117K2ttacOXO0a9euN7p2BQoUkJeXV4L7qlSposWLF+uzzz7TgQMHNHPmTJ06dUr169fXkiVLVLp06Reeu2bNmvL19VXJkiW1adMm+fn5qWDBgpoxY4ak5JmEbdasWXJ2dtbChQu1efNmubm5acGCBYZl8eI0a9ZMs2fPVsWKFbVr1y7NmzdPDx48ULdu3bR8+fIEVwEAAAAAAFNjFsMaF0ii0NBQPXz4UI6OjvG+OLlw4YJq1aqlKlWqGA31/5D8bGurqIfMygsAeLF6G7YpY4lSKd0Mk2BmFrtk3J07zKyNt4u+hnfF3PztLOeXrHfQ8d9w5coVffrpp+rRo0e8fb///rskqWLFiu+6WQAAAADwXkvWZ9Dx3+Ds7CwXFxdt2rRJTZs2Vbly5RQdHa29e/fq6NGjKlGihFq1apXSzQQAAACA9woBHUlmZmam6dOna86cOfr777+1aNEiRUdHK3v27Pryyy/VsWNHwyRuAAAAAIBXQ0DHa0mTJo26dOmiLl26pHRTAAAAAOCDwDPoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAAACaAgA4AAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOgAAAAAAJoCADgAAAACACSCgAwAAAABgAgjoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAAACaAgA4AAJDMLFKnVmqHjCndDADAe8YypRsAvG9q/7VGT58+Telm4ANmJsnOPq1C7oUpJqUbgw8afe3tSe2QUemy50jpZgAA3jMEdCCJHIoXV3R0SrcCHzIzMylTpvSyuPNQMaQmvEX0NQAATAtD3AEAAAAAMAEEdAAAAAAATAABHQAAAAAAE0BABwAAAADABBDQAQAAAAAwAQR0AAAAAABMAAEdAAAAAAATQEAHAAAAAMAEENABAAAAADABBHQAAAAAAEwAAR0AAAAAABNgmdINAN43568/0dOnKd0KfMjMzKS74VEKCXmimJiUbg0+ZPS1D4NtGnNlsuOeCwB8CAjoQBIN8w1V+CP+JYu37WFKNwD/GfS1910qS2lizwyEdAD4APA3OQAAwHvs8RPpQXh0SjcDAJAMCOgAAAAAAJgAAjoAAAAAACaAgA4AAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOgAAAAAAJoCADgAAAACACSCgAwAAAABgAgjoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAkSfPnz1GWLLaJvrZu3ZzgcVFRUapSpYKyZLFV2bLFklTnjRvX1a9fH5UpU1Q5cmSWq2spDR06UCEh91567BdfNDK07XnR0dGaNWuGPvusqnLnzqrcubOqXr3/aeXKFUlqHwC8S5Yp3QAAAACYhqCgw6913I8/jtXx48deo74jatasgYKDgw3bzp8/p6lTJysgYIdWrFgjGxubBI+dPftPbdmyKdFze3l11ZIlC4227dmzW3v27Fa/ft+pb9/+SW4vALxtBHQAAABIko4ciQ3oTk7ZNHr0+Hj7ixYtHm/bwYP/aMqUiUmuKyoqSl26eBjCeZs2Hvr4YzfNnv2nAgJ26uDBA5o3b5Y6duwa79grVy5r2LBBiZ47IGCnIZzny5dfPXv2UXBwsH78cYwiIiL0449j1aBBYxUs6JzkdgPA20RABwAAgGJiYnTs2FFJUrFixVWnTr2XHhMVFaVevTz15MmTJNe3dq2/zpw5LUlq3ryVvL1/liRVrlxFbdo0l6PjRzIzS/hpzD59vPTw4YNEz33gwD9Kly69QkMfatq0P1W8eAlJ0tWrl+XrO13R0dEKCNhJQAdgcgjoAAAA0IUL5/XgwX1JUv78BXX0aJBu3ryuXLlyK1++AgkeM378GJ04cVw2NjaysLBUaOjDV65v/fq1hveNGjUxvHd0/Ejr1m1N9LhZs2Zo69bNMjMzU8aMGXXnzp14Zbp376nu3Xvq2rWrcnLKZth+8+ZNw/uPPsr6ym0FgHeFSeIAAACgoKAjhvczZkxX1aoV1aLF5/r447Jq2LC2rl69YlT+2aHtAwYMlr29fZLqe/6Z9caN6ypnziwqVaqwRowYovDw8HjHPDu0vVOnripYsNAL64gL5ytXrlCvXp76+++/JEmlSpVW9eo1k9ReAHgXCOgAAABQUNAhw/uIiAijfQEBO9WoUR3DHfbIyEj16uWpp0+fysWlvLp06Z7k+u7cuW14361bJ+3cuV2PHj3StWtXNWXKRDVv3lhRUVFGx/Tu7aXQ0IfKmTO3vvtu6CvVEx0dLU/PjlqwYK4kqXr1mlq0aLksLRlICsD0ENBfkZ+fn5ydneO9ihUrpgoVKqh169ZasGCBnj59+k7aM3nyZDk7O2vx4sVJPjbus0yYMOEttMxYXDtf9eXn5/fW2wQAAOKLiHikDBnslDZtOk2e/KvOn7+urVt3GyaGu3jxgn799RdJ/w5tT506tSZN8pG5edL/SRkeHmZ4HxYWqp49+2jSJB8VKxb7vHhgYIB++83HUGbmTF9t2xY7tH3ChMlKmzbtK9Vz/fo1o6C/detmff/9UD1+/DjJbQaAt42vDpOoUKFCqlGjhuHniIgIBQcHa9euXRo6dKhWrVql6dOnJ7okSHIpX768vLy8VKRIkSQfW7hwYXl5ecnFxeUttMxYXDuftWHDBp04cULVq1dX4cKF47UNAAC8eyNGjNaIEaP16NEjWVtbS5IKFy6iceN+Ur16scPB161bo5o1a+mXXyZJkr755jsVKFDwtepLndra8L5jxy4aPHi4JKlatRpycSmuyMhILV26SD179tbly5c0fPhgSVLbth1UuXKVV67HwSGjjhw5JUvLVBo4sJ/8/BZr9uw/ZWdnb6gTAEwFAT2JChcurJ49e8bbHhYWpgEDBmjt2rX69ttv9fPPP7/Vdri6usrV1fW1ji1cuPA7C8IJtfPq1as6ceKEatSooSZNmiRyJAAASAlx4TxOsWL/Lq127dpVw9B2e3t75cmTV/7+qyRJERHhhv/6+6+Sg0NGVajwcaL12Nvb68aN65Kk8uX/Lefo+JHy5s2v48eP6ty5M5Kkb7/9SqGhD2VlZaVy5cob6rx799/10/39V8nGxkZVq1Y3qsfGxsZw48Tb+2etXLlcjx8/1uzZMwjoAEwOAT2ZpE2bVuPGjdPJkye1du1aHTx4UKVKlUrpZgEAALxUdHS0jh07qhs3rikq6rHREmuhoaGG9+nSpdPJkyckSffu3VPHjm3inevOnTvy8GilihUrafly/0TrLFiwkGGiuGeHu0uSlZWVJMnc3EKSDHVGRUXJyyv+uuiS5OHRSjly5NT+/UG6dOmiLl26qBs3rqtp0+aGMmnTplX69Ol19+5dhYSEGI0WAABTwDPoycjGxkYeHh6SpJUrVxq2P336VLNnz1bjxo1VsmRJlS1bVu3atdO2bdsSPM/Fixc1cOBAValSRSVKlFCtWrU0fvx4PXjw73qfCT2D/ujRI3l7e6t+/foqVaqUXFxc1KZNG/n7G/9yTOwZ9JCQEI0bN041atRQsWLF5OrqKk9PTx08eNCo3JUrV+Ts7KxRo0Zp7969atOmjUqXLq0yZcqoS5cuOnr06OtcPoP+/fvL2dlZ+/fvV9OmTVWsWDFVq1ZN165dM3xOHx8f1a1bV8WLF1f58uXVrVs3HTp0KMHzHTx4UJ6ennJ1dVXx4sVVu3Zt/fLLL4qMjHyjdgIA8CFp2LC2WrVqpq5d2xvubEvS+vVrDO9dXRO/I55Uzw5TX736b8P7Bw/uG9ZHL1ToxbO0J6ZXL081aVJP3bt31sGD/xi2Hzt2VHfv3pUUO8M74RyAqSGgJ7Py5ctLkvbs2SMpNpz36NFDI0eOVFRUlJo1a6YGDRro7Nmz6ty5s2bOnGl0/IEDB9SkSRMtXbpUhQoVkru7u7JkyaLff/9dHh4eCS45Eqd3796aNm2asmTJInd3d9WtW1enT59Wnz59tHDhwhe2+/r162rcuLF8fX3l4OAgd3d3VahQQdu3b1erVq20fPnyeMfs27dPHh4esrCwUMuWLVWmTBlt3bpVrVu3NvzyexO9evWSjY2N2rRpo+LFi8vJyUnh4eFq3bq1Jk2aJBsbG7m7u6tmzZrat2+fWrVqpbVr1xqdY+XKlXJ3d9fu3btVpUoVtWvXThkyZNDPP/+sdu3aEdIBAJBkbm6uZs1i7zRHRkaqadMGmjt3liZO/FEDB34rSbK0tFS3bl66detBgq8cOXJKknLkyKlbtx4Y7p4HBwfL33+Vli9frv379xrqbNSoiTJlyixJ8vdfqZ49u2nhwnlq166VwsJi79o3b+4uSdq/PyjBOitWrGQ4361bD7R/f5Ak6YsvWhq2d+jQRr6+0zVjxu9q3foLw/a2bdsn70UEgGTAEPdk5uTkJEm6deuWJGnOnDnavHmzGjVqpFGjRhmW9Ojdu7datmypcePGqVKlSsqXL59iYmI0YMAARUREyMfHR9WqVTOcd8SIEZo7d678/PzUunXrePWeOnVKmzdvVoMGDTR+/HjD9k6dOql27dry9fVV8+bN4x0XZ/Dgwbp27Zq+/PJLde/+71IpR48eVevWrTV48GC5uroqa9ashn3Hjh3ToEGD1KbNv8PbBgwYID8/P/n5+alTp05JvXxGcubMqZkzZxrNDDtx4kQdOXJE3bp1U58+fQzbe/TooaZNm2rAgAFydXWVnZ2dbt++rUGDBsne3l4LFy5UtmzZDOW9vb01bdo0+fj4GJ0HAID/qgEDBisgYKeOHz+mU6dOqk+ffyd5NTMz05gxP6po0WJJPu+JE8fUrl0rSVLt2nU1c+Z8SZKtbQb98ss0tW3bQpGRkVq4cJ4WLpxnOO5///tM7dp1eK3P0qKFuzZsWKdVq1boypXL6t//a6P9//vfZ+rV66vXOjcAvE3cQU9mqVKlkvTv81qLFi2ShYWFBg8ebLTeZoYMGeTpGTvJStzSYocPH9b58+dVtWpVo3AuSd26dVPnzp2VJ0+eBOuNiYmRJJ07d87o7nWOHDm0evVqrVixItE237x5U9u3b1fevHmNwrkkFS1aVB07dlRUVFS8JdBsbW3VqlUro21x7b506VKi9b2qWrVqGYXzp0+faunSpcqcObN69eplVNbJyUlt27ZVWFiYYUj/ihUr9OjRI3l6ehqFc0nq2bOn0qZN+1rL1AEAYGrMzN78ZWdnJ3//Derdu6/y5csvKysrZchgp6pVq2vZslXy8OjwwuNf1J7E9lWrVl1r125W3boNZG9vr9SpU6tQocIaPnyUZs6cJwsL89eq08LCXL6+s+TtPUmlS5cxTBRXsmRpjRvnrdmzFyhVKstkuW68TO+VXP9P8OL1stfbwB30ZBYWFjvJSdq0aRUeHq4zZ84oXbp0mjFjRryywcGxM48eO3bM6L8JTS6XJUsW9e3bN9F6nZ2dVa5cOe3du1dVqlRR2bJlVbFiRX3yyScvfX4rrt5y5coluD9uObbjx48bbc+RI4csLCyMtqVPn16SkmVt0Rw5chj9fP78eYWGhsrW1lY+Pj7xyl+4cEHSv5/nyJEjkmK/+EhoyH2aNGl0+/Zt3bx5U46Ojm/cXgAAUoqdXVplymT1xufJlCm9JkwYrwkTxr+88HMuXbqY4PaGDesYbiQkpEqVj1Wlyook1ydJO3duf+H+r77qpa++6vXCMvgwZcyYPqWbALwWAnoyu3LliqTYcPnw4UNJsXfTp0yZkugx9+/flxQ7SZv0b8hNqunTp2vGjBlatWqVAgICFBAQIG9vb+XOnVvfffedqlRJeM3QuHYmVm9ceI2IiDDanjp16nhlzf7/q6QX/SJ+Vc+vJR93na5du/ZK1zNuUr2Enp9/VkhICAEdAPBeCwkJ0500pjuviplZbGAKDn6oZPgnApAo+hreFXNzycEh+b8IIqAns7jJ4VxcXJQ2bVpJUr58+eLNpJ6QuPJxgfl5YWFhhjIJsbGxUffu3dW9e3fduHFDu3fv1oYNG7R+/Xr16NFDa9asUfbs2eMdly5dOkmxQ90TEhd07ezsXvoZ3qa4z/7JJ59o+vTpr1x+8eLFKlGixFttGwAAKSkmRu9FGHlf2on3H30Nb9vb6l88g56MoqKiNH9+7MQnDRs2VLp06ZQjRw5dunRJ9+7di1f+6NGjGjdunDZv3izp36VEDh8+HK9sSEiIXFxc1LFjxwTrPnjwoMaOHWtYEu2jjz5So0aNNGXKFDVp0kSPHz/WgQMHEjy2SJEihnM8efIk3v7AwEBJUsGCBV/08d+6vHnzytraWsePH1dUVFS8/Tt37pS3t7f27dsnSSpcuLAkJbj8WnR0tH744QdNmzYtWYbjAwAAAMCbIqAnk8jISA0YMEAXL15U/fr1DaG3adOmevz4sYYPH24UKsPDwzV06FD5+voa7lCXLVtWOXLk0KZNm7Rz506j80+dOlXR0dGqXLlygvWHhoZqxowZmjx5sqKjow3bY2JidPXqVUnxn+mO89FHH6lSpUq6fPlyvGe7T5w4oT/++ENWVlaqU6dOEq9K8rKyslKDBg10+/Zt/fTTT0af886dOxoyZIimTZtm2NawYUOlSpVKPj4+hufT4/zxxx/6448/tGvXLsPEfgAAAACQkhjinkTHjx/X5MmTDT9HRkbqxo0b2rVrl4KDg+Xq6qoRI0YY9nfs2FG7d+/W6tWrdfz4cbm5ucnc3FwbNmzQ9evX9dlnn6levXqSJAsLC40dO1adO3dWp06dVL16deXIkUOHDh3S/v37Vb58+QSXWJOkihUr6tNPP9WWLVtUv359VaxYURYWFtq9e7eOHz+uWrVqJTj5XJwRI0bI3d1dv/zyi3bu3KlSpUrp5s2b2rhxo6Kjo/X9998nGvDfpX79+ungwYOaMWOGdu/erXLlyikyMlLr1q3TvXv31L59e8OkdtmzZ9fQoUM1ZMgQNWzYUDVq1NBHH32koKAg7d69W5kzZ9bQoUNT+BMBAAAAQCwCehKdOHFCJ06cMPycKlUq2dnZqUiRIqpXr57q169vNLN5qlSpNH36dM2dO1crVqzQ0qVLlSpVKuXKlUuenp76/PPPjcq7uLhoyZIl8vHxUUBAgDZv3ixHR0d16dJFnp6eRku1Pcvc3FwTJ07UnDlztHLlSi1btkxPnjxRnjx5NGDAALm7u7/wc2XLlk1+fn769ddftXHjRs2dO1cZMmRQtWrV1LFjR5N5hjt9+vRasGCBfH19tXr1ai1YsEBp0qRR/vz55e7uHu8uf7NmzZQnTx79/vvv2rFjh8LDw5U1a1a5u7urS5cu+uijj1LokwAAAACAMbOY5JhuG/gPqffVZYU/4n8bAIDpGNM1vfI6me59FzOz2CXc7txhZm28XfQ1vCvm5m9nOT+eQQcAAAAAwAQQ0AEAAAAAMAEEdAAAAAAATAABHQAAAAAAE0BABwAAAADABBDQAQAAAAAwAQR0AAAAAABMAAEdAAAAAAATQEAHAAAAAMAEENABAAAAADABBHQAAAAAAEwAAR0AAAAAABNAQAcAAAAAwAQQ0AEAAAAAMAEEdAAAAAAATAABHQAAAAAAE0BABwAAAADABBDQAQAAAAAwAQR0AAAAAABMAAEdAAAAAAATQEAHAAAAAMAEENABAAAAADABBHQAAID3WCpLyTYN/6QDgA+BZUo3AHjfDOuQTk+fpnQr8CEzM5Ps7NIqJCRMMTEp3Rp8yOhrHwbbNObKZEdAB4APAQEdSKI8WS0VHZ3SrcCHzMxMypTJSnfSRBKa8FbR1wAAMC183QoAAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOgAAAAAAJoCADgAAAACACSCgAwAAAABgAgjoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAy5RuAPC+eWh+WjHm0SndDHzgLJRWDy3CUroZ+A+gr/23WEbbyTraMaWbAQBIBAEdSKLD9j31RPxjFu+AfUo3AP8Z9LX/DLMYK7ncnUdIBwATxRB3AACA/4gYsyg9MQ9J6WYAABJBQAcAAAAAwAQQ0AEAAAAAMAEEdAAAAAAATAABHQAAAAAAE0BABwAAAADABBDQAQAAAAAwAQR0AAAAAABMAAEdAAAAAAATQEAHAAAAAMAEENABAAAAADABBHQAAAAAAEwAAR0AAAAAABNAQAcAAAAAwAQQ0AEAAJBk8+fPUZYstom+tm7dbFR+wYIFqlevlvLmzSZn51xq1KiONm/emKQ6582brWrVKilnziwqWDCnPDzcdfLkiefqmfvCdsW9FiyYm2AdoaEPVbhwHmXJYqu2bVsm7aIAwBsioAMAACDJgoIOv3LZr7/+Ui1btlRgYIBCQx/q3r172rVrh1q0aKIVK/xe6Rzjx49R7949FBR0WI8ePVJISIj8/VeqTp0aOnbsaJLbb2FhEW9bTEyMBg78VsHBwUk+HwAkB8uUbgAAAADeP0eOxAZ0J6dsGj16fLz9RYsWlxR7R3vWrBmSpJw5c6lnzz66f/++fvhhlKKiojRgwDeqV69hgoE5zrlzZzVhQmwdWbM6qV+/73T69Cn5+Pyshw8faODAflq27G9JUqVKn+jPP+fFO8edO7fVr18fRUdHq3z5CmrYsInR/tDQUPXv/7UWLZr/GlcDAJIHAR0AAABJEhMTY7hrXaxYcdWpUy/RslOnTpYkmZuba8mSFcqdO68kKTw8TIGBAXJ0/Ei3bt1U1qxOiZ5j6dJFevLkiSRpyJAR+vzzLyRJhw8f1I4d27Rz53ZdunRROXPmUvbsOZQ9e4545+jSxUPR0dFKn95Wv/3mKysrK8O+WbNmaNIkb12+fCmJVwIAkhcBHQAAAEly4cJ5PXhwX5KUP39BHT0apJs3rytXrtzKl6+Aodzly5d0/PgxSZKLi4vy5MmrmJjYff37D3rl+nbv3mV4X7FiJcP7KlWqaseObZKkwMAA5cyZK8Hj165dreXLY4fSDxgwSNmyZTfaP2LEED14cF82NjZq3Lip5s2b/cptA4DkxDPoAAAASJKgoCOG9zNmTFfVqhXVosXn+vjjsmrYsLauXr0iSTp+/N9nwwsXLqyffhqvEiWclSuXoxo2rK2AgJ2vVN+ZM6clxd6Fd3T8yLDdySmb4f3Zs6cTPPbp06caNOhbSVLu3Hnk4dEpXhkzMzNVqVJVq1dvUrNmLV6pTQDwNnAHHQAAAEkSFHTI8D4iIsJoX0DATjVqVEcbN27XnTt3DNtXrFihkJAQo3Kff15fvr5z9NlndV5Y3/37scfZ2KSRufm/95fSpk1neP/suZ/l779SFy9ekCR16+YlS8v4//xdvXqj8uePvfO/c+f2F7YFAN6mZL+DPnnyZDk7O2vy5MnJfeokcXZ21ieffPLaxwcGBmr37t2Gn69cuSJnZ2e1bPl6y220adNGzs7OCb5KlSqlTz/9VL1799bRo0mfhfRDFdeXFi9enNJNAQAAz4iIeKQMGeyUNm06TZ78q86fv66tW3cbJoa7ePGCfv31F4WHhxmOCQkJUfHiJTVpko+8vHrLwsJCT548UZ8+PRQWFpZYVZKkR48eSYo/8/qzP0dGRiZ47NSpUyRJDg4OatmydYJl4sI5AKS0D/YOupeXl9KlS/fyggmYP3++hg0bppEjR6pChQqSJFtbW3l5eSlr1qxv1K7GjRsrW7ZsRttCQkJ08OBBrV69Whs2bNCcOXNUqlSpN6rnQ1C+fHl5eXmpSJEiKd0UAADwjBEjRmvEiNF69OiRrK2tJUmFCxfRuHE/qV69mpKkdevWqF27DoZjUqdOrQULlipz5iySpEePIvT7778pODhYW7ZsUt269ROtz9raWuHh4Xr69KnR9riJ4+LKPO/IkUPat2+PJKlu3QaysbF5zU8MAO/GBxvQe/bs+drHPjscK46tre0bnTNO48aN5erqmuC+CRMm6Ndff9UPP/ygefPiLw/yX+Pq6protQIAACnv+VBcrFhxw/tr167Kzs7e8HOBAgWUJUsWwyRxFStW1u+//yZJOnv2zAvrsbXNoPDwcEVEhCsmJkZmZmaSpLCwUEOZDBns4h23cuUKw/vateu+2ocCgBTEJHEmpEePHkqVKpX2798f73kuAAAAUxAdHa2goCPasGGt/P1XGe0LDf03MKdLl07OzoUMPz8/jN3KKpXh/YvWQJekPHnyGuq+deuWYfv169cM7xMapr5ly8b/r8tKlSt/+sI6AMAUpHhA/+eff+Tp6SlXV1cVK1ZMNWvW1Lhx4xKc6CMkJERjxoxRtWrVVLx4cdWvX1/Lly+Xj4+PnJ2dFRgYaCib0DPoe/fuVadOnVSpUiUVL15c1atX1/Dhw43+oq9WrZqmTIl9VmnQoEFydnbWlStXEn0GPSYmRosWLdIXX3yhMmXKqEKFCmrXrp127ny1WUmfZWVlZRiW/+wvOEk6e/asvv76a7m5ualYsWKqXr26xo0bp/v377/xderatasWLlyoihUrqlSpUurbt+9r1bt+/Xq1adNGH3/8sUqUKKHPPvtM3t7eevjwoVG5e/fuafjw4frss89UokQJubq6qlOnTtq1a5dRucSeQb927ZqGDBmiKlWqqFixYnJzc9PXX3+tM2eMv30PDAyUs7Oz/vzzT61fv17NmjVTyZIlVb58efXu3VuXLrHWKQAAr6Nhw9pq1aqZunZtrxs3rhu2r1+/xvDe1fVjFShQ0DDT+oULF3TkyGHD/n/+2W94X6jQv0E+IeXK/Tuibtu2zYb3cUusxdX3rIiICMNs80WKFFXq1Klf6bMBQEpK0SHuS5cu1aBBg2Rpaanq1avL0dFR//zzj3x9fbVu3TrNmzdPjo6OkqQHDx7I3d1dZ86cUenSpVWrVi2dOXNG3377rXLmzPnSuvbv368OHTrI1tZWNWvWVLp06XT06FHNmzdPO3bs0MqVK2Vtba22bdtq48aN2rNnj6pWraqiRYvK1tZWDx48SPC8vXv31po1a+Tk5KR69erJwsJCf//9tzp27Chvb2/Vrfvqw6mCgoJ07949Zc6cWZkyZTJs3717tzw9PfX48WPVqFFD2bNn1/Hjx+Xr66tNmzZp/vz5cnBweO3rdOTIEQUEBKhRo0aKiYkxPPOdlHpXr16tPn36KHv27Kpbt66srKy0b98+TZs2Tfv27dO8efNkZmamqKgoderUScePH1f16tVVs2ZN3blzR2vWrNHOnTv122+/vXByv+PHj6tdu3a6f/++Pv74Y9WuXVsXLlzQ33//rY0bN8rHx0cVK1Y0OmbVqlUKCgpS1apVVb58ee3fv1+rV6/WwYMHtX79eqVKlSqR2gAAwPPMzc3VrFlz+fpOV2RkpJo2bSBPz566ffuWJk70liRZWlqqWzcvmZmZqUOHzho5cphiYmLUunVz9e7dVyEh9zR1auyEwk5O2Qx3t4ODgxUYGCBJcnR0VNmy5SRJTZs215QpExUdHa3vvx+qx48f6/TpU9q6NTasV6r0iXLkMP53zrlzZw3PqBcvXuptXxYASBYpFtCvXbumYcOGydbWVjNnzjR8cxoTE6OJEyfq119/1aBBgzR9+nRJ0pQpU3TmzBm1a9dO3333neE8vr6+Gjdu3EvrmzlzpqKiojR//nyjoDpw4EAtWbJEGzZsUL169eTh4aGHDx9qz549ql69upo1ayZJCQb0VatWac2aNfr44481ZcoUw93v9u3bq3Hjxho9erRq1aqV4HIecaKjo/XgwQPt27dPo0ePliR5enoanq2KjIw03NFetGiR0YRpcZPZjRo1St7e3q99nYKDgzVixAg1b97csC2p9U6fPl02NjZavny54TrExMSoQ4cO2rVrlw4ePKjSpUtr586dCgoKkqenp3r37m045+effy53d3fNnDkz0YAeHR2tb775Rvfv39e4cePUqFEjw75t27apa9eu+vrrr7Vx40alSZPGsO/IkSOaOnWqqlWrZmhX+/btFRAQoA0bNqh27doJ1gcAABI2YMBgBQTs1PHjx3Tq1En16eNl2GdmZqYxY35U0aLFJEmenj21Y8c2bdmySdeuXVW/fn0MZW1sbOTjM11WVlaSpBMnjsnDo5Uk6bPP6mrWrPmSpEKFCqtPn2/k7T1ON25cV+/ePQznSJ/eVqNG/RCvjVeuXDa8f36CXgAwVSk2xP2vv/5SVFSUOnbsaDSsyczMTD179lSuXLm0bds2Xb9+XU+fPtWKFSuUIUMGffnll0bnadeunXLnzv3S+mL+f0aSffv2GW3/9ttvtX379iTd6Y6zfPlySVL//v2NZozPmTOnBgwYoA4dOig8PNzomLZt2xotsVa4cGG5urqqR48eCg0N1aBBg+Tu7m4ov2nTJt2+fVstW7aMN5t5y5YtlTt3bq1Zs0ahoaFvdJ0+++wzo5+TUm+cR48e6ciRI4afzczM5O3trYCAAJUuXVrSv38Ox48fN7o2Li4uWrdunaZOnZpoGw8ePKjTp0+rcuXKRuFckj755BM1atRId+/e1bp164z2FSxY0BDO49r16aefShLD3AEA/0lmZm/2srOzk7//BvXu3Vf58uWXlZWVMmSwU9Wq1bVs2Sp5eHQwlLWySqX58xdr/PjxcnYuJCsrK9nb26tevYZas2aT3NwqGZ07sTb27z9QPj7TVLJkaVlbWytDBjvVrl1X/v7rVaRIkXjlw8L+fcTOwcEhSZ8vua4Tr5R58WfH61293oYUu4N+7NgxSbFLaT3P0tJSpUuX1sWLF3Xs2DE9evRIISEhKleunNKmTWtU1sLCQqVLl9aFCxdeWF+LFi20YcMGDRgwQFOmTFGlSpVUsWJFubm5KUuWLK/1GY4fP640adLI2dk53r6mTZsmeMyzy6wFBwdr9erVCgkJUfPmzTVo0CDDN8hx4gLv+fPnE1xbPm4N0ZMnT8rBweG1rlOGDBmUIUOG1663bNmyatWqlQYOHCgPDw/lzZtXbm5uqlSpkipUqGA0w2vFihWVO3dubdmyRW5ubnJ1dVXFihVVuXJl5cmTJ8FrFudFfUaKDfl+fn46fvy4UYBP6Lzp06eXJD1+/PiFdQIA8KGxs08re6V/4/NkypReEyaM14QJ41+pfN++fY3muUlIw4Z1DF/mJ8TTs7M8PTu/Un1du3ZU164dX6lsUtqA90PGjG/ex4GUkGIBPW7isLig9Ly4Z88jIiJ07949SVLmzJlfWPZF3NzcNHfuXM2YMUM7duzQwoULtXDhQqVKlUoNGjTQoEGDjIZFv4qQkBDZ29vLLAlfnzy/zFqvXr3Url07LVy4UGnSpFH//v2NyscNrd+0aZM2bdqU6Hnv379vaEdSr1NC64YmpV4p9guJTJkyafbs2QoMDNTs2bM1e/ZspUmTRi1atNDXX38tS0tLWVtba+HChZo2bZrWrFmjzZs3a/Pm2OfHihYtqmHDhqlEiRIJ1hXXZxJb3/7ZPvOs57/0kGS4VvwCBgD814TcC9PTpw9fXjAZmZnFBqbg4IfiVy/eJvoa3hVzc8nBIfm/CEqxgB4Xsm7evKl8+fLF2x8X/Ozs7Ax3g5+fDTzO8zOeJ6ZMmTIqU6aMoqKidPjwYe3YsUPLly/X0qVLZW5urpEjRybpM6RNm1ZhYWFG63HGiYyMlKWl5UuXDXFwcNAvv/yixo0ba8aMGcqXL5/hufe4OqTYNdLr1KnzwnOdPHlS0ptfp6TWG+fTTz/Vp59+qoiICP3zzz/avn27li9fLl9fX9na2srT01NS7J9pv3791K9fP128eFG7du3S2rVrFRAQoE6dOmnTpk0JhvBn+0xC4r5UsLOze+XPCQDAf1FKBZeYmJSrG/8t9DW8bW+rf6XYM+hxzzXv3bs3wf179uyRmZmZChQooLx58ypNmjQKCgoyzMb5rAMHDrywrujoaE2bNk0TJkyQFHtH1cXFRb1799a8efPiteNV74g7OzsrPDxcp06dirfvxx9/VMmSJeM9856QuGfWJWnUqFG6fPnfSU0KFy4sSTp8+HCCx/r4+Gjq1KkKCQl54+v0rKTUGxoaql9++UUzZsyQFDvhi5ubm/r3728YHh93fbdv366RI0fq4sWLkqRcuXKpZcuW+vPPP+Xq6qr79+8neD2lf/tMYtd09+7dkmKfOQcAAACA902KBfQGDRooVapUmjlzpk6cOGG0z8fHR+fOnZObm5scHR2VKlUqNW7cWPfu3TOsUR5n6dKlOnr06AvrMjc316ZNm/Tbb7/FC3dxYTh79uyGbXGzrr/s+eTGjRtLksaPH280rPrKlStasWKF0qVLp5IlS77wHHGaNm2qSpUqKSIiQkOGDDFsr1Gjhuzs7DR37lwdOnTI6Bh/f39NmjRJy5cvl62t7Rtfp2clpd40adJo4cKF+vnnn3X+/Hmjss9f32vXrmn27NmG2fnjREZG6vbt2zI3N090ptXSpUsrX7582rdvn5YsWWK0b9euXfLz85OdnZ2qVq36yp8TAAAAAEzFWxvivmzZMu3ZsyfBfTlz5tSoUaM0dOhQDRkyRM2aNTOsg37gwAEdOnRI2bNnNxpy3qtXL+3YsUNTp05VYGCgSpYsqTNnzmj79u2yt7fXvXv3XjicvG/fvvLw8JCHh4dq1qyp7Nmz68aNG1q3bp2sra2NZj3PmjWrpNil2a5fv67WrVsneM7GjRtr48aN2rBhgxo2bKjKlSvr8ePH8vf3V1hYmKZNm5akNbaHDx+u+vXrG8JmkyZNlC5dOv3www/y8vJSy5YtVa1aNeXKlUtnz57V1q1blSZNGo0dO1bm5ubJcp3iJLXe/v3766uvvlKTJk1Uq1YtZc6cWRcvXtTGjRvl4OCgzp1jJ3Rp0KCBFi5cqMWLF+vkyZNycXHR48ePtW3bNl28eFEdOnRI9Fl5c3NzjR8/Xh4eHho4cKBWrVqlwoUL68KFC9qyZYtSp04tb2/veBPkAQAAAMD74K0F9KtXr+rq1asJ7ot7VrhZs2bKkyePpk+frl27dikiIkLZsmVTt27d1KlTJ6MJ5Ozs7DRv3jxNmjRJmzZt0pEjR5QvXz5NmjRJf//9t9atWycbG5tE2+Pi4qJ58+Zp2rRpOnjwoNavX68MGTKoevXq8vT0VIECBQxla9eure3bt2vjxo2aM2eOypcvn+BM4GZmZvr55581d+5c+fn5afHixbKwsFDJkiXVvXv3RGcbT0z27NnVu3dvjR49WuPGjdMnn3yiTJkyqUqVKlq8eLGmTZumwMBAbdmyRVmyZFH9+vXVtWtXo2f43/Q6PSsp9dapU0cZMmSQr6+vdu7cqXv37iljxoxq3LixunfvLicnJ0mxw999fX01Y8YMbdiwQQsWLJAU+7iAp6dnvOXTnle0aFH5+fnp119/1fbt27Vv3z5lzJhRjRo1UufOnZU3b94kXXMAAAAAMBVmMe/JNNYXL16Uo6NjgjOOt2jRQgcOHFBAQIAcHBxSoHWmg+v09v0lNz1RWEo3AwCA11L63u9K9yT+ErFvk5lZ7LJsd+4wszbeLvoa3hVz87eznF+KPYOeVF9++aXKly8fbwbvffv26eDBg3J2diZ0iusEAAAAAO+rFFtmLanc3d01aNAgNWrUyDCB2cWLF7Vp0yZZW1tr6NChKd1Ek8B1AgAAAID303sT0Js1ayZHR0fNmjVLmzdvVkhIiDJmzKg6deqoS5cuyp8/f0o30SRwnQAAAADg/fTePIMOmAqeQQcAvM94Bh0fMvoa3pX//DPoAAAAAAB8yAjoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAAACaAgA4AAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOgAAAAAAJoCADgAAAACACSCgAwAAAABgAgjoAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAMB/hFmMlSyj7VK6GQCARFimdAOA902Je5MVo+iUbgY+cHb2aRVyLyylm4H/APraf4tltJ2sox1TuhkAgEQQ0IEkSh9dQNHkc7xFZmaSvdLr6dOHiolJ6dbgQ0ZfAwDAtDDEHQAAAAAAE0BABwAAAADABBDQAQAAAAAwAQR0AAAAAABMAAEdAAAAAAATQEAHAAAAAMAEsMwakERmZrEv4G2J61/0M7xt9DW8K/Q1vCv0Nbwrb6uPmcXEsPIpAAAAAAApjSHuAAAAAACYAAI6AAAAAAAmgIAOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAAACaAgA4AAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOvAKgoOD1b17d7m4uMjV1VWjRo3SkydPUrpZ+ACcOHFC7du3V/ny5eXm5qZ+/frp7t27kqRDhw6pWbNmKl26tKpVq6bFixencGvxvnv69KnatGmj/v37G7bRz5DcQkJC1K9fP7m6uqpcuXLq3r27bt26JYn+huR19OhRubu7y8XFRZUqVdLIkSMVFRUlib6G5HH37l3VrFlTgYGBhm0v61vLli1TzZo1VapUKTVp0kQHDhxIUp0EdOAV9O7dW2nSpNH27du1ZMkSBQQE6M8//0zpZuE99+jRI3Xq1EmlS5fWjh07tGrVKoWEhOi7777T/fv31aVLFzVq1Eh79+7VqFGjNGbMGB0+fDilm4332JQpU7Rv3z7Dz/QzvA09e/ZUeHi41q9fr82bN8vCwkKDBw+mvyFZRUdHq2vXrqpVq5b27NmjJUuWaMeOHZo+fTp9Dcli//79at68uS5dumTY9rK+FRgYqO+//15jx47V3r171aBBA3l6eioiIuKV6yWgAy9x8eJF7dmzR998841sbGyUI0cOde/eXXPnzk3ppuE9d+3aNRUqVEg9evSQlZWV7O3t1bx5c+3du1fr1q2TnZ2d3N3dZWlpqY8//lj169en3+G1BQQEaN26dfrf//5n2EY/Q3ILCgrSoUOHNHbsWNna2ipdunT6/vvv1bdvX/obktX9+/d1+/ZtRUdHKyYmRpJkbm4uGxsb+hre2LJly9S3b1/16dPHaPvL+tbixYtVt25dlS1bVqlSpZKHh4fs7e3l7+//ynUT0IGXOH36tOzs7OTo6GjYli9fPl27dk0PHjxIwZbhfZc3b179/vvvsrCwMGxbu3atihYtqtOnT6tgwYJG5fPnz68TJ06862biAxAcHKyBAwfK29tbNjY2hu30MyS3w4cPK3/+/Fq0aJFq1qypSpUqady4ccqcOTP9DcnK3t5eHh4eGjdunIoXL64qVaood+7c8vDwoK/hjVWqVEnr169XnTp1jLa/rG+dOXPmjfseAR14ibCwMKN/0Eoy/BweHp4STcIHKCYmRhMmTNDmzZs1cODABPudtbU1fQ5JFh0drW+++Ubt27dXoUKFjPbRz5Dc7t+/r5MnT+rChQtatmyZli9frps3b+rbb7+lvyFZRUdHy9raWoMHD9bBgwe1atUqnT17Vj///DN9DW8sc+bMsrS0jLf9ZX0rOfoeAR14iTRp0sR7biTu57Rp06ZEk/CBCQ0NVa9evbRy5UrNmTNHzs7OsrGx0aNHj4zKPXr0iD6HJPvtt99kZWWlNm3axNtHP0Nys7KykiQNHDhQ6dKlU6ZMmdS7d29t3bpVMTEx9Dckm/Xr12vt2rVq1aqVrKysVKBAAfXo0UPz58/n7za8NS/rW8nR9wjowEsUKFBAISEhunPnjmHb2bNn9dFHHyl9+vQp2DJ8CC5duqTPP/9coaGhWrJkiZydnSVJBQsW1OnTp43KnjlzRgUKFEiJZuI9tmLFCu3Zs0cuLi5ycXHRqlWrtGrVKrm4uNDPkOzy58+v6OhoPX782LAtOjpaklS4cGH6G5LN9evXDTO2x7G0tFSqVKn4uw1vzcv6VoECBd647xHQgZfInTu3ypYtq9GjRys0NFSXL1+Wj4+PmjZtmtJNw3vu/v37ateuncqUKaM//vhDDg4Ohn01a9bUnTt39Oeff+rx48favXu3Vq5cqc8//zwFW4z30Zo1a/TPP/9o37592rdvn+rVq6d69epp37599DMku4oVKypHjhz67rvvFBYWprt372rChAmqUaOG6tWrR39DsqlUqZJu376tX3/9VU+fPtXly5c1depU1a9fn7/b8Na8rG81bdpUK1eu1O7du/X48WP9+eefCg4OVs2aNV+5DrOYuGkPASTqzp07GjFihAIDA2Vubq5GjRqpb9++RpN7AUk1Y8YMjR07VjY2NjIzMzPad+DAAR05ckSjRo3SqVOn5ODgoO7du6tJkyYp1Fp8KOLWQB87dqwk0c+Q7G7evGlYYigyMlLVqlXTwIEDZWtrS39Dstq1a5cmTpyoc+fOKX369GrQoIFhZRT6GpKLs7OzZs2aJVdXV0kv/725YsUKTZ06VTdv3lT+/Pk1aNAglSxZ8pXrI6ADAAAAAGACGOIOAAAAAIAJIKADAAAAAGACCOgAAAAAAJgAAjoAAAAAACaAgA4AAAAAgAkgoAMAAAAAYAII6AAAAAAAmAACOgAAAAAAJoCADgAAAACACSCgAwAAAABgAgjoAAAAAACYAAI6AAAAAAAm4P8Al0+sCS5d0vgAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recall_eval('DecisionTree', dt_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Y_f-BWX6CCA"
   },
   "source": [
    "###  \n",
    "### **앙상블 기법의 종류**\n",
    "- 배깅 (Bagging): 여러개의 DecisionTree 활용하고 샘플 중복 생성을 통해 결과 도출. RandomForest\n",
    "- 부스팅 (Boosting): 약한 학습기를 순차적으로 학습을 하되, 이전 학습에 대하여 잘못 예측된 데이터에 가중치를 부여해 오차를 보완해 나가는 방식. XGBoost, LGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dtkXJlKj0FUW"
   },
   "source": [
    "![앙상블](https://teddylee777.github.io/images/2019-12-18/image-20191217144823555.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-rwJjK90FUX"
   },
   "source": [
    "###  \n",
    "### 4) 랜덤포레스트(RandomForest)\n",
    "+ Bagging 대표적인 모델로써, 훈련셋트를 무작위로 각기 다른 서브셋으로 데이터셋을 만들고\n",
    "+ 여러개의 DecisonTree로 학습하고 다수결로 결정하는 모델"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NpOEkIoCAdFd"
   },
   "source": [
    "**주요 Hyperparameter**\n",
    "- random_state: 랜덤 시드 고정 값. 고정해두고 튜닝할 것!\n",
    "- n_jobs: CPU 사용 갯수\n",
    "- max_depth: 깊어질 수 있는 최대 깊이. 과대적합 방지용\n",
    "- n_estimators: 앙상블하는 트리의 갯수\n",
    "- max_features: 최대로 사용할 feature의 갯수. 과대적합 방지용\n",
    "- min_samples_split: 트리가 분할할 때 최소 샘플의 갯수. default=2. 과대적합 방지용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "LfzSa0N60FUX"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "JegAZUoX0FUX",
    "outputId": "96e8729f-ee39-47fb-db82-a4f89ff28448",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier(n_estimators=3, random_state=42)",
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     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rfc = RandomForestClassifier(n_estimators=3, random_state=42)\n",
    "# rfc.fit(X_train, Y_train)\n",
    "rfc = RandomForestClassifier(n_estimators=3, random_state=42)\n",
    "rfc.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "yd3AA9mFa_0n"
   },
   "outputs": [],
   "source": [
    "# rfc_pred = rfc.predict(X_test)\n",
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "-RkzXSShS6ks",
    "outputId": "e3cdf947-b72d-4cef-d795-5ec334ab602d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   model     recall\n",
      "0     LogisticRegression  56.071429\n",
      "1           DecisionTree  54.642857\n",
      "2  RandomForest Ensemble  52.857143\n",
      "3     K-Nearest Neighbor  51.071429\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recall_eval('RandomForest Ensemble', rfc_pred, y_test)\n",
    "recall_eval('RandomForest Ensemble', rfc_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vxLwe2Qw0FUY"
   },
   "source": [
    "###  \n",
    "### 5) XGBoost\n",
    "+ 여러개의 DecisionTree를 결합하여 Strong Learner 만드는 Boosting 앙상블 기법\n",
    "+ Kaggle 대회에서 자주 사용하는 모델이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xw17wJya85Ms"
   },
   "source": [
    "**주요 특징**\n",
    "- scikit-learn 패키지가 아닙니다.\n",
    "- 성능이 우수함\n",
    "- GBM보다는 빠르고 성능도 향상되었습니다.\n",
    "- 여전히 학습시간이 매우 느리다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-fAjfIX9Jpcx"
   },
   "source": [
    "**주요 Hyperparameter**\n",
    "- random_state: 랜덤 시드 고정 값. 고정해두고 튜닝할 것!\n",
    "- n_jobs: CPU 사용 갯수\n",
    "- learning_rate: 학습율. 너무 큰 학습율은 성능을 떨어뜨리고, 너무 작은 학습율은 학습이 느리다. 적절한 값을 찾아야함. n_estimators와 같이 튜닝. default=0.1\n",
    "- n_estimators: 부스팅 스테이지 수. (랜덤포레스트 트리의 갯수 설정과 비슷한 개념). default=100\n",
    "- max_depth: 트리의 깊이. 과대적합 방지용. default=3. \n",
    "- subsample: 샘플 사용 비율. 과대적합 방지용. default=1.0\n",
    "- max_features: 최대로 사용할 feature의 비율. 과대적합 방지용. default=1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "ABeT1Opy0FUY",
    "outputId": "cec43e50-ffd1-4bab-af84-a87cefeb9aff",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-1.7.5-py3-none-win_amd64.whl (70.9 MB)\n",
      "     ---------------------------------------- 70.9/70.9 MB 1.6 MB/s eta 0:00:00\n",
      "Requirement already satisfied: scipy in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from xgboost) (1.10.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from xgboost) (1.23.5)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.7.5\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "LH2Plwt60FUZ"
   },
   "outputs": [],
   "source": [
    "# from xgboost import XGBClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "hygo7t_j0FUZ",
    "outputId": "74c1e08e-c7a9-4bea-b3dd-b23a57876167",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              n_estimators=3, n_jobs=None, num_parallel_tree=None,\n              predictor=None, random_state=42, ...)",
      "text/html": "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              n_estimators=3, n_jobs=None, num_parallel_tree=None,\n              predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              n_estimators=3, n_jobs=None, num_parallel_tree=None,\n              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(n_estimators=3, random_state=42)  \n",
    "xgb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "YiIBtKi80FUZ"
   },
   "outputs": [],
   "source": [
    "xgb_pred = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "kAKKoLLh0FUZ",
    "outputId": "10d769a9-f45e-4d43-d577-4ab488d2a88c",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   model     recall\n",
      "0     LogisticRegression  56.071429\n",
      "1           DecisionTree  54.642857\n",
      "2  RandomForest Ensemble  52.857143\n",
      "3                XGBoost  52.142857\n",
      "4     K-Nearest Neighbor  51.071429\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recall_eval('XGBoost', xgb_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUvyyQcM0FUZ"
   },
   "source": [
    "###  \n",
    "### 6) Light GBM\n",
    "+ XGBoost와 함께 주목받는 DecisionTree 알고리즘 기반의 Boosting 앙상블 기법\n",
    "+ XGBoost에 비해 학습시간이 짧은 편이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Unk2_LocLD_I"
   },
   "source": [
    "**주요 특징**\n",
    "- scikit-learn 패키지가 아닙니다.\n",
    "- 성능이 우수함\n",
    "- 속도도 매우 빠릅니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GaKdEHJpLrm4"
   },
   "source": [
    "**주요 Hyperparameter**\n",
    "- random_state: 랜덤 시드 고정 값. 고정해두고 튜닝할 것!\n",
    "- n_jobs: CPU 사용 갯수\n",
    "- learning_rate: 학습율. 너무 큰 학습율은 성능을 떨어뜨리고, 너무 작은 학습율은 학습이 느리다. 적절한 값을 찾아야함. n_estimators와 같이 튜닝. default=0.1\n",
    "- n_estimators: 부스팅 스테이지 수. (랜덤포레스트 트리의 갯수 설정과 비슷한 개념). default=100\n",
    "- max_depth: 트리의 깊이. 과대적합 방지용. default=3. \n",
    "- colsample_bytree: 샘플 사용 비율 (max_features와 비슷한 개념). 과대적합 방지용. default=1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "id": "KeMfBOjQ0FUa",
    "outputId": "37b789ab-e898-463b-ea50-d37ffdb86515",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lightgbm\n",
      "  Downloading lightgbm-3.3.5-py3-none-win_amd64.whl (1.0 MB)\n",
      "     ---------------------------------------- 1.0/1.0 MB 7.2 MB/s eta 0:00:00\n",
      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from lightgbm) (1.2.1)\n",
      "Requirement already satisfied: wheel in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from lightgbm) (0.38.4)\n",
      "Requirement already satisfied: numpy in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from lightgbm) (1.23.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from lightgbm) (1.10.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (1.1.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\perar\\anaconda3\\envs\\pyai_study\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.2.0)\n",
      "Installing collected packages: lightgbm\n",
      "Successfully installed lightgbm-3.3.5\n"
     ]
    }
   ],
   "source": [
    "!pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "190g_jQU0FUa"
   },
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "34NklLzz0FUa",
    "outputId": "bfde9023-41f3-481e-910d-7f7883c5018f",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LGBMClassifier(n_estimators=3, random_state=42)",
      "text/html": "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(n_estimators=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(n_estimators=3, random_state=42)</pre></div></div></div></div></div>"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lgbm = LGBMClassifier(n_estimators=3, random_state=42)\n",
    "# lgbm.fit(X_train, y_train)\n",
    "\n",
    "lgbm = LGBMClassifier(n_estimators=3, random_state=42)\n",
    "lgbm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "IqymXJLO0FUa"
   },
   "outputs": [],
   "source": [
    "lgbm_pred = lgbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false,
    "id": "wBLc6O9q0FUb",
    "outputId": "dbe4570f-e313-4f9c-b30f-3a9d27110663",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   model     recall\n",
      "0     LogisticRegression  56.071429\n",
      "1           DecisionTree  54.642857\n",
      "2  RandomForest Ensemble  52.857143\n",
      "3                XGBoost  52.142857\n",
      "4     K-Nearest Neighbor  51.071429\n",
      "5                   LGBM   0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recall_eval('LGBM', lgbm_pred, y_test)\n",
    "recall_eval('LGBM', lgbm_pred, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "iEYoahG80FUb",
    "outputId": "fed70c62-0759-409d-faa7-23c665d71609",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.7344713134186819"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정확도는 73% 정도 나온다.\n",
    "lgbm.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "DzHQwJBf0FUb",
    "outputId": "8acaae40-8784-49ee-e595-b7be6661db57",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.0"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 재현율 0으로 나온다. \n",
    "recall_score(Y_test, lgbm_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dwF8m19w0FUb"
   },
   "source": [
    "##  \n",
    "# <font color=red>2. 재현율 성능이 너무 안나온다. 어떻게 해결할수 있을까?</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w6mhbAam0FUb"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KLSjCzg80FUc"
   },
   "source": [
    "##  \n",
    "## 배운 내용 정리\n",
    "1. 머신러닝 모델 프로세스 <br>\n",
    "① 라이브러리 임포트(import)  \n",
    "② 데이터 가져오기(Loading the data)  \n",
    "③ 탐색적 데이터 분석(Exploratory Data Analysis)  \n",
    "④ 데이터 전처리(Data PreProcessing) : 데이터타입 변환, Null 데이터 처리, 누락데이터 처리, \n",
    "더미특성 생성, 특성 추출 (feature engineering) 등  \n",
    "⑤ Train, Test  데이터셋 분할  \n",
    "⑥ 데이터 정규화(Normalizing the Data)  \n",
    "⑦ 모델 개발(Creating the Model)  \n",
    "⑧ 모델 성능 평가\n",
    "2. 평가 지표 활용 : 모델별 성능 확인을 위한 함수 (가져다 쓰면 된다)\n",
    "3. 단일 회귀예측 모델 : LogisticRegression, KNN, DecisionTree\n",
    "4. 앙상블 (Ensemble) : RandomForest, XGBoost, LGBM\n",
    "5. 재현율 성능이 너무 안나온다. 어떻게 해결할수 있을까?"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p>\n",
    "<p>\n",
    "<p>"
   ],
   "metadata": {
    "id": "DiZZH78kneDY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p>\n",
    "<p>\n",
    "<p>"
   ],
   "metadata": {
    "id": "gAOb2TJlnhyx"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p>\n",
    "<p>\n",
    "<p>"
   ],
   "metadata": {
    "id": "dNiFmy0ani4a"
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OE89vy1a0YH3"
   },
   "source": [
    "# [실습-퀴즈] Python을 활용한 AI 모델링 - 딥러닝 파트\n",
    "+ 이번시간에는 Python을 활용한 AI 모델링에서 딥러닝에 대해 실습해 보겠습니다.\n",
    "+ 여기서는 딥러닝 모델 DNN에 대해 코딩하여 모델 구축해 보겠습니다.\n",
    "+ 한가지 당부 드리고 싶은 말은 \"백문이불여일타\" 입니다. \n",
    "+ 이론보다 실습이 더 많은 시간과 노력이 투자 되어야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jiPP1idv0YH8"
   },
   "source": [
    "## 학습목차\n",
    "1. 딥러닝 심층신경망(DNN) 모델 프로세스\n",
    " - 데이터 가져오기\n",
    " - 데이터 전처리\n",
    " - Train, Test 데이터셋 분할\n",
    " - 데이터 정규화\n",
    " - DNN 딥러닝 모델\n",
    "2. 재현율 성능이 좋지 않다. 어떻게 성능향상 할수 있나?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zr3QI2qG0YH9"
   },
   "source": [
    "#  \n",
    "# 1. 딥러닝 심층신경망(DNN) 모델 프로세스\n",
    "① 라이브러리 임포트(import)  \n",
    "② 데이터 가져오기(Loading the data)  \n",
    "③ 탐색적 데이터 분석(Exploratory Data Analysis)  \n",
    "④ 데이터 전처리(Data PreProcessing) : 데이터타입 변환, Null 데이터 처리, 누락데이터 처리, \n",
    "더미특성 생성, 특성 추출 (feature engineering) 등  \n",
    "⑤ Train, Test  데이터셋 분할  \n",
    "⑥ 데이터 정규화(Normalizing the Data)  \n",
    "⑦ 모델 개발(Creating the Model)  \n",
    "⑧ 모델 성능 평가\n",
    "\n",
    "### <font color='red'>1 ~ 6번까지 완료한 상태</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fUKskwle0YIM"
   },
   "source": [
    "## ⑦ 딥러닝 심층신경망(DNN) 모델 구현"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-qtH1IV_0YIM"
   },
   "source": [
    "####  \n",
    "### 라이브러리 임포트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "xZ-81qT80YIM"
   },
   "outputs": [],
   "source": [
    "# import tensorflow as tf\n",
    "# from tensorflow.keras.models import Sequential\n",
    "# from tensorflow.keras.layers import Dense, Dropout\n",
    "#\n",
    "# tf.random.set_seed(100)\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "\n",
    "tf.random.set_seed(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VUDoigSc0YIM"
   },
   "source": [
    "####  \n",
    "### 하이퍼파라미터 설정 : batch_size, epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "zmEdqNNS0YIM"
   },
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "epochs = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PqXbXmeD0YIN"
   },
   "source": [
    "####  \n",
    "### 모델 입력(features) 갯수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zCUyUw6J0YIN",
    "outputId": "2454aff8-f6fd-485f-ee55-2ece1b943d39"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[5], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mX_train\u001B[49m\u001B[38;5;241m.\u001B[39mshape\n",
      "\u001B[1;31mNameError\u001B[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L1mqWzpz0YIN"
   },
   "source": [
    "####  \n",
    "### 모델 출력(label) 갯수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kzVC8VGO0YIN",
    "outputId": "b95c5396-b919-4a99-b2bb-989ea9ea6790"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(4918,)"
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1zLx5_rB0YIO"
   },
   "source": [
    "####  \n",
    "### A. 이진분류 DNN모델 구성 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zKKe_aOJ0YIO"
   },
   "source": [
    "![hidden Layer](https://github.com/gzone2000/TEMP_TEST/raw/master/hidden_layer1.PNG)\n",
    "+ [출처] https://subscription.packtpub.com/book/data/9781788995207/1/ch01lvl1sec03/deep-learning-intuition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2LX2K8Hu0YIO"
   },
   "source": [
    "##### <font color=blue> **[문제] 요구사항대로 Sequential 모델을 만들어 보세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "Fygby0lS0YIO"
   },
   "outputs": [],
   "source": [
    "# Sequential() 모델 정의 하고 model로 저장\n",
    "# input layer는 input_shape=() 옵션을 사용한다.\n",
    "# 39개 input layer\n",
    "# unit 4개 hidden layer\n",
    "# unit 3개 hidden layer \n",
    "# 1개 output layser : 이진분류\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Dense(1, input_shape=(1,)))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gRD_erqd0YIO"
   },
   "source": [
    "####  \n",
    "### 모델 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_dWPN4Hf0YIP",
    "outputId": "c13286a7-2094-4e4f-aff2-aa7d7cd332fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hArp-8W00YIP"
   },
   "source": [
    "####  \n",
    "### 모델 구성 -  과적합 방지"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P12SscRX0YIP"
   },
   "source": [
    "![dropout](https://github.com/gzone2000/TEMP_TEST/raw/master/dropout.PNG)\n",
    "+ [출처] https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "id": "hTZ0lfsB0YIP"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(4, activation='relu', input_shape=(39,)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(3, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "awF-BLSo0YIQ"
   },
   "source": [
    "####  \n",
    "### 과적합 방지 모델 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5EPZjdj80YIQ",
    "outputId": "5c973173-027e-4d7f-80ab-9858a0f7cee4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_1 (Dense)             (None, 4)                 160       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 4)                 0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 15        \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 3)                 0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 179\n",
      "Trainable params: 179\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1VOruMmv0YIQ"
   },
   "source": [
    "####  \n",
    "### 모델 컴파일 – 이진 분류 모델"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nVZnbiNp0YIQ"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mHEfmI450YIQ"
   },
   "source": [
    "- 모델 컴파일 – 다중 분류 모델 (Y값을 One-Hot-Encoding 한경우) <br>\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qvjLtvLO0YIR"
   },
   "source": [
    "- 모델 컴파일 – 다중 분류 모델  (Y값을 One-Hot-Encoding 하지 않은 경우) <br>\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AK35wHQq0YIR"
   },
   "source": [
    "- 모델 컴파일 – 예측 모델\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rTX4ExMb0YIR"
   },
   "source": [
    "####  \n",
    "### 모델 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l-H8W9R80YIR"
   },
   "source": [
    "##### <font color=blue> **[문제] 요구사항대로 DNN 모델을 학습시키세요.** </font>\n",
    "+ 모델 이름 : model\n",
    "+ epoch : 10번\n",
    "+ batch_size : 10번"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l0VkwonN0YIS",
    "outputId": "0bd4f847-c11e-4df6-938a-2507b165d3e5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/10\n",
      "492/492 [==============================] - 2s 3ms/step - loss: 0.6015 - accuracy: 0.7318 - val_loss: 0.5247 - val_accuracy: 0.7345\n",
      "Epoch 2/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5439 - accuracy: 0.7416 - val_loss: 0.4802 - val_accuracy: 0.7345\n",
      "Epoch 3/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5225 - accuracy: 0.7548 - val_loss: 0.4734 - val_accuracy: 0.7345\n",
      "Epoch 4/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5139 - accuracy: 0.7623 - val_loss: 0.4646 - val_accuracy: 0.7364\n",
      "Epoch 5/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5153 - accuracy: 0.7554 - val_loss: 0.4651 - val_accuracy: 0.7368\n",
      "Epoch 6/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5016 - accuracy: 0.7674 - val_loss: 0.4550 - val_accuracy: 0.7653\n",
      "Epoch 7/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5042 - accuracy: 0.7593 - val_loss: 0.4532 - val_accuracy: 0.7496\n",
      "Epoch 8/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.4989 - accuracy: 0.7633 - val_loss: 0.4536 - val_accuracy: 0.7620\n",
      "Epoch 9/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5035 - accuracy: 0.7609 - val_loss: 0.4553 - val_accuracy: 0.7539\n",
      "Epoch 10/10\n",
      "492/492 [==============================] - 1s 2ms/step - loss: 0.5023 - accuracy: 0.7588 - val_loss: 0.4567 - val_accuracy: 0.7544\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4adf1ffb50>"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "# 앞쪽에서 정의된 모델 이름 : model\n",
    "# Sequential 모델의 fit() 함수 사용\n",
    "# @인자\n",
    "### X, y : X_train, y_train\n",
    "### validation_data=(X_test, y_test)\n",
    "### epochs 10번\n",
    "### batch_size 10번\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WPZYzqib0YIS"
   },
   "source": [
    "####  \n",
    "### B. 다중 분류 DNN 구성\n",
    "+ 13개 input layer\n",
    "+ unit 5개 hidden layer\n",
    "+ dropout\n",
    "+ unit 4개 hidden layer \n",
    "+ dropout\n",
    "+ 2개 output layser : 이진분류"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Opb5-0OI0YIS"
   },
   "source": [
    "![다중분류](https://github.com/gzone2000/TEMP_TEST/raw/master/hidden_layer2.PNG)\n",
    "+ [출처] https://www.educba.com/dnn-neural-network/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "k0Tiq3A40YIS"
   },
   "outputs": [],
   "source": [
    "# 39개 input layer\n",
    "# unit 5개 hidden layer\n",
    "# dropout\n",
    "# unit 4개 hidden layer \n",
    "# dropout\n",
    "# 2개 output layser : 다중분류\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(5, activation='relu', input_shape=(39,)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(4, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sheeDtax0YIS"
   },
   "source": [
    "####  \n",
    "### 모델 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sLGbErrf0YIT",
    "outputId": "a40264ba-9d7f-4f91-d501-7b9335c4ba38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 5)                 200       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 5)                 0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 24        \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4)                 0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 10        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 234\n",
      "Trainable params: 234\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uSa3lsUp0YIT"
   },
   "source": [
    "####  \n",
    "### 모델 컴파일 – 다중 분류 모델"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "Y_train.shape"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DYEQKGgTIZ6S",
    "outputId": "5509172a-71d6-4b7e-e13e-e757eac4394f"
   },
   "execution_count": 8,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Y_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[8], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mY_train\u001B[49m\u001B[38;5;241m.\u001B[39mshape\n",
      "\u001B[1;31mNameError\u001B[0m: name 'Y_train' is not defined"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "8uqwCzFC0YIT"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='sparse_categorical_crossentropy', \n",
    "              metrics=['accuracy']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RhUlTphR0YIT"
   },
   "source": [
    "####  \n",
    "### 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FfooZ2jr0YIT",
    "outputId": "12da8856-e37a-4d85-eaf7-0e7cc0be755b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[116], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m history \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mY_train\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      2\u001B[0m \u001B[43m          \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_test\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mY_test\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m          \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m20\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m          \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m16\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     67\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[0;32m     68\u001B[0m     \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[0;32m     69\u001B[0m     \u001B[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001B[39;00m\n\u001B[1;32m---> 70\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m     71\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[0;32m     72\u001B[0m     \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
      "File \u001B[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_fileqffmpmo0.py:15\u001B[0m, in \u001B[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001B[1;34m(iterator)\u001B[0m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m     14\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m---> 15\u001B[0m     retval_ \u001B[38;5;241m=\u001B[39m ag__\u001B[38;5;241m.\u001B[39mconverted_call(ag__\u001B[38;5;241m.\u001B[39mld(step_function), (ag__\u001B[38;5;241m.\u001B[39mld(\u001B[38;5;28mself\u001B[39m), ag__\u001B[38;5;241m.\u001B[39mld(iterator)), \u001B[38;5;28;01mNone\u001B[39;00m, fscope)\n\u001B[0;32m     16\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m:\n\u001B[0;32m     17\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "\u001B[1;31mValueError\u001B[0m: in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train,\n",
    "          validation_data=(X_test, Y_test),\n",
    "          epochs=20, \n",
    "          batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qsohXyXZ0YIT"
   },
   "source": [
    "####  \n",
    "### Callback : 조기종료, 모델 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "id": "z7d_nFMf0YIU"
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "id": "hoOULwhL0YIU"
   },
   "outputs": [],
   "source": [
    "# val_loss 모니터링해서 성능이 5번 지나도록 좋아지지 않으면 조기 종료\n",
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "id": "bKT5doN10YIU"
   },
   "outputs": [],
   "source": [
    "# val_loss 가장 낮은 값을 가질때마다 모델저장\n",
    "check_point = ModelCheckpoint('best_model.h5', verbose=1,                              monitor='val_loss', mode='min', save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3IUAfPnJ0YIU"
   },
   "source": [
    "|####\n",
    "### 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-TYvrlT90YIU",
    "outputId": "09847321-27c5-47ac-b6ae-2d67258c5821"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[126], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m history \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mY_train\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      2\u001B[0m \u001B[43m          \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m50\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m20\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m          \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_test\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mY_test\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m          \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mearly_stop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheck_point\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     67\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[0;32m     68\u001B[0m     \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[0;32m     69\u001B[0m     \u001B[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001B[39;00m\n\u001B[1;32m---> 70\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m     71\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[0;32m     72\u001B[0m     \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
      "File \u001B[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_fileqffmpmo0.py:15\u001B[0m, in \u001B[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001B[1;34m(iterator)\u001B[0m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m     14\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m---> 15\u001B[0m     retval_ \u001B[38;5;241m=\u001B[39m ag__\u001B[38;5;241m.\u001B[39mconverted_call(ag__\u001B[38;5;241m.\u001B[39mld(step_function), (ag__\u001B[38;5;241m.\u001B[39mld(\u001B[38;5;28mself\u001B[39m), ag__\u001B[38;5;241m.\u001B[39mld(iterator)), \u001B[38;5;28;01mNone\u001B[39;00m, fscope)\n\u001B[0;32m     16\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m:\n\u001B[0;32m     17\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "\u001B[1;31mValueError\u001B[0m: in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=X_train, y=Y_train,\n",
    "          epochs=50 , batch_size=20,\n",
    "          validation_data=(X_test, Y_test), verbose=1,\n",
    "          callbacks=[early_stop, check_point])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pWF_TAU-0YIV"
   },
   "source": [
    "####  \n",
    "## ⑧ 모델 성능 평가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LwkmcZV90YIV"
   },
   "source": [
    "### 성능 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "p3Z4OGG40YIW",
    "outputId": "63083a11-6a14-488a-9efe-b5e9971ba85d"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[136], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# plt.plot(history.history['accuracy'])\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(\u001B[43mhistory\u001B[49m\u001B[38;5;241m.\u001B[39mhistory[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mval_accuracy\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m      3\u001B[0m plt\u001B[38;5;241m.\u001B[39mtitle(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAccuracy\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m      4\u001B[0m plt\u001B[38;5;241m.\u001B[39mxlabel(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mEpochs\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "# plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Acc')\n",
    "plt.legend(['acc', 'val_acc'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1u_eyBJL0YIW"
   },
   "source": [
    "### 성능 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "id": "7tztF7yi0YIW"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "id": "3IWrThr20YIW"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2137, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2123, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2111, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2079, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[138], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m pred \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_test\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     67\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[0;32m     68\u001B[0m     \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[0;32m     69\u001B[0m     \u001B[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001B[39;00m\n\u001B[1;32m---> 70\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m     71\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[0;32m     72\u001B[0m     \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
      "File \u001B[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_filefa20e9xw.py:15\u001B[0m, in \u001B[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__predict_function\u001B[1;34m(iterator)\u001B[0m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m     14\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m---> 15\u001B[0m     retval_ \u001B[38;5;241m=\u001B[39m ag__\u001B[38;5;241m.\u001B[39mconverted_call(ag__\u001B[38;5;241m.\u001B[39mld(step_function), (ag__\u001B[38;5;241m.\u001B[39mld(\u001B[38;5;28mself\u001B[39m), ag__\u001B[38;5;241m.\u001B[39mld(iterator)), \u001B[38;5;28;01mNone\u001B[39;00m, fscope)\n\u001B[0;32m     16\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m:\n\u001B[0;32m     17\u001B[0m     do_return \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "\u001B[1;31mValueError\u001B[0m: in user code:\n\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2137, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2123, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2111, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\training.py\", line 2079, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\perar\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_3\" is incompatible with the layer: expected shape=(None, 39), found shape=(None, 38)\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y_q7Pz-j0YIW",
    "outputId": "cb2c251e-c8a3-4e9d-cba3-430daad593c4"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[140], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpred\u001B[49m\u001B[38;5;241m.\u001B[39mshape()\n",
      "\u001B[1;31mNameError\u001B[0m: name 'pred' is not defined"
     ]
    }
   ],
   "source": [
    "pred.shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "ogC2i7810YIa"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[134], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m y_pred \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39margmax(\u001B[43mpred\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'pred' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred = np.argmax(pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "y_pred"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gI0BTOmJJxCd",
    "outputId": "ed2108fa-2fde-4489-cfe8-26becc001877"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nEI_Exmv0YIa",
    "outputId": "65c6a540-5f32-48c9-ca7c-4146d0b46f50"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[130], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# 정확도 80%\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m accuracy_score(Y_train, \u001B[43my_pred\u001B[49m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "# 정확도 80%\n",
    "accuracy_score(Y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ftb7Y6K_0YIa",
    "outputId": "59478c7f-58fc-4a82-ebfd-84a6e7ac4119"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'recall_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[10], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# 재현율 성능이 좋지 않다\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m \u001B[43mrecall_score\u001B[49m(Y_test, y_pred)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'recall_score' is not defined"
     ]
    }
   ],
   "source": [
    "# 재현율 성능이 좋지 않다\n",
    "recall_score(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zEdx0ESm0YIb",
    "outputId": "041c0358-f521-4681-a2d1-1cf9ee5cc673"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.91      0.87      1549\n",
      "           1       0.67      0.49      0.57       560\n",
      "\n",
      "    accuracy                           0.80      2109\n",
      "   macro avg       0.75      0.70      0.72      2109\n",
      "weighted avg       0.79      0.80      0.79      2109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# accuracy, recall, precision 성능 한번에 보기\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7897QYB80YIb"
   },
   "source": [
    "#  \n",
    "# 2. 재현율 성능이 좋지 않다. 어떻게 성능향상 할수 있나?\n",
    "+ 성능향상 할수 있는 방법은 여러가지 있습니다.\n",
    "+ DNN 하이퍼 파라미터 수정하면서 성능향상이 되는지 확인\n",
    "+ 데이터 줄이거나 늘리거나, Feature(컬럼)을 늘리거나 줄이거나 하는 식의 Feature Engineering 방법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fiWiXYwK0YIb"
   },
   "source": [
    "####  \n",
    "## Feature Engineering 통한 성능향상\n",
    "+ 불균현 Churn 데이터 균형 맞추기 : OverSampling, UnderSampling\n",
    "+ OverSampling 기법 : SMOTE(Synthetic Minority Over-sampling Technique) \n",
    "+ 참조사이트 : https://datascienceschool.net/03%20machine%20learning/14.02%20%EB%B9%84%EB%8C%80%EC%B9%AD%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%AC%B8%EC%A0%9C.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b3YkI1TY0YIb"
   },
   "source": [
    "![SMOTE](https://github.com/gzone2000/TEMP_TEST/raw/master/SMOTE.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kstnDhLB0YIb"
   },
   "source": [
    "### imbalanced-learn 패키지 설치\n",
    "+ imbalanced data 문제를 해결하기 위한 다양한 샘플링 방법을 구현한 파이썬 패키지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0Z0Qetyc0YIb",
    "outputId": "74f3982c-d77b-4ba4-af64-d75b2208854d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
      "Collecting imbalanced-learn\n",
      "  Downloading imbalanced_learn-0.9.0-py3-none-any.whl (199 kB)\n",
      "\u001B[K     |████████████████████████████████| 199 kB 5.3 MB/s \n",
      "\u001B[?25hRequirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.4.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (3.1.0)\n",
      "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.21.5)\n",
      "Requirement already satisfied: scikit-learn>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.1.0)\n",
      "Installing collected packages: imbalanced-learn\n",
      "  Attempting uninstall: imbalanced-learn\n",
      "    Found existing installation: imbalanced-learn 0.8.1\n",
      "    Uninstalling imbalanced-learn-0.8.1:\n",
      "      Successfully uninstalled imbalanced-learn-0.8.1\n",
      "Successfully installed imbalanced-learn-0.9.0\n"
     ]
    }
   ],
   "source": [
    "!pip install -U imbalanced-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3dvd2MA-0YIc"
   },
   "source": [
    "####  \n",
    "### SMOTE 함수 이용하여 Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wCdb2lYr0YIc"
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vr-FkjZC0YIc"
   },
   "outputs": [],
   "source": [
    "# SMOTE 함수 정의 및 Oversampling 수행\n",
    "\n",
    "smote = SMOTE(random_state=0)\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AWs6dQU50YIc",
    "outputId": "3ddffd0c-ace0-48d4-b715-a8e281c1dedf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (4918, 39) (4918,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (7224, 39) (7224,)\n"
     ]
    }
   ],
   "source": [
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RPPj3I2T0YId",
    "outputId": "e4e1ac8c-e05f-4bc4-b2d7-b4cfa5474f90"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    3612\n",
       "1    3612\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "# SMOTE 적용 후 레이블 값 분포 : 0과 1 갯수가 동일 \n",
    "pd.Series(y_train_over).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KWSRIlA20YId"
   },
   "source": [
    "####  \n",
    "### 데이터 정규화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2Ykxk8kL0YId"
   },
   "outputs": [],
   "source": [
    "# MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_over = scaler.transform(X_train_over)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sg6z7eC30YId",
    "outputId": "136129d2-4374-4b85-e086-d12e29f52a6c"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "((7224, 39), (7224,), (2109, 39), (2109,))"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "X_train_over.shape, y_train_over.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXNCh5DU0YId"
   },
   "source": [
    "####  \n",
    "### 모델 개발(Creating the Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aqHIkUKF0YIe"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(39,)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q11y4xL00YIe"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='sparse_categorical_crossentropy', \n",
    "              metrics=['accuracy']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BEtI8XeR0YIe"
   },
   "outputs": [],
   "source": [
    "# 여기서는 val_accuracy 모니터링해서 성능이 좋아지지 않으면 조기 종료 하게 함.\n",
    "early_stop = EarlyStopping(monitor='val_accuracy', mode='max', \n",
    "                           verbose=1, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rby6O0xa0YIe"
   },
   "outputs": [],
   "source": [
    "check_point = ModelCheckpoint('best_model.h5', verbose=1,\n",
    "                              monitor='val_loss', mode='min', \n",
    "                              save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-VXAZpC00YIe",
    "outputId": "444dab34-c2c3-4314-b2b8-8205e7fc37f6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/50\n",
      "216/226 [===========================>..] - ETA: 0s - loss: 0.5767 - accuracy: 0.6998\n",
      "Epoch 1: val_loss improved from inf to 0.48758, saving model to best_model.h5\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.5764 - accuracy: 0.7004 - val_loss: 0.4876 - val_accuracy: 0.7288\n",
      "Epoch 2/50\n",
      "225/226 [============================>.] - ETA: 0s - loss: 0.5140 - accuracy: 0.7569\n",
      "Epoch 2: val_loss did not improve from 0.48758\n",
      "226/226 [==============================] - 1s 4ms/step - loss: 0.5145 - accuracy: 0.7568 - val_loss: 0.5001 - val_accuracy: 0.7202\n",
      "Epoch 3/50\n",
      "223/226 [============================>.] - ETA: 0s - loss: 0.5008 - accuracy: 0.7622\n",
      "Epoch 3: val_loss did not improve from 0.48758\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.5029 - accuracy: 0.7619 - val_loss: 0.4921 - val_accuracy: 0.7302\n",
      "Epoch 4/50\n",
      "213/226 [===========================>..] - ETA: 0s - loss: 0.4892 - accuracy: 0.7724\n",
      "Epoch 4: val_loss did not improve from 0.48758\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4912 - accuracy: 0.7702 - val_loss: 0.4993 - val_accuracy: 0.7288\n",
      "Epoch 5/50\n",
      "222/226 [============================>.] - ETA: 0s - loss: 0.4875 - accuracy: 0.7703\n",
      "Epoch 5: val_loss improved from 0.48758 to 0.48548, saving model to best_model.h5\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4872 - accuracy: 0.7701 - val_loss: 0.4855 - val_accuracy: 0.7435\n",
      "Epoch 6/50\n",
      "215/226 [===========================>..] - ETA: 0s - loss: 0.4794 - accuracy: 0.7778\n",
      "Epoch 6: val_loss improved from 0.48548 to 0.46920, saving model to best_model.h5\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4806 - accuracy: 0.7770 - val_loss: 0.4692 - val_accuracy: 0.7496\n",
      "Epoch 7/50\n",
      "204/226 [==========================>...] - ETA: 0s - loss: 0.4686 - accuracy: 0.7854\n",
      "Epoch 7: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4722 - accuracy: 0.7820 - val_loss: 0.5052 - val_accuracy: 0.7198\n",
      "Epoch 8/50\n",
      "215/226 [===========================>..] - ETA: 0s - loss: 0.4730 - accuracy: 0.7826\n",
      "Epoch 8: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4726 - accuracy: 0.7814 - val_loss: 0.4725 - val_accuracy: 0.7487\n",
      "Epoch 9/50\n",
      "213/226 [===========================>..] - ETA: 0s - loss: 0.4679 - accuracy: 0.7801\n",
      "Epoch 9: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4676 - accuracy: 0.7802 - val_loss: 0.4699 - val_accuracy: 0.7501\n",
      "Epoch 10/50\n",
      "219/226 [============================>.] - ETA: 0s - loss: 0.4579 - accuracy: 0.7865\n",
      "Epoch 10: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4568 - accuracy: 0.7874 - val_loss: 0.5129 - val_accuracy: 0.7165\n",
      "Epoch 11/50\n",
      "208/226 [==========================>...] - ETA: 0s - loss: 0.4562 - accuracy: 0.7910\n",
      "Epoch 11: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4577 - accuracy: 0.7901 - val_loss: 0.5067 - val_accuracy: 0.7240\n",
      "Epoch 12/50\n",
      "215/226 [===========================>..] - ETA: 0s - loss: 0.4545 - accuracy: 0.7885\n",
      "Epoch 12: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4536 - accuracy: 0.7895 - val_loss: 0.4892 - val_accuracy: 0.7373\n",
      "Epoch 13/50\n",
      "215/226 [===========================>..] - ETA: 0s - loss: 0.4430 - accuracy: 0.7951\n",
      "Epoch 13: val_loss did not improve from 0.46920\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4461 - accuracy: 0.7921 - val_loss: 0.4716 - val_accuracy: 0.7544\n",
      "Epoch 14/50\n",
      "216/226 [===========================>..] - ETA: 0s - loss: 0.4434 - accuracy: 0.7956\n",
      "Epoch 14: val_loss improved from 0.46920 to 0.46211, saving model to best_model.h5\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4447 - accuracy: 0.7949 - val_loss: 0.4621 - val_accuracy: 0.7549\n",
      "Epoch 15/50\n",
      "208/226 [==========================>...] - ETA: 0s - loss: 0.4433 - accuracy: 0.8000\n",
      "Epoch 15: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4408 - accuracy: 0.8020 - val_loss: 0.5209 - val_accuracy: 0.7179\n",
      "Epoch 16/50\n",
      "205/226 [==========================>...] - ETA: 0s - loss: 0.4478 - accuracy: 0.7971\n",
      "Epoch 16: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4462 - accuracy: 0.7973 - val_loss: 0.4783 - val_accuracy: 0.7553\n",
      "Epoch 17/50\n",
      "217/226 [===========================>..] - ETA: 0s - loss: 0.4375 - accuracy: 0.7995\n",
      "Epoch 17: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4393 - accuracy: 0.7982 - val_loss: 0.5024 - val_accuracy: 0.7297\n",
      "Epoch 18/50\n",
      "220/226 [============================>.] - ETA: 0s - loss: 0.4310 - accuracy: 0.8060\n",
      "Epoch 18: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4326 - accuracy: 0.8058 - val_loss: 0.5032 - val_accuracy: 0.7278\n",
      "Epoch 19/50\n",
      "212/226 [===========================>..] - ETA: 0s - loss: 0.4270 - accuracy: 0.8011\n",
      "Epoch 19: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 3ms/step - loss: 0.4278 - accuracy: 0.8008 - val_loss: 0.4931 - val_accuracy: 0.7373\n",
      "Epoch 20/50\n",
      "209/226 [==========================>...] - ETA: 0s - loss: 0.4315 - accuracy: 0.8103\n",
      "Epoch 20: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4304 - accuracy: 0.8101 - val_loss: 0.4865 - val_accuracy: 0.7515\n",
      "Epoch 21/50\n",
      "214/226 [===========================>..] - ETA: 0s - loss: 0.4243 - accuracy: 0.8121\n",
      "Epoch 21: val_loss did not improve from 0.46211\n",
      "226/226 [==============================] - 1s 2ms/step - loss: 0.4253 - accuracy: 0.8116 - val_loss: 0.4962 - val_accuracy: 0.7373\n",
      "Epoch 21: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=X_train_over, y=y_train_over, \n",
    "          epochs=50 , batch_size=32,\n",
    "          validation_data=(X_test, y_test), verbose=1,\n",
    "          callbacks=[early_stop, check_point])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gEARelPF0YIf"
   },
   "source": [
    "####  \n",
    "### 모델 성능 평가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D-Jy63Ku0YIf"
   },
   "source": [
    "#### 성능 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "Dscj1l_C0YIg",
    "outputId": "8a4e2fa5-157e-4028-83cb-a52a5fc9765b"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Acc')\n",
    "plt.legend(['acc', 'val_acc'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JxdSoKeK0YIg"
   },
   "source": [
    "#### 성능 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RcFwYbAw0YIg"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K8q5K66K0YIg"
   },
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o6aPdPsm0YIg",
    "outputId": "7ea305ea-02bb-4f1e-ce25-84949540d36e"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(2109, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DgLTZU3r0YIh"
   },
   "outputs": [],
   "source": [
    "y_pred = np.argmax(pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HnJuNtcG0YIh",
    "outputId": "867a9225-3ab6-4d5c-de09-754b7afe82bb"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7373162636320532"
      ]
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "# 정확도 70~80%\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "29pLPidt0YIh",
    "outputId": "06379a7d-f204-4a83-cd8c-4ec2eb2220bb"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7892857142857143"
      ]
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "# 재현율 70% 정도로 이전보다 좋아졌다.\n",
    "recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-inYBRsx0YIh",
    "outputId": "4d363171-48e1-44de-fa0a-b67c3117e521"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.72      0.80      1549\n",
      "           1       0.50      0.79      0.61       560\n",
      "\n",
      "    accuracy                           0.74      2109\n",
      "   macro avg       0.70      0.75      0.71      2109\n",
      "weighted avg       0.80      0.74      0.75      2109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# recall 성능을 올렸지만, 반대급부로 precision 성능은 떨어진다.\n",
    "# accuracy, recall, precision 어떤것에 집중할지 선택하는것도 필요하다.\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KsnemPIy0YIi"
   },
   "source": [
    "###  \n",
    "## 배운 내용 정리\n",
    "1. 딥러닝 심층신경망(DNN) 모델 프로세스\n",
    " + 데이터 가져오기\n",
    " + 데이터 전처리\n",
    " + Train, Test 데이터셋 분할\n",
    " + 데이터 정규화\n",
    " + DNN 딥러닝 모델\n",
    "2. 재현율 성능이 좋지 않다. 어떻게 성능향상 방법은?\n",
    " + Feature Engineering : 성능 잘 나올수 있도록 데이터 가공\n",
    " + 불균현 데이터 문제 해소 : under-sampling, over-sampling\n",
    " + Over-Sampling 기법 : SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cphLsim20YIi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "wlJpMmfenj3R"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "2gEO7fRTnj0Y"
   },
   "execution_count": null,
   "outputs": []
  }
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