Python활용AI모델링_01_전처리_퀴즈_easy_v3_20220219.ipynb

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   "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_01_%EC%A0%84%EC%B2%98%EB%A6%AC_%ED%80%B4%EC%A6%88_easy_v3_20220219.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "471_omVbzt5v"
   },
   "source": [
    "# [실습-퀴즈] Python 활용한 AI 모델링 - 전처리 파트\n",
    "+ 이번시간에는 Python을 활용한 AI 모델링에서 전처리에 대해 실습해 보겠습니다.\n",
    "+ 머신러닝과 AI 모델링 전체에서 60~70% 차지하는 부분이 바로 전처리 파트입니다.\n",
    "+ 굉장히 시간과 노력이 많이 투입되며, 어려운 부분일수 있습니다.\n",
    "+ 데이터가 깨끗이 정리되지 않는다면 머신러닝/AI 성능을 장담할수 없으므로 데이터 전처리에 심혈을 기울려 주시기 바랍니다.\n",
    "+ 한가지 당부 드리고 싶은 말은 \"백문이불여일타\" 입니다. \n",
    "+ 이론보다 실습이 더 많은 시간과 노력이 투자 되어야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2tiRXBd_zt50"
   },
   "source": [
    "## 학습목차\n",
    "0. 실습 내용 확인\n",
    "1. 필요 라이브러리 임포트 및 파일 읽어오기\n",
    "2. EDA (Exploratory Data Analysis) 탐색적 데이터 분석 : 데이터 분석?\n",
    "3. 데이터 전처리 수행\n",
    " - 불필요 컬럼 삭제\n",
    " - 컬럼 내용 변경하기\n",
    " - Null 처리\n",
    " - 컬럼 type 변경하기\n",
    "4. 시각화\n",
    "5. 결과 저장하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6XPa1owzt50"
   },
   "source": [
    "##  \n",
    "# 0. 실습 내용 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d0UfUo5xzt51"
   },
   "source": [
    "## <font color=blue> **머신러닝, 딥러닝을 사용한 통신 서비스 이탈 예측** </font>\n",
    "### 모든 관련 고객 데이터를 분석하고 강력하고 정확한 이탈 예측 모델을 개발하여 고객을 유지하고 고객 이탈률을 줄이기 위한 전략을 수립합니다.\n",
    "### Churn은 서비스를 중단하거나 업계의 경쟁업체로 이전한 고객 또는 사용자를 의미합니다. 모든 조직이 기존 고객을 유지하고 새로운 고객을 유치하는 것이 매우 중요합니다. 그 중 하나가 실패하면 비즈니스에 좋지 않습니다. 목표는 업계에서 경쟁 우위를 유지하기 위해 이탈 예측을 위한 머신러닝, 딥러닝의 가능성을 탐색하는 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fzXI1Ty4zt52"
   },
   "source": [
    "#  \n",
    "# 1. 필요 라이브러리 임포트 및 파일 읽어오기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqhbiL7hzt52"
   },
   "source": [
    "### Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "73Ke8PLdzt53"
   },
   "source": [
    "##### <font color=blue> **[문제] numpy 라이브러리를 np alias로 임포트하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "AwQq6UUFzt54"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MFJooTutzt56"
   },
   "source": [
    "####  \n",
    "### Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drnb3ta-zt56"
   },
   "source": [
    "##### <font color=blue> **[문제] pandas 라이브러리를 pd alias로 임포트하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "EMZobYP2zt57"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BlLqoxDgzt57"
   },
   "source": [
    "####  \n",
    "### <font color=green>읽어올 데이터 파일 : data_v1.csv</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jt8N_9ybzt58"
   },
   "source": [
    "#### Telco Customer Churn Dataset  컬럼\n",
    "1. CustomerID: Customer ID unique for each customer\n",
    "2. gender: Whether the customer is a male or a female\n",
    "3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) : 고령자 여부\n",
    "4. Partner: Whether the customer has a partner or not (Yes, No)\n",
    "5. Dependents: Whether the customer has dependents or not (Yes, No) : 부양가족 여부\n",
    "6. Tenure: Number of months the customer has stayed with the company : 서비스 사용 개월수\n",
    "7. PhoneService: Whether the customer has a phone service or not (Yes, No)\n",
    "8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "9. InternetService: Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service)\n",
    "11. OnlineBackup: Whether the customer has an online backup or not (Yes, No, No internet service)\n",
    "12. DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "13. TechSupport: Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "14. StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "15. StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "16. Contract: The contract term of the customer (Month-to-month, One year, Two years)\n",
    "17. PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)\n",
    "18. PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "19. MonthlyCharges: The amount charged to the customer monthly\n",
    "20. TotalCharges: The total amount charged to the customer\n",
    "21. Churn: Whether the customer churned or not (Yes or No) : 이탈 여부"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lPQqI6x-zt58"
   },
   "source": [
    "####  \n",
    "### CSV 파일 데이터 읽어오기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7XcVbE7lzt59"
   },
   "source": [
    "##### <font color=blue> **[문제] data.csv 파일을 Pandas read_csv 함수를 이용하여 읽어 df 변수에 저장하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "JtlO-NTgzt59"
   },
   "outputs": [],
   "source": [
    "# 읽어 들일 파일명 : data_v1.csv\n",
    "# Pandas read_csv 함수 활용\n",
    "# 결과 : df 저장\n",
    "df = pd.read_csv('pyAice_data/data_v1.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "outputId": "440e23b8-fee3-4b31-cb87-df4d9eca749b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
      "0     7590-VHVEG     NaN            0.0     Yes         No       1   \n",
      "1     5575-GNVDE    Male            0.0      No         No      34   \n",
      "2     3668-QPYBK    Male            0.0      No         No       2   \n",
      "3     7795-CFOCW    Male            0.0      No         No      45   \n",
      "4     9237-HQITU  Female            0.0      No         No       2   \n",
      "...          ...     ...            ...     ...        ...     ...   \n",
      "7038  6840-RESVB    Male            0.0     Yes        Yes      24   \n",
      "7039  2234-XADUH  Female            0.0     Yes        Yes      72   \n",
      "7040  4801-JZAZL  Female            0.0     Yes        Yes      11   \n",
      "7041  8361-LTMKD    Male            1.0     Yes         No       4   \n",
      "7042  3186-AJIEK     NaN            0.0      No         No      66   \n",
      "\n",
      "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
      "0              No  No phone service             DSL             No  ...   \n",
      "1             Yes                No             DSL            Yes  ...   \n",
      "2             Yes                No             DSL            Yes  ...   \n",
      "3              No  No phone service             DSL            Yes  ...   \n",
      "4             Yes                No     Fiber optic             No  ...   \n",
      "...           ...               ...             ...            ...  ...   \n",
      "7038          Yes               Yes             DSL            Yes  ...   \n",
      "7039          Yes               Yes     Fiber optic             No  ...   \n",
      "7040           No  No phone service             DSL            Yes  ...   \n",
      "7041          Yes               Yes     Fiber optic             No  ...   \n",
      "7042          Yes                No     Fiber optic            Yes  ...   \n",
      "\n",
      "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
      "0                  No          No          No              No             NaN   \n",
      "1                 Yes          No          No              No        One year   \n",
      "2                 NaN          No          No              No  Month-to-month   \n",
      "3                 NaN         Yes          No              No        One year   \n",
      "4                 NaN          No          No              No  Month-to-month   \n",
      "...               ...         ...         ...             ...             ...   \n",
      "7038              Yes         Yes         Yes             Yes        One year   \n",
      "7039              Yes          No         Yes             Yes        One year   \n",
      "7040               No          No          No              No  Month-to-month   \n",
      "7041               No          No          No              No  Month-to-month   \n",
      "7042              Yes         Yes         Yes             Yes        Two year   \n",
      "\n",
      "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
      "0                 Yes           Electronic check          29.85         29.85   \n",
      "1                  No               Mailed check          56.95        1889.5   \n",
      "2                 Yes               Mailed check          53.85        108.15   \n",
      "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
      "4                 Yes           Electronic check          70.70        151.65   \n",
      "...               ...                        ...            ...           ...   \n",
      "7038              Yes               Mailed check          84.80        1990.5   \n",
      "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
      "7040              Yes           Electronic check          29.60        346.45   \n",
      "7041              Yes               Mailed check          74.40         306.6   \n",
      "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
      "\n",
      "     Churn  \n",
      "0       No  \n",
      "1       No  \n",
      "2      Yes  \n",
      "3       No  \n",
      "4      Yes  \n",
      "...    ...  \n",
      "7038    No  \n",
      "7039    No  \n",
      "7040    No  \n",
      "7041   Yes  \n",
      "7042    No  \n",
      "\n",
      "[7043 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "# 읽어온 데이터프레임 확인하기\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dY2h_-WQzt5-"
   },
   "source": [
    "#  \n",
    "# 2. EDA (Exploratory Data Analysis) 탐색적 데이터 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AhWFhfq3zt5_"
   },
   "source": [
    "### 데이터 탐색하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "_fhnLu_Tzt5_",
    "outputId": "fc708ff9-511c-4169-a3ef-f7860ff77e08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n0  7590-VHVEG     NaN            0.0     Yes         No       1           No   \n1  5575-GNVDE    Male            0.0      No         No      34          Yes   \n2  3668-QPYBK    Male            0.0      No         No       2          Yes   \n3  7795-CFOCW    Male            0.0      No         No      45           No   \n4  9237-HQITU  Female            0.0      No         No       2          Yes   \n\n      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n0  No phone service             DSL             No  ...               No   \n1                No             DSL            Yes  ...              Yes   \n2                No             DSL            Yes  ...              NaN   \n3  No phone service             DSL            Yes  ...              NaN   \n4                No     Fiber optic             No  ...              NaN   \n\n  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n0          No          No              No             NaN              Yes   \n1          No          No              No        One year               No   \n2          No          No              No  Month-to-month              Yes   \n3         Yes          No              No        One year               No   \n4          No          No              No  Month-to-month              Yes   \n\n               PaymentMethod MonthlyCharges  TotalCharges Churn  \n0           Electronic check          29.85         29.85    No  \n1               Mailed check          56.95        1889.5    No  \n2               Mailed check          53.85        108.15   Yes  \n3  Bank transfer (automatic)          42.30       1840.75    No  \n4           Electronic check          70.70        151.65   Yes  \n\n[5 rows x 21 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>customerID</th>\n      <th>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>DeviceProtection</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>7590-VHVEG</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5575-GNVDE</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</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>1889.5</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3668-QPYBK</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>NaN</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>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7795-CFOCW</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>NaN</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>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9237-HQITU</td>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</td>\n      <td>NaN</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>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
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   "execution_count": 8,
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    "id": "uRcoM31azt5_",
    "outputId": "81460218-e776-4330-fd65-fe0c7f07121b"
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   "outputs": [
    {
     "data": {
      "text/plain": "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n7038  6840-RESVB    Male            0.0     Yes        Yes      24   \n7039  2234-XADUH  Female            0.0     Yes        Yes      72   \n7040  4801-JZAZL  Female            0.0     Yes        Yes      11   \n7041  8361-LTMKD    Male            1.0     Yes         No       4   \n7042  3186-AJIEK     NaN            0.0      No         No      66   \n\n     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n7038          Yes               Yes             DSL            Yes  ...   \n7039          Yes               Yes     Fiber optic             No  ...   \n7040           No  No phone service             DSL            Yes  ...   \n7041          Yes               Yes     Fiber optic             No  ...   \n7042          Yes                No     Fiber optic            Yes  ...   \n\n     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n7038              Yes         Yes         Yes             Yes        One year   \n7039              Yes          No         Yes             Yes        One year   \n7040               No          No          No              No  Month-to-month   \n7041               No          No          No              No  Month-to-month   \n7042              Yes         Yes         Yes             Yes        Two year   \n\n     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n7038              Yes               Mailed check          84.80        1990.5   \n7039              Yes    Credit card (automatic)         103.20        7362.9   \n7040              Yes           Electronic check          29.60        346.45   \n7041              Yes               Mailed check          74.40         306.6   \n7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n\n     Churn  \n7038    No  \n7039    No  \n7040    No  \n7041   Yes  \n7042    No  \n\n[5 rows x 21 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>customerID</th>\n      <th>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>DeviceProtection</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>7038</th>\n      <td>6840-RESVB</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>24</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</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>1990.5</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>7039</th>\n      <td>2234-XADUH</td>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>72</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</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>7362.9</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>7040</th>\n      <td>4801-JZAZL</td>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>11</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</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>No</td>\n    </tr>\n    <tr>\n      <th>7041</th>\n      <td>8361-LTMKD</td>\n      <td>Male</td>\n      <td>1.0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>4</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</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>306.6</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>7042</th>\n      <td>3186-AJIEK</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>66</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>105.65</td>\n      <td>6844.5</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VJYiz0u_zt6A"
   },
   "source": [
    "####  \n",
    "### 자료구조 파악"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QkjgjO1Wzt6A",
    "outputId": "6a1da062-1c77-405d-8619-de4f05cc02e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7034 non-null   object \n",
      " 2   SeniorCitizen     7042 non-null   float64\n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7041 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7040 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  3580 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7042 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7042 non-null   object \n",
      " 18  MonthlyCharges    7042 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(1), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Stjl-h0ozt6A"
   },
   "source": [
    "####  \n",
    "### 데이터 타입, 인덱스, 컬럼명, Values 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F4vmCj_1zt6B",
    "outputId": "68eb3266-3266-41a8-f70c-1e87cb434c0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "RangeIndex(start=0, stop=7043, step=1)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인덱스\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8n0g3dtmzt6B",
    "outputId": "86a531f0-2eb4-441a-ec00-423cca040997"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n      dtype='object')"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼명\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XarNNK4Bzt6B",
    "outputId": "720f1a15-31fb-408c-96db-b08c0fa1a756"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([['7590-VHVEG', nan, 0.0, ..., 29.85, '29.85', 'No'],\n",
       "       ['5575-GNVDE', 'Male', 0.0, ..., 56.95, '1889.5', 'No'],\n",
       "       ['3668-QPYBK', 'Male', 0.0, ..., 53.85, '108.15', 'Yes'],\n",
       "       ...,\n",
       "       ['4801-JZAZL', 'Female', 0.0, ..., 29.6, '346.45', 'No'],\n",
       "       ['8361-LTMKD', 'Male', 1.0, ..., 74.4, '306.6', 'Yes'],\n",
       "       ['3186-AJIEK', nan, 0.0, ..., 105.65, '6844.5', 'No']],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Values\n",
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYtIzNkozt6C"
   },
   "source": [
    "####  \n",
    "### Null 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7MXtq6-Qzt6C",
    "outputId": "65b9acd0-9e93-408b-aff5-f50ccc17e493"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "customerID             0\ngender                 9\nSeniorCitizen          1\nPartner                0\nDependents             2\ntenure                 0\nPhoneService           3\nMultipleLines          0\nInternetService        0\nOnlineSecurity         0\nOnlineBackup           0\nDeviceProtection    3463\nTechSupport            0\nStreamingTV            0\nStreamingMovies        0\nContract               1\nPaperlessBilling       0\nPaymentMethod          1\nMonthlyCharges         1\nTotalCharges           0\nChurn                  0\ndtype: int64"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OUMKMO-xzt6C"
   },
   "source": [
    "####  \n",
    "### 통계 정보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "Yv73rg6Fzt6C",
    "outputId": "398e837a-dbb6-49c6-886d-016bbde7d802"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       SeniorCitizen       tenure  MonthlyCharges\ncount    7042.000000  7043.000000     7042.000000\nmean        0.162170    32.371149       64.763256\nstd         0.368633    24.559481       30.091898\nmin         0.000000     0.000000       18.250000\n25%         0.000000     9.000000       35.500000\n50%         0.000000    29.000000       70.350000\n75%         0.000000    55.000000       89.850000\nmax         1.000000    72.000000      118.750000",
      "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>SeniorCitizen</th>\n      <th>tenure</th>\n      <th>MonthlyCharges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>7042.000000</td>\n      <td>7043.000000</td>\n      <td>7042.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.162170</td>\n      <td>32.371149</td>\n      <td>64.763256</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.368633</td>\n      <td>24.559481</td>\n      <td>30.091898</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>18.250000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>9.000000</td>\n      <td>35.500000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>29.000000</td>\n      <td>70.350000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>55.000000</td>\n      <td>89.850000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>72.000000</td>\n      <td>118.750000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SeniorCitizen 컬럼 : 고령자 여부, 범주형 0,1\n",
    "# tenure : 서비스 사용한 월수로 평균 32개월 사용, 최대 72개월 충성고객 있음 확인\n",
    "#           25%, 50%, 75% 비율과 Max를 보니, 아무래도 Outliner 있는것으로 사료됨.\n",
    "# MonthlyCharges : 월사용요금, 평균 64$ , 최소 18$, 최대 118$\n",
    "# 숫자형 데이터만 나온는 듯?\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GynhTmEpzt6D"
   },
   "source": [
    "#  \n",
    "# 3. 데이터 전처리 수행"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IqJ28PGuzt6D"
   },
   "source": [
    "####  \n",
    "### 자료구조 파악"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xmf37Pq7zt6D"
   },
   "source": [
    "##### <font color=blue> **[문제] df DataFrame의 함수를 활용해서 자료구조(Row, columnm , Not-null, type)을 파악 하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BUl6gWF5zt6D",
    "outputId": "75d7fe58-96fb-45ff-dd26-bdec2a53f8ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7034 non-null   object \n",
      " 2   SeniorCitizen     7042 non-null   float64\n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7041 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7040 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  3580 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7042 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7042 non-null   object \n",
      " 18  MonthlyCharges    7042 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(1), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# DataFrame info 함수 \n",
    "\n",
    "df.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lm9Px7KEzt6E"
   },
   "source": [
    "####  \n",
    "### 컬럼 삭제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gycCw_rLzt6E"
   },
   "source": [
    "##### <font color=blue> **[문제] df 데이터프레임에서 'customerID' 컬럼 삭제 하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n0  7590-VHVEG     NaN            0.0     Yes         No       1           No   \n1  5575-GNVDE    Male            0.0      No         No      34          Yes   \n2  3668-QPYBK    Male            0.0      No         No       2          Yes   \n3  7795-CFOCW    Male            0.0      No         No      45           No   \n4  9237-HQITU  Female            0.0      No         No       2          Yes   \n\n      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n0  No phone service             DSL             No  ...               No   \n1                No             DSL            Yes  ...              Yes   \n2                No             DSL            Yes  ...              NaN   \n3  No phone service             DSL            Yes  ...              NaN   \n4                No     Fiber optic             No  ...              NaN   \n\n  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n0          No          No              No             NaN              Yes   \n1          No          No              No        One year               No   \n2          No          No              No  Month-to-month              Yes   \n3         Yes          No              No        One year               No   \n4          No          No              No  Month-to-month              Yes   \n\n               PaymentMethod MonthlyCharges  TotalCharges Churn  \n0           Electronic check          29.85         29.85    No  \n1               Mailed check          56.95        1889.5    No  \n2               Mailed check          53.85        108.15   Yes  \n3  Bank transfer (automatic)          42.30       1840.75    No  \n4           Electronic check          70.70        151.65   Yes  \n\n[5 rows x 21 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>customerID</th>\n      <th>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>DeviceProtection</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>7590-VHVEG</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5575-GNVDE</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</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>1889.5</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3668-QPYBK</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>NaN</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>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7795-CFOCW</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>NaN</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>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9237-HQITU</td>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</td>\n      <td>NaN</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>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "eMLDhurCzt6E"
   },
   "outputs": [],
   "source": [
    "# DataFrame drop 함수 \n",
    "# 'customerID' 컬럼 삭제\n",
    "# axis=1 옵션 사용해서 컬럼단위 삭제 수행\n",
    "# inplace=True 옵션 사용하여 df DataFrame에 저장\n",
    "\n",
    "df = df.drop('customerID',axis=1)\n",
    "# df.drop('customerID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gTFXKBt7zt6E",
    "outputId": "0c3b6bbd-1c33-41e6-add3-6340e34c5c9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7034 non-null   object \n",
      " 1   SeniorCitizen     7042 non-null   float64\n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7041 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7040 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  3580 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7042 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7042 non-null   object \n",
      " 17  MonthlyCharges    7042 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   object \n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(1), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# 21컬럼에서 20개 컬럼으로 1개 줄어듬 확인\n",
    "# TotalCharges 컬럼은 사용요금으로 숫자형이어야 하는데 Ojbect로 나옴. 확인 필요\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7fT9Rdfjzt6F"
   },
   "source": [
    "####  \n",
    "### 컬럼 내용 변경하기 \n",
    "\n",
    "범주형 문자 데이터를 숫자 변환하는것은 성능에 많은 영향을 미치므로 꼭 변환하로록 하자.<br>\n",
    "null, _ 문제있는 문자 데이터를 모델링하기 전에 미리 다른 데이터로 변경하거나 필요없을 경우 삭제하도록 하자."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nw6o9Zw5zt6F"
   },
   "source": [
    "#### TotalCharges 컬럼 타입 변경하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CxKGXN9izt6F",
    "outputId": "0e469533-8cf5-40e7-b07e-2d8f905c2e3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0         29.85\n1        1889.5\n2        108.15\n3       1840.75\n4        151.65\n         ...   \n7038     1990.5\n7039     7362.9\n7040     346.45\n7041      306.6\n7042     6844.5\nName: TotalCharges, Length: 7043, dtype: object"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TotalCharges : 월사용요금, 실수형으로 보인다.\n",
    "# df['TotalCharges']\n",
    "# 나온 형태가 시리즈가 된다. => 한개의 컬럼만 가지고 있는 것\n",
    "df['TotalCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "WMkg_8b1zt6F",
    "outputId": "a03a8f47-a528-4589-a8d8-61db56c4f0f4"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ''",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[41], line 4\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# TotalCharges 컬럼 타입을 float로 변경해 보자.\u001B[39;00m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;66;03m# 문자열을 숫자형으로 변경할수 없으므로 에러 발생\u001B[39;00m\n\u001B[1;32m----> 4\u001B[0m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mTotalCharges\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mastype\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\generic.py:6240\u001B[0m, in \u001B[0;36mNDFrame.astype\u001B[1;34m(self, dtype, copy, errors)\u001B[0m\n\u001B[0;32m   6233\u001B[0m     results \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m   6234\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39miloc[:, i]\u001B[38;5;241m.\u001B[39mastype(dtype, copy\u001B[38;5;241m=\u001B[39mcopy)\n\u001B[0;32m   6235\u001B[0m         \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns))\n\u001B[0;32m   6236\u001B[0m     ]\n\u001B[0;32m   6238\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   6239\u001B[0m     \u001B[38;5;66;03m# else, only a single dtype is given\u001B[39;00m\n\u001B[1;32m-> 6240\u001B[0m     new_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_mgr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mastype\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   6241\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_constructor(new_data)\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mastype\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m   6243\u001B[0m \u001B[38;5;66;03m# GH 33113: handle empty frame or series\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\internals\\managers.py:448\u001B[0m, in \u001B[0;36mBaseBlockManager.astype\u001B[1;34m(self, dtype, copy, errors)\u001B[0m\n\u001B[0;32m    447\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mastype\u001B[39m(\u001B[38;5;28mself\u001B[39m: T, dtype, copy: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m, errors: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraise\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m T:\n\u001B[1;32m--> 448\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mastype\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\internals\\managers.py:352\u001B[0m, in \u001B[0;36mBaseBlockManager.apply\u001B[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001B[0m\n\u001B[0;32m    350\u001B[0m         applied \u001B[38;5;241m=\u001B[39m b\u001B[38;5;241m.\u001B[39mapply(f, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    351\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 352\u001B[0m         applied \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mgetattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mf\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    353\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mTypeError\u001B[39;00m, \u001B[38;5;167;01mNotImplementedError\u001B[39;00m):\n\u001B[0;32m    354\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m ignore_failures:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:526\u001B[0m, in \u001B[0;36mBlock.astype\u001B[1;34m(self, dtype, copy, errors)\u001B[0m\n\u001B[0;32m    508\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    509\u001B[0m \u001B[38;5;124;03mCoerce to the new dtype.\u001B[39;00m\n\u001B[0;32m    510\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    522\u001B[0m \u001B[38;5;124;03mBlock\u001B[39;00m\n\u001B[0;32m    523\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    524\u001B[0m values \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvalues\n\u001B[1;32m--> 526\u001B[0m new_values \u001B[38;5;241m=\u001B[39m \u001B[43mastype_array_safe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    528\u001B[0m new_values \u001B[38;5;241m=\u001B[39m maybe_coerce_values(new_values)\n\u001B[0;32m    529\u001B[0m newb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmake_block(new_values)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:299\u001B[0m, in \u001B[0;36mastype_array_safe\u001B[1;34m(values, dtype, copy, errors)\u001B[0m\n\u001B[0;32m    296\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m values\u001B[38;5;241m.\u001B[39mcopy()\n\u001B[0;32m    298\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 299\u001B[0m     new_values \u001B[38;5;241m=\u001B[39m \u001B[43mastype_array\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    300\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mValueError\u001B[39;00m, \u001B[38;5;167;01mTypeError\u001B[39;00m):\n\u001B[0;32m    301\u001B[0m     \u001B[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001B[39;00m\n\u001B[0;32m    302\u001B[0m     \u001B[38;5;66;03m#  trying to convert to float\u001B[39;00m\n\u001B[0;32m    303\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m errors \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:230\u001B[0m, in \u001B[0;36mastype_array\u001B[1;34m(values, dtype, copy)\u001B[0m\n\u001B[0;32m    227\u001B[0m     values \u001B[38;5;241m=\u001B[39m values\u001B[38;5;241m.\u001B[39mastype(dtype, copy\u001B[38;5;241m=\u001B[39mcopy)\n\u001B[0;32m    229\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 230\u001B[0m     values \u001B[38;5;241m=\u001B[39m \u001B[43mastype_nansafe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    232\u001B[0m \u001B[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001B[39;00m\n\u001B[0;32m    233\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(dtype, np\u001B[38;5;241m.\u001B[39mdtype) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(values\u001B[38;5;241m.\u001B[39mdtype\u001B[38;5;241m.\u001B[39mtype, \u001B[38;5;28mstr\u001B[39m):\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:170\u001B[0m, in \u001B[0;36mastype_nansafe\u001B[1;34m(arr, dtype, copy, skipna)\u001B[0m\n\u001B[0;32m    166\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(msg)\n\u001B[0;32m    168\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m copy \u001B[38;5;129;01mor\u001B[39;00m is_object_dtype(arr\u001B[38;5;241m.\u001B[39mdtype) \u001B[38;5;129;01mor\u001B[39;00m is_object_dtype(dtype):\n\u001B[0;32m    169\u001B[0m     \u001B[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001B[39;00m\n\u001B[1;32m--> 170\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43marr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mastype\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m    172\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m arr\u001B[38;5;241m.\u001B[39mastype(dtype, copy\u001B[38;5;241m=\u001B[39mcopy)\n",
      "\u001B[1;31mValueError\u001B[0m: could not convert string to float: ''"
     ]
    }
   ],
   "source": [
    "# TotalCharges 컬럼 타입을 float로 변경해 보자.\n",
    "# 문자열을 숫자형으로 변경할수 없으므로 에러 발생\n",
    "\n",
    "df['TotalCharges'].astype(float) # 실수형 데이터로 바꿀 수 X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1ePbx-y7zt6G",
    "outputId": "1784b8ac-8e01-4425-e4bb-3ed56e1ee017"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "7038    False\n",
       "7039    False\n",
       "7040    False\n",
       "7041    False\n",
       "7042    False\n",
       "Name: TotalCharges, Length: 7043, dtype: bool"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Boolean indexing으로 검색\n",
    "(df['TotalCharges'] == '') | (df['TotalCharges'] == ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "11"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df['TotalCharges'] == '') | (df['TotalCharges'] == ' ')).sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "0       False\n1       False\n2       False\n3       False\n4       False\n        ...  \n7038    False\n7039    False\n7040    False\n7041    False\n7042    False\nName: TotalCharges, Length: 7043, dtype: bool"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotalCharges'] == ' ' # 불린 인덱스"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "-_UvMk4vzt6G",
    "outputId": "8d7f1a41-4395-4717-bd47-bb73a0fb3700"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n488   Female            0.0     Yes        Yes       0           No   \n753     Male            0.0      No        Yes       0          Yes   \n936   Female            0.0     Yes        Yes       0          Yes   \n1082    Male            0.0     Yes        Yes       0          Yes   \n1340  Female            0.0     Yes        Yes       0           No   \n3331    Male            0.0     Yes        Yes       0          Yes   \n3826    Male            0.0     Yes        Yes       0          Yes   \n4380  Female            0.0     Yes        Yes       0          Yes   \n5218    Male            0.0     Yes        Yes       0          Yes   \n6670  Female            0.0     Yes        Yes       0          Yes   \n6754    Male            0.0      No        Yes       0          Yes   \n\n         MultipleLines InternetService       OnlineSecurity  \\\n488   No phone service             DSL                  Yes   \n753                 No              No  No internet service   \n936                 No             DSL                  Yes   \n1082               Yes              No  No internet service   \n1340  No phone service             DSL                  Yes   \n3331                No              No  No internet service   \n3826               Yes              No  No internet service   \n4380                No              No  No internet service   \n5218                No              No  No internet service   \n6670               Yes             DSL                   No   \n6754               Yes             DSL                  Yes   \n\n             OnlineBackup     DeviceProtection          TechSupport  \\\n488                    No                  Yes                  Yes   \n753   No internet service  No internet service  No internet service   \n936                   Yes                  Yes                   No   \n1082  No internet service  No internet service  No internet service   \n1340                  Yes                  Yes                  Yes   \n3331  No internet service                  NaN  No internet service   \n3826  No internet service                  NaN  No internet service   \n4380  No internet service                  NaN  No internet service   \n5218  No internet service                  NaN  No internet service   \n6670                  Yes                  Yes                  Yes   \n6754                  Yes                   No                  Yes   \n\n              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n488                   Yes                   No  Two year              Yes   \n753   No internet service  No internet service  Two year               No   \n936                   Yes                  Yes  Two year               No   \n1082  No internet service  No internet service  Two year               No   \n1340                  Yes                   No  Two year               No   \n3331  No internet service  No internet service  Two year               No   \n3826  No internet service  No internet service  Two year               No   \n4380  No internet service  No internet service  Two year               No   \n5218  No internet service  No internet service  One year              Yes   \n6670                  Yes                   No  Two year               No   \n6754                   No                   No  Two year              Yes   \n\n                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n488   Bank transfer (automatic)           52.55                 No  \n753                Mailed check           20.25                 No  \n936                Mailed check           80.85                 No  \n1082               Mailed check           25.75                 No  \n1340    Credit card (automatic)           56.05                 No  \n3331               Mailed check           19.85                 No  \n3826               Mailed check           25.35                 No  \n4380               Mailed check           20.00                 No  \n5218               Mailed check           19.70                 No  \n6670               Mailed check           73.35                 No  \n6754  Bank transfer (automatic)           61.90                 No  ",
      "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>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>DeviceProtection</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>488</th>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</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>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>52.55</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>753</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</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>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>20.25</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>80.85</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</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>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>25.75</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1340</th>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Credit card (automatic)</td>\n      <td>56.05</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3331</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>NaN</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>No</td>\n      <td>Mailed check</td>\n      <td>19.85</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3826</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>NaN</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>No</td>\n      <td>Mailed check</td>\n      <td>25.35</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4380</th>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>NaN</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>No</td>\n      <td>Mailed check</td>\n      <td>20.00</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>5218</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>NaN</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>19.70</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6670</th>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>73.35</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6754</th>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>61.90</td>\n      <td></td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean indexing으로 검색\n",
    "\n",
    "cond = (df['TotalCharges'] == '') | (df['TotalCharges'] == ' ')\n",
    "df[cond] # True 인 것만 출력이 된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JKPmMVFlzt6G"
   },
   "source": [
    "####  \n",
    "##### <font color=blue> **[문제] df 데이터프레임의 'TotalCharges' 컬럼의 값 ' ' --> '0' 변경하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "-acD_1OKzt6G"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7034 non-null   object \n",
      " 1   SeniorCitizen     7042 non-null   float64\n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7041 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7040 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  3580 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7042 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7042 non-null   object \n",
      " 17  MonthlyCharges    7042 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   object \n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(1), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# DataFrame replace 함수 \n",
    "# 대상 컬럼 : 'TotalCharges'  \n",
    "\n",
    "df['TotalCharges'].replace(' ', '0', inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UMSbb4T8zt6H"
   },
   "source": [
    "####  \n",
    "##### <font color=blue> **[문제] df 데이터프레임의 'TotalCharges' 컬럼 타입을 object에서 float 변경하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "NhKAXpuTzt6H"
   },
   "outputs": [],
   "source": [
    "# 'TotalCharges' 컬럼 type을 float로 변경\n",
    "# 결과를 TotalCharges 컬럼에 다시 넣어야 합니다.\n",
    "\n",
    "df['TotalCharges'] = df['TotalCharges'].astype(float)\n",
    "# 아까는 공백이 있었지만 이제 0으로 바꿔서 값이 나온다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113
    },
    "id": "mv0Vpc0uzt6H",
    "outputId": "3afe9104-f4a3-4ba6-bf48-4d6ca90c7601"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\nIndex: []",
      "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>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</th>\n      <th>DeviceProtection</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  </tbody>\n</table>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다시 Boolean indexing으로 검색 : '' 값을 가진 Row 없음\n",
    "\n",
    "cond = (df['TotalCharges'] == '') | (df['TotalCharges'] == ' ')\n",
    "df[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I2ozL1ydzt6H",
    "outputId": "7bc539c8-3686-4457-8e67-06bbac82de40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7034 non-null   object \n",
      " 1   SeniorCitizen     7042 non-null   float64\n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7041 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7040 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  3580 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7042 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7042 non-null   object \n",
      " 17  MonthlyCharges    7042 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   float64\n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(3), int64(1), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# TotalCharges 컬럼 float 변경 확인 \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jP3W6JR7zt6H"
   },
   "source": [
    "#### Churn 컬럼의 문자열값을 숫자로 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9zrpsGwMzt6H",
    "outputId": "5ed83e99-7956-4232-a965-c96dd4addd3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "No     5174\nYes    1869\nName: Churn, dtype: int64"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Churn 컬럼의 분포 확인 \n",
    "# Churn Yes : 이탈 , No : 이탈안함. 언밸런스하게 No쪽이 많은 차지하고 있음\n",
    "\n",
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "AtOxyzm9zt6I"
   },
   "outputs": [],
   "source": [
    "# 'Churn' 컬럼의 ['Yes', 'No'] --> [1, 0] 변경하기\n",
    "# 컴퓨터는 문자열 이해 잘 하지 못해 숫자로 변경해야 함.\n",
    "\n",
    "df['Churn'].replace(['Yes', 'No'], [1, 0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ws8TKo9bzt6I",
    "outputId": "5ed25cfc-90fd-4808-e8ae-aa5f3e1720ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "No     5174\nYes    1869\nName: Churn, dtype: int64"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Churn 컬럼의 분포 확인 \n",
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ozIRjhjJzt6I"
   },
   "source": [
    "####  \n",
    "### Null 데이터 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vBJW3_W7zt6I"
   },
   "source": [
    "##### <font color=blue> **[문제] df 데이터프레임에 대해 컬럼별로 null 얼마나 있는지 null 갯수를 나열 하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MKO2LGSyzt6J",
    "outputId": "e5d35700-e9d5-4f87-ab84-b6b845e54991"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "gender                 9\nSeniorCitizen          1\nPartner                0\nDependents             2\ntenure                 0\nPhoneService           3\nMultipleLines          0\nInternetService        0\nOnlineSecurity         0\nOnlineBackup           0\nDeviceProtection    3463\nTechSupport            0\nStreamingTV            0\nStreamingMovies        0\nContract               1\nPaperlessBilling       0\nPaymentMethod          1\nMonthlyCharges         1\nTotalCharges           0\nChurn                  0\ndtype: int64"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame isnull(), sum() 함수 활용\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Hi-jVgMzt6J"
   },
   "source": [
    "####  \n",
    "### 결측치 처리\n",
    "\n",
    "데이터에 결측치 있으면 모델링시 알지 못하는 에러 발생할수 있으므로 반드시 결측치를 제거나 변경해야 한다.<br>\n",
    "결측치 제거시 dropna() 함수 활용하면 된다.<br>\n",
    "결측치를 변경시 변경하는 방법이 꼭 정답이 아니며, 여러가지 판단하고 고민이 필요하다.<br>\n",
    "주로, 문자형 컬럼에 대해 최빈값으로 , 숫자형 컬럼에 대해 중간값으로 결측치 대신해서 채울수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tKv3QNq4zt6J"
   },
   "source": [
    "####  \n",
    "##### <font color=blue> **[문제] df 데이터프레임의 결측치 많은 컬럼은 컬럼 제거하고 나머지 결측치는 Row 제거 하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "83xoHjqlzt6J"
   },
   "outputs": [],
   "source": [
    "# 1. 결측치 많은 컬럼 :  DeviceProtection   --> drop 함수 이용하여 해당 컬럼 제거\n",
    "# 2. 결측치 작은 Row에 대해서 dropna로 제거 \n",
    "# inplace=True 옵션으로 자체 저장\n",
    "\n",
    "# df.drop('DeviceProtection',axis=1,inplace=True)\n",
    "df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K3vpuuBwzt6J",
    "outputId": "c7d81ace-433f-4e0a-a498-ec273af14d0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "gender              0\nSeniorCitizen       0\nPartner             0\nDependents          0\ntenure              0\nPhoneService        0\nMultipleLines       0\nInternetService     0\nOnlineSecurity      0\nOnlineBackup        0\nTechSupport         0\nStreamingTV         0\nStreamingMovies     0\nContract            0\nPaperlessBilling    0\nPaymentMethod       0\nMonthlyCharges      0\nTotalCharges        0\nChurn               0\ndtype: int64"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null 여부 다시 확인\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UXdXv6XLzt6K",
    "outputId": "9503eab1-1c5c-4318-ec43-787d5908d24d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7027 entries, 1 to 7041\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7027 non-null   object \n",
      " 1   SeniorCitizen     7027 non-null   float64\n",
      " 2   Partner           7027 non-null   object \n",
      " 3   Dependents        7027 non-null   object \n",
      " 4   tenure            7027 non-null   int64  \n",
      " 5   PhoneService      7027 non-null   object \n",
      " 6   MultipleLines     7027 non-null   object \n",
      " 7   InternetService   7027 non-null   object \n",
      " 8   OnlineSecurity    7027 non-null   object \n",
      " 9   OnlineBackup      7027 non-null   object \n",
      " 10  TechSupport       7027 non-null   object \n",
      " 11  StreamingTV       7027 non-null   object \n",
      " 12  StreamingMovies   7027 non-null   object \n",
      " 13  Contract          7027 non-null   object \n",
      " 14  PaperlessBilling  7027 non-null   object \n",
      " 15  PaymentMethod     7027 non-null   object \n",
      " 16  MonthlyCharges    7027 non-null   float64\n",
      " 17  TotalCharges      7027 non-null   float64\n",
      " 18  Churn             7027 non-null   int64  \n",
      "dtypes: float64(3), int64(2), object(14)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# DeviceProtection 컬럼 삭제 확인\n",
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "45qw-GtGzt6K"
   },
   "source": [
    "#  \n",
    "# 4. 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "192lDHykzt6K"
   },
   "source": [
    "### 라이브러리 임포트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q0V7_mlvzt6K"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fYXooyxzt6K"
   },
   "source": [
    "####  \n",
    "### Bar 차트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JHwK2RIFzt6K",
    "outputId": "11b66038-4301-48cc-cec8-fb18f4137ae5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Male      3550\nFemale    3477\nName: gender, dtype: int64"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "pandas.core.series.Series"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['gender'].value_counts())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "id": "8iSUAfQ6zt6L",
    "outputId": "2fc723d5-38dc-4602-e94f-d8219da650ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 68,
     "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": [
    "df['gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "syDAao21zt6L"
   },
   "source": [
    "##### <font color=blue> **[문제] df 데이터프레임의 'Partner' 컬럼의 값 분포를 구하고 Bar 차트를 그리세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "gYRqqgEEzt6L",
    "outputId": "0e0cce32-0681-4970-9d1e-0fc56edfe660"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 72,
     "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": [
    "# DataFrame value_counts()와 plot() 함수 활용\n",
    "# 대상 컬럼 : 'Partner'  \n",
    "# plot 함수의 인자 : kind='bar'\n",
    "\n",
    "df['Partner'].value_counts().plot(kind='bar')\n",
    "# type(df['Partner'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k0WhB7Qfzt6L"
   },
   "source": [
    "####  \n",
    "### 한꺼번에 Object 컬럼에 대해서 분포 Bar 차트 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 259
    },
    "id": "0E6sIM9Ozt6L",
    "outputId": "e237e8bf-197a-43ac-8053-b81184c400b4"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "data type '' not understood",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[75], line 5\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# Object 컬럼만 뽑으려면 \u001B[39;00m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;66;03m# 1. 일일히 눈으로 보고 Object 컬럼 고른다\u001B[39;00m\n\u001B[0;32m      3\u001B[0m \u001B[38;5;66;03m# 2. select_dtypes() 함수 활용한다.\u001B[39;00m\n\u001B[1;32m----> 5\u001B[0m \u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mselect_dtypes\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m0\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\frame.py:4723\u001B[0m, in \u001B[0;36mDataFrame.select_dtypes\u001B[1;34m(self, include, exclude)\u001B[0m\n\u001B[0;32m   4720\u001B[0m             converted_dtypes\u001B[38;5;241m.\u001B[39mappend(infer_dtype_from_object(dtype))\n\u001B[0;32m   4721\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mfrozenset\u001B[39m(converted_dtypes)\n\u001B[1;32m-> 4723\u001B[0m include \u001B[38;5;241m=\u001B[39m \u001B[43mcheck_int_infer_dtype\u001B[49m\u001B[43m(\u001B[49m\u001B[43minclude\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   4724\u001B[0m exclude \u001B[38;5;241m=\u001B[39m check_int_infer_dtype(exclude)\n\u001B[0;32m   4726\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m dtypes \u001B[38;5;129;01min\u001B[39;00m (include, exclude):\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\frame.py:4720\u001B[0m, in \u001B[0;36mDataFrame.select_dtypes.<locals>.check_int_infer_dtype\u001B[1;34m(dtypes)\u001B[0m\n\u001B[0;32m   4718\u001B[0m         converted_dtypes\u001B[38;5;241m.\u001B[39mextend([np\u001B[38;5;241m.\u001B[39mfloat64, np\u001B[38;5;241m.\u001B[39mfloat32])\n\u001B[0;32m   4719\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 4720\u001B[0m         converted_dtypes\u001B[38;5;241m.\u001B[39mappend(\u001B[43minfer_dtype_from_object\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[0;32m   4721\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mfrozenset\u001B[39m(converted_dtypes)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pyAI_study\\lib\\site-packages\\pandas\\core\\dtypes\\common.py:1696\u001B[0m, in \u001B[0;36minfer_dtype_from_object\u001B[1;34m(dtype)\u001B[0m\n\u001B[0;32m   1687\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mAttributeError\u001B[39;00m, \u001B[38;5;167;01mTypeError\u001B[39;00m):\n\u001B[0;32m   1688\u001B[0m         \u001B[38;5;66;03m# Handles cases like get_dtype(int) i.e.,\u001B[39;00m\n\u001B[0;32m   1689\u001B[0m         \u001B[38;5;66;03m# Python objects that are valid dtypes\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1692\u001B[0m         \u001B[38;5;66;03m# TypeError handles the float16 type code of 'e'\u001B[39;00m\n\u001B[0;32m   1693\u001B[0m         \u001B[38;5;66;03m# further handle internal types\u001B[39;00m\n\u001B[0;32m   1694\u001B[0m         \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[1;32m-> 1696\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m infer_dtype_from_object(\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdtype\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m)\u001B[49m)\n",
      "\u001B[1;31mTypeError\u001B[0m: data type '' not understood"
     ]
    }
   ],
   "source": [
    "# Object 컬럼만 뽑으려면 \n",
    "# 1. 일일히 눈으로 보고 Object 컬럼 고른다\n",
    "# 2. select_dtypes() 함수 활용한다.\n",
    "\n",
    "df.select_dtypes('0').head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "loHWSdxKzt6M",
    "outputId": "4987b572-6791-4307-a8c9-eb47c16a089a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array(['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'TechSupport',\n       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n       'PaymentMethod'], dtype=object)"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Object 컬럼명만 뽑아보자\n",
    "df.select_dtypes('O').columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9G2QSJcKzt6M",
    "outputId": "3a050a54-0203-4f1c-c037-b9dcc2ee9b7d"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' 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[81], line 8\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m object_list:\n\u001B[0;32m      7\u001B[0m     df[col]\u001B[38;5;241m.\u001B[39mvalue_counts()\u001B[38;5;241m.\u001B[39mplot(kind\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbar\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m----> 8\u001B[0m     \u001B[43mplt\u001B[49m\u001B[38;5;241m.\u001B[39mtitle(col)\n\u001B[0;32m      9\u001B[0m     plt\u001B[38;5;241m.\u001B[39mshow()\n",
      "\u001B[1;31mNameError\u001B[0m: name 'plt' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Object 컬럼 하나씩 가져와서 Bar 차트 그려보기\n",
    "# 불균형 컬럼 : Dependents, PhoneService. 심한 불균형 가진 PhoneService 컬럼 삭제 필요  \n",
    "\n",
    "object_list = df.select_dtypes('object').columns.values\n",
    "\n",
    "for col in object_list:\n",
    "    df[col].value_counts().plot(kind='bar')\n",
    "    plt.title(col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UcI1GbIPzt6M"
   },
   "source": [
    "#### 불균형 심한 PhoneService 컬럼 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "7vOTa2mFzt6M"
   },
   "outputs": [],
   "source": [
    "df.drop('PhoneService', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rn0wGwNGzt6M"
   },
   "source": [
    "####  \n",
    "### 숫자형 컬럼에 대한 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "0iCdMZx_zt6N",
    "outputId": "a9e8cb47-0fcb-4446-d27e-6b3971d1a7ba"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-a4062a60-8e89-42c0-b75f-fd4eb75e6a90\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4062a60-8e89-42c0-b75f-fd4eb75e6a90')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-a4062a60-8e89-42c0-b75f-fd4eb75e6a90 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-a4062a60-8e89-42c0-b75f-fd4eb75e6a90');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  Churn\n",
       "1            0.0      34           56.95       1889.50      0\n",
       "2            0.0       2           53.85        108.15      1\n",
       "3            0.0      45           42.30       1840.75      0"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "# number(int, float) 컬럼에 대해 검색\n",
    "df.select_dtypes( 'number').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69xzs51zzt6N"
   },
   "source": [
    "#### Churn 컬럼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xW6s6sHGzt6N",
    "outputId": "5a917ea4-4200-4900-a57a-6e761c9e2993"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    5161\n",
       "1    1866\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "# Churn 컬럼은 0, 1 되어 있으므로 분포 확인\n",
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "BGgPFvYizt6N",
    "outputId": "cdaf4273-bb67-4c7c-e494-fa62d75dc40e"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30b302690>"
      ]
     },
     "metadata": {},
     "execution_count": 41
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Churn 컬럼에 대한 Bar 차트 확인\n",
    "# 이탈(1)가 이탈않음(0)에 비해 1/3 수준임 : 불균형\n",
    "\n",
    "df['Churn'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NcTCAI3uzt6N"
   },
   "source": [
    "#### SeniorCitizen 컬럼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qG3lRh-4zt6N",
    "outputId": "a7ea6831-26e3-4a6f-c280-e13fb0b9de79"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0    5885\n",
       "1.0    1142\n",
       "Name: SeniorCitizen, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "# SeniorCitizen 컬럼은 0, 1 되어 있으므로 분포 확인\n",
    "df['SeniorCitizen'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "6Lerw418zt6O",
    "outputId": "c8105a65-49b5-41b6-d9d3-9b4314ad754a"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc312444fd0>"
      ]
     },
     "metadata": {},
     "execution_count": 43
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# SeniorCitizen 컬럼에 대한 Bar 차트 확인\n",
    "# 이탈(1)가 이탈않음(0)에 비해 1/5 수준임 : 불균형\n",
    "\n",
    "df['SeniorCitizen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YpEyB2Lyzt6O"
   },
   "source": [
    "##### <font color=blue> **[문제] 불균형 심한 'SeniorCitizen' 컬럼을 삭제하세요.** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true,
    "id": "vDYDOLtyzt6O"
   },
   "outputs": [],
   "source": [
    "# DataFrame drop() 함수 활용\n",
    "# 대상 컬럼 : 'SeniorCitizen'  \n",
    "# axis 와 inplace 옵션 사용\n",
    "\n",
    "df.drop('SeniorCitizen', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JttbK72gzt6O",
    "outputId": "c9424279-5162-407a-d9a0-73a956ef7146"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7027 entries, 1 to 7041\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7027 non-null   object \n",
      " 1   Partner           7027 non-null   object \n",
      " 2   Dependents        7027 non-null   object \n",
      " 3   tenure            7027 non-null   int64  \n",
      " 4   MultipleLines     7027 non-null   object \n",
      " 5   InternetService   7027 non-null   object \n",
      " 6   OnlineSecurity    7027 non-null   object \n",
      " 7   OnlineBackup      7027 non-null   object \n",
      " 8   TechSupport       7027 non-null   object \n",
      " 9   StreamingTV       7027 non-null   object \n",
      " 10  StreamingMovies   7027 non-null   object \n",
      " 11  Contract          7027 non-null   object \n",
      " 12  PaperlessBilling  7027 non-null   object \n",
      " 13  PaymentMethod     7027 non-null   object \n",
      " 14  MonthlyCharges    7027 non-null   float64\n",
      " 15  TotalCharges      7027 non-null   float64\n",
      " 16  Churn             7027 non-null   int64  \n",
      "dtypes: float64(2), int64(2), object(13)\n",
      "memory usage: 988.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# SeniorCitizen 삭제 확인\n",
    "df.info() # columns의 개수가 17개로 줄어들었다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KFZu8kIQzt6O"
   },
   "source": [
    "####  \n",
    "### Histgram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "8w8QE1dzzt6P"
   },
   "outputs": [],
   "source": [
    "# seaborn 라이브러리 임포트\n",
    "# 앞에 !가 있으면 리눅스 명령어\n",
    "#!pip install seaborn\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1qgjcXFOzt6P"
   },
   "source": [
    "#### tenure 컬럼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "m5tzyEMkzt6P",
    "outputId": "70f21ec2-0fad-467d-d5ea-424e229e1656"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='tenure', ylabel='Count'>"
     },
     "execution_count": 86,
     "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": [
    "# tenure (서비스 사용기간)에 대한 히스토그램\n",
    "# 처음에 많이 사용하고 , 70개월 사용하는 충성고객도 있다.\n",
    "# histlot => Y 값은 있어도 되지만 여기서는 필요 없음\n",
    "sns.histplot(data=df, x='tenure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "NPWzP0hFzt6P",
    "outputId": "5ca5dff8-d3f2-4957-85c3-a91b62e21033"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='tenure', ylabel='Count'>"
     },
     "execution_count": 89,
     "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": [
    "# tenure (서비스 사용기간) 대한 히스토그램을 Churn 으로 구분\n",
    "# 히스토그램으로 Churn 구분하니 겹쳐서 보기 어렵다.\n",
    "\n",
    "sns.histplot(data=df, x='tenure', hue='Churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "VvFSzHkdzt6P",
    "outputId": "2133cb3a-2fb2-414a-e439-89f170a4e614"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='tenure', ylabel='Density'>"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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RJj2Th8aF5DmzkyQR0lLfKwEXQgghNOJfFpuZGY8pRLukctoToUJZGuugvr6e73zncjZv3hjU55GzxoQQQoh2a4p8iVCwmigeT1b70tjBhjbsLg8Woz44T6Sq4G479e2Oe18ddGfXmMHaraZGX3+9lUcffYDy8oNdf84ukkRICCGEAMob2yiq8R2yesbw0NQHASRGGIm3Gqlvc7Gv1hboNt2rVJW4t7+F8VBwZ1e+yTV4Kg3fertLydCHH77P88//nYUL7+D++38VxOh8ZGlMCCGEgECR9MShscQG4ZDVE1EUJTArFNQ6oX7SbnratBm88cY7zJlzQUieT2aEhBBCCDpumw+1nKRINh5oCN4WekXxzcx0c2ms2w0Vu7E0lpgYnENuT0QSISGEEGGv2e5m80HfYdxaJEIh2UKvKGCM6N59DTpQ+l5n6d4gS2NCCCHC3pf76/B4VYYnRvRad+eu8G+hL5Yt9CEniZAQQoiwp+WyGEBGvC/5qrO5aLa7NYkhXEkiJIQQIqy5PF6+LPF3k9YmEYoyG0iKNAFQWm/TJIZwJTVCQgghwtqWg420ODwkRBgZNzgIW9c7aViClZpWJ6V1bYwbHKNZHH3J558Hf7u/zAiJAcXm9FDT4tA6DCFEP+JfFps1IhGdhlvMMxN8hcwyIxRaMiMk+j2H28uL6w+wtriW4ppWvCoMibUwNSOOG6emB9behRDim1RVZW1x6LtJH4//s6q0rpvdn0W3SCIk+rWKRjv3vreL3VUtgcuU9svf3X6I/+4+zI/PyOTqyWnodf2jmZgQInSKalqpaHJgNuiYPixO01iGyYyQJiQREv1WYXULP1r6NU12N7EWA4vOGcG0jHgizXq2HmzitU0HyT/QwJ9X72N9aT2/nz8meGf4CCH6pc8KfbNB04fFa/75MKx9Rqisvg2PV5U/3kJEaoREv9Tm8vCr93fTZHczJjWaV26YzCVjU0mONhNpMnDGiAT+uuA07js/B4tBx7qSeu5+dyd2l0fr0IUQfchnRTUAnJOt7bIYwOAYCya9gtOjcqjZ3iuPqapqrzxOX9Ubr08SIdEvPbmqmJK6NgZFmfjLt8aRGmM55jaKonD5+MH85YpxWI061pc2cPe7O3F7BmZ3VCFE15Q3trG3uhWd4iuU1ppepwSaOfa0Tkiv981uOZ0De/OI//Xp9d1f4JKlMdHvrCys4d3th1CAh+aOIi7i5IcjTh4ax1PfPo07397B+lLfUtnd52aHJlghRJ+1usi3LDZpaOwpP0dCZVhCBPtqbZTU2Th9eEK3H0en02O1RtHSUg+AyWRG6cGOOK9XwePpO7NLqqridDpoaanHao1Cp+v+vI4kQqJfcXtVnl6zD4AbpqaTlxHXqftNHBrLQ/NGcfe7O3ljSwWjU6K5eGxKECMVQvR1n7UnQudkh/aQz5Px1wkdqO/5zrGYGF8i5U+GekKn0+H19r3ZdKs1KvA6u0sSIdGvfFxwmIMNdmItBr4/I6NL9z07O5FbZmTw/FcH+O3/CslNjiRnUFSQIhVC9GX1Nifbyn2HrJ7dB+qD/IYl+JfGer5zTFEUYmMTiY6Ox+Pp/rEdigLx8ZHU17fSl0qO9HpDj2aC/CQREv2Gx6vyz/UHALh2ylAiTF3f4XHr6cPYXdXCF/vruP/DPbx07SRMBimVEyLcrC2uw6vCqOQoBh+nxlArw+L9W+h7r5eQTqdDpzN1+/6KAhaLBaPR1acSod4ivwFEv7GqsIaSujaizQaumjSkW4+hUxR+c1Eu8VYjhdWt/GNdaS9HKYToD1a17xbrS7NBcGRGqLrFSatTDl8NBUmERL/xUn4ZAN+ZNIQoc/cnMxMiTNx3QQ4ASzaUBabHhRDhweb0kF/qq5s5J6fv1AcBxFiMxFt9hdsH63tnC704OUmERL+w93ALew63YNQrfGdyWo8f7+zsJC4Zm4JXhUc+3ovT3feKAIUQwbGupA6nRyU9zkJWYoTW4RxjaJxvqe5goxy1EQqSCIl+YcWuwwCcOSKROGvvbHP96TkjSIgwUlLXxssbynrlMYUQfd+qQv+yWFKPtpQHS1p7L6GDDTIjFAqSCIk+z+NV+ajAlwjNG53ca48bYzFy1+wsAP65/kCv7NIQQvRtLo+XL/bXAX2jm/TxpMX6ZoTKZUYoJCQREn2WrrmcqJV3s+fZK6lpdRJr1nHGiJ71i/im80cOYkZmPE6Pyu8+LRrw7eiFCHebyhpocXhIiDBy2pAYrcM5rsDSmMwIhYQkQqJPsmx/iYRXZmHd/W/esU8C4FL3x8R8/Y9efR5FUfjFnGxMeoUNBxpY1d5gTQgxMPmbKJ6dnYiuDy6LAQyN9S2NlTfIjFAoSCIk+hx97W6iPn8AxeukIfVMPlTOBOBb+rVEfvkohor8Xn2+oXFWrs8bCsBfPiuWg1mFGKA8XjVQH9SXukl/U1r7jNChZgcuORsx6CQREn2L6iV61S9QvG4cIy7if+P/it2jMCTWwpjRk1BQifnfnSjO5l592pumZ5AcZaKiycFrm8p79bGFEH3D5oMN1NlcxFoMTOvk8TxaSIo0YTbo8KpwqGlgH5raF0giJPoUy85XMVZtxmuMomXWw3xR4itqPHN4Aq1nPYgneij65jIiP3+gV5/XatRz+1kjAF/hdFWzfPgIMdB8sqca8PUOMuj77q8/RVECBdOyhT74+u47QYQfZyuRXz0OQOuMn+OJTOWLfb5E6PQRCaimaJrP+zMqCtbdb6CvL+rVp79w1CAmDInB7vYGDnYVQgwMbq/Kyr2+ZbHzRw7SOJpTGypb6ENGEiHRZ5j3fYjO0YgnZhj2cd+luNbG4RYnZoOOKUNjAXANmYEz83wArF+/2KvPrygKd5+bhQJ8VFAtHaeFGEA2Hqin0e4m3mpkSnqc1uGcUmBGSAqmg04SIdFnWAqWAmAffRXo9HzZPhs0JT0Wi/HIAattE29pv/2bKPb6Xo1hVEo0809LBeCJlcV4ZTu9EAOCf1ns3NwkDLq+uVvsaP4t9BWNMiMUbJIIiT5B13QQU/mXqCjYRy4A4Mv2+qAzhnfsHeQaMhN34hgUdxuWXa/3eiwLz8wkyqyn4HAL7+041OuPL4QILZfHG9g23x+WxUC6S4eSJEKiT7Ds/Q8ArrTT8Uan0eJws7W8CYDTv5EIoSjYJvhmhazb/wkeV6/GkhBh4pYZwwBY/HkJLQ45AVqI/uyLfXU02d0MijIxMS1W63A6ZehR3aWl0WtwSSIktKeqmAveBMA+6koA8g804PGqZMRbA0WDR3PkzMdrTULfUonpwGe9HtJVk4aQEW+lzuYKnHovhOifPtztO6LnwlHJ6PvBshjA4BgLCtDm8lJr690/9kRHkggJzRmqNmNoLMFrjMSRNQ+ATQcaAJgxLP4Ed7Jgz5kPgLn4/V6PyajXcefZvu30r206KAWLQvRTTXYXa/f5lsXm9uJZhcFmMuhIiTYD0mE62CQREprzz+g4h50LxggAtrTv2Jo09MTT2I6sS3z33/8xeHq/78+sEQlMHxaHy6Py9Jr9vf74Qojg+3RvDS6PSnZSJLnJUVqH0yX+gulyKZgOKkmEhOZMBz8HwJU+C4Bmu5ui6lYAJp4kEXIPzsMTmYLO2YypbG2vx6UoCovOyUKnwMrCGjaVNfT6cwghguvDXVUAzBvTf2aD/NLazxyTnWPBJYmQ0JTibMZwaDMAzqG+ROjriiZUID3OQlKk6SR31uHIuhgAc1HvL48BZCdF8u3xgwH446piPF4pWhSiv6hotLOlvAkFuGBU/0uEUmN8S2OVTZIIBZMkQkJTxvKvUFQPnphheGPSgSPLYp3Z3XFkeeyjoCyPAfzwdN92+r3Vrby/U7bTC9Ff+H9e8zLiAvU2/cmQ9p1jFXLeWFBJIiQ0ZTzoW9Jypp8VuGzrwfZE6CTLYn7BXh4DiIswcutM2U4vRH/i8aos3+FbFru8vUlqfzM4xpcIVcrSWFBJIiQ05U9enOlnAuBwe9lV5TtZflJn+n0oOpwj5voea/8nwQkSuHLike30/1wv2+mF6OvWl9ZT1ewg1mLg7OwkrcPplsHtS2OHmh2yLB9EkggJzehaKjHUF6Ki4Eo7A4Cdh5pweVQSI02BHROn4syYDYCpbDUEqfGYUa9jUft2+tc3y3Z6Ifq6d7f7lsXmjknBbOifv+oGRZnR6xQ8XpXqFlkeCxbN3h21tbUsXLiQvLw8pk+fzqOPPorbffwlh9WrV3PppZcyceJE5s6dy6pVq457u0ceeYR77723w2U2m41f/vKXTJ8+nSlTpvDzn/+c1tbWXn89ouuMB78AwJ08HtUSB8DWg75u0pPSYlCUzjU+c6bNRNWZ0DcfRN9QHJRYAc4ckcCMYfG4PCpPyXZ6IfqsOpuT1cW+3kGXjeufy2IAep1CarS/YFoSoWDRLBFatGgRERERrF27lrfeeot169bx0ksvHXO7kpISbr/9du688042btzI7bffzqJFi6iqqgrcpr6+nrvvvpslS5Ycc/+HH36YyspKPvroIz7++GMqKyt54okngvnSRCcZK9cDvmM1/LZV+OqDJnSlDb4xAteQ6QBB6TLt59tOPwK9AqtkO70Qfdb7O6rweFXGDY4me1Ck1uH0yOD2gmnZORY8miRCpaWl5Ofnc88992C1WklPT2fhwoW8+uqrx9x22bJl5OXlcd5552EwGJg3bx5Tp07ljTfeAKC1tZWLLrqImJgYLrzwwg73bWtr47333uOOO+4gLi6OxMRE7r77bt5++23a2rq2tKEo8tXTr2+Oo7FqKwDu1Mnt16vsPtQCwGlDorv02K6MswHf8lgwX0P2oEi+PWEI4NtO71VVzce1s+MtXzLeA+nrROPtVVX+s60CgMvHp2oeZ0+/hhy1hb4vjndf/+oMQ5eygV5SWFhIXFwcKSkpgcuysrKoqKigqamJmJiYwOVFRUXk5uZ2uH92djYFBQUAmM1mPvjgA5KSko5ZFistLcXlcnW4f1ZWFna7nZKSEkaPHt3pmBMTo7v0GsXxBcbR2Qp1ewCIGX0mxERzsN5GfZsLg05hxqhULEZ95x94wjz48hFM5V+RFGsEY+fqi7rjl5eO5aOCw+ytbmVVSQNXT8sI2nP1lLxvQ0vGO7SON96f7KqioslBXISR68/M6trnSB+UlRoLO6qod3hJStL2/TVQ39+aJEKtra1YrR0P0vR/b7PZOiRCx7utxWLBZrMBYDAYSEo6/o6Alhbf7EJERMQxz9PVOqHa2uZg1eGGBUXx/RD5x9FQsZ441YsnMoV6ZzTUNPPF3moAspIiaWm00dKVJ9ClEx+Zgr61isbtn+LKOOvU9+mBW2cO48lVxfz+vwXMSIsmyqzJj9IJfXO8RXDJeIfWycb7+dVFAMwfm9L1z5E+KNbom9bYf7iZmppmTWLor+9vf9ynosmnd0RExDFLU/7vIyM7rudarVbs9o5ro3a7/Zjbneh5/I/tv73/eaKiunbmjKoGbUNSWPGPo+HQVgDcyRMD47qrfVlsdEpUN8ZawZl+DtaCNzAeWN2hL1EwLJgwmLe2VlBa38Y/vizlp+dkBfX5ukvet6El4x1a3xzvklob60sb0ClwxYQhA+L/hX8LfUWTXfPXM1Df35rUCOXk5NDQ0EBNTU3gsuLiYlJTU4mO7pi95ebmUlhY2OGyoqIicnJyTvk8w4cPx2g0UlRU1OF5jEYjmZmZPXsRokcMh7cC4EqZFLhs9yHfXztjUrs3/eqfBQpWY8WjGfQ67j7Xl/y8sbmcPYf7+9+dQvR/S7f6aoNmjUgMdGXu74a0N1U81CS9hIJFk0QoMzOTKVOm8Nhjj9HS0kJZWRmLFy9mwYIFx9x2/vz55Ofns2LFCtxuNytWrCA/P5/LLrvslM9jtVqZO3cuTzzxBHV1ddTV1fHEE09wySWXYLEMjB+S/ipQKJ08EQBVVdld5UsmxqR0LxFyDpkJgL52N4q9oachntKMzATOyx2ER4XH/1eIdyD+qSREP9HQ5uK9Hb7eQVdNGqJxNL0nqb2XkNurUtPq1DqcAUmz7fNPPfUUbrebOXPmcNVVVzFr1iwWLlwIwKRJk1i+fDngK25+5plnePbZZ5k6dSqLFy/m6aefZvjw4Z16nvvvv5/MzEwuvfRSLrroIoYOHcpvfvOboL0ucWqKrQZ9s687szt5PAAHG+w0O9yY9AojkiJOdvcTUiOTccdloaBirMzvtXhP5mezRxBp0rOjspl3vq4MyXMKIY715pYK7G4vI5OjmJoRp3U4vcagUwLnpMlRG8GhWYVnUlISTz311HGv27JlS4fvZ82axaxZs075mI8//vgxl0VFRfHwww/z8MMPdy9Q0euMh7cB4I7PRjX7CuN3tx+rkTMoCqO++/m5a8gMDA3FGMvX4Rx+Qc+DPYVBUWZ+dEYmT64q5q9rSzgnJ4mECFPQn1cIcUSby8MbW8oB+O60dDrbjLW/GBJjpqLRTmWznYl0ocea6JT+2Xdc9GuGKl+i618Wg46F0j3hSvMtjxkrvurR43TFgolDGJUcRbPDzV9W7wvZ8wohfN7dfohGu5uhcRbOzemf54qdTGrg8FXpLh0MkgiJkDO0zwi5UiYGLvPPCHW3UNrPlTbD9xw1O1EcjT16rM4y6BTuPT8HBVix6zAbDzSE5HmFEODyeHl140EAbsgbil43sGaD4EjBdIUsjQWFJEIi5Ay1uwBwJ40DfJ1g/buuRnezUNrPG5mKO3Y4iurFWLmhZ4F2wdjUaK6YMBjwFU7bXZ6QPbcQ4Wz5jkMcanaQGGni4rH991yxk0kNnEIviVAwSCIkQkppq0ff6jsnzpM4CvD9ldPq9GDUK2Qmdq9Q+mj+WSFj+boeP1ZXLDxzOImRJkrr23j2y9KQPrcQ4cju8vDCVwcAuHl6er89Zf5U/MXSVc2yNBYMA/NdI/osQ+1uADzR6agmXz1QYbWvy/eIxEgMvTCt7RrSngiFsE4IINpi4L7zff2tXt14kG3loVmaEyJcvf11JdUtTlKizVx+2mCtwwma1KN6CanSpqPXSSIkQkpf6zsjzt0+GwRQ1J4I5fTSKdGu9n5ChurtvjPNQmhWViKXjE1BBR787x5ZIhMiSFodbl5a72vDccuMDEwDdDYIIDnKtxPV7vbSZHdrHM3AM3DfOaJPMgQSoSMH3hbW9G4i5I0egidqiK9OqL2DdSj97JwskqNMlDXY+eva/SF/fiHCwT/W7KPO5mJonIVLxqac+g79mMWoJ85qBGR5LBgkERIh5Z8R8nSYEfIVSmcn9U4iBOBKnQKA8dDmXnvMzoq2GPi/C3MBeGNLBZvKGkIegxAD2aEmO8+uKQbgtjOHY+hB77H+IjXaXzAtiVBvG/jvHtF3eL0Y6joujdmcHg42+HZC9NaMEIC7PREyHNrYa4/ZFTMzE/jWeN8Oloc+2ovNKUtkQvSWp9fsx+7yMmloDHNyB17foOORgungkURIhE5DKYrLhqoz4YkbAcC+2lZUICnSRHwvdmQ+MiO0SbPjku88ewSD2zvCPrGy6NR3EEKc0rbyRj4qqEZR4K7Z2QOui/SJSCIUPJIIidA53N4/KCEHdL7TXfa2F0pn9+JsEIA7aSyq3ozO0YC+QZtuz5EmA/dfNBIFeG9nFf/dfViTOIQYKNweL4//z/dHxVVT0hnVw070/Ym/l5AkQr1PEiEROlW+RMhzvB1jvVgfBIDehDt5AgCGQ5t697G7YEp6HN+fkQH4Gi2W1bdpFosQ/d2SjQcpqmkl1mrg5xeN1DqckArMCDVJU8XeJomQCJ2qHQC4E45TKN3LM0IArtTJABg1qhPy+/7MYUwaGkur08O97+2SLfVCdENJnY3n1/kald41O4vEKLPGEYWWLI0FjyRCInT8S2NJvq3zqqr2+tb5o7lS84D2OiENGXQKD88bRbzVyN7qVh7/X6E0RROiCzxelUc/3ovTozIjM565o5O1Dink/InQ4RYnHq98fvQmSYREaHgcUOvb7upJ8E1pVzU7aHF4MOgUMhN6frTGN/kLpvV1e1EcTb3++F2REm3msUtGo1Pgg12HeXNrpabxCNGf/HP9AbaWNxFp0vPL83LCpkD6aElRZnQKuL0qdTan1uEMKJIIiZDQN5aC6sFrisYb6dtWXlxjA2BYghVjEPqAqBGD8MQMQ0HFULWl1x+/q/Iy4rj9LN9uuT9+Vkx+ab3GEQnR920rbwwsif18TjZDYi0aR6QNg04hKdK3s1aWx3qXJEIiJPT1vp0enrgR0P7X3L7aI2eMBUtfqRPyu25KGheOGoTHq/KL93axv9amdUhC9FmNbS5+s6IAjwoXjU5m3piB3UH6VPxnjkki1LskERIhoa9vXxaLzw5c5k8ChvfCifMncqROKPQdpo9HURR+feFIJgyJocXhYdGyHdS2yjS3EN/k9qr86v3dVDQ5SIu18Is52ae+0wDnrxM61CSJUG+SREiERGBG6OhEqM6XCI0IYiIU6DBdtRlUb9CepyvMBh1/uGwMabEWKhrt3P6f7TTLQYpCdPD0mn3kH2jA0v7zEmU2aB2S5mTnWHBIIiRC4kgilAX4doyFYkbInTgK1RCBztmMvm5v0J6nq+IjTDx1xWkkRBgprG5l0bIdtMm2eiEAePvrSl7bVA7Ag3NHkjMofBonnowkQsEhiZAIPlUNdHf2xPkSocMtTlqdHvQKpMdZg/fcOgOulIlA36kT8suIt/LXBacRbTbwdUUTi97eIWeSibC3cm81v/tfIQC3zMjg3NxBGkfUd8jBq8EhiZAIOp3tMDpnMyg6PHGZAJS0zwalxwdnx9jR+lqd0NFyBkXxl2+PI9KkZ/PBRm7/z3ZaHLJMJsLT+tJ6/m9FAV4VLj8tlR+cPkzrkPqUFDlmIygkERJB518WIz4T9L4f5H11/mWx4O0Y89P6JPpTOW1IDM8cNTP0o6VfU90iH3QivHy5v4673tmJy6MyOyeJe8O0X9DJ+JfG6lqduDx9o+ZxIJBESASdvsG3Y4zEnMBl+9u3zgezPsjPv4Xe0LAPpa0u6M/XHWMHx/C3q8aTEGFkz+EWbnp1C3sPt2gdlhAh8VlhDXe9sxOH28usEQk8Mm8Uep0kQd8UbzVi0iuowGH5Y6nXSCIkgs6/dZ6koxOh9hmhIHSU/ibVEo+7vTbJWNX3lsf8RiZH8eK1ExmeEMHhFie3/HsrH+6u0josIYLq35vL+cV7u3B7Vc7LTeJ388dgMsivpuNRFEW20AeBvNtE0Bka2pfGknIB346xfSHYMXa0I8tj2p47dippsVaev2YCUzPiaHN5+c2KPTz2yV45qFUMOG6Pl99/WsSTq4rxqnDZaak8fPHooNcM9neyc6z3yTtOBN2RGSFfIlRnc9Fkd6MAw+KDuGPsKIEO033gqI1TibEYefqK0/j+jAwUYNnXh7j6X5vYeKBB69CE6BWHmuz84I1tvLm1AgW446zh3Hd+DgZZDjulFOku3eskERLB5WpD33zQ9+/2pbGS9kLptDgLFqM+NGGktNcJVW0Fb9+fXdHrFH50RiZPX3EayVEmyhvt/PjNr/nNigIONdm1Dk+Ibvt0bzXXL9nM9spmosx6/nDZGG6Ymi6F0Z0kM0K9TxIhEVT6xv0AeM1xEJEIcGRZLAT1QX6ehJG+xoquFvT1hSF73p6anhnPGzflccWEwQB8uPswV7y4gT99ViwfhKJfqbc5+dX7u7n3vd002t2MSY3mlRsmc3Z2ktah9SuSCPU+6VkugupII8Xh6Nr/4vP3EMoMYSKETo8rZSKm8i8xVm3GkzgqdM/dQ1FmA/eel8P8can8ZfU+Nh9s5LVN5SzdUsEFowZx2WmpTEqLlb+oRZ/k8aq8u72SZz4vocnuRq/ATdMz+P6MDKkH6oZUSYR6nSRCIqj0jSWALxEytl92oL4NgGEJoakP8nOnTMZU/iWGQ5thzLUhfe7eMCY1mr9fNZ4vS+p5Ob+MzQcbWbHrMCt2HWZonIXZ2UmcnZ3I2MExGPWSFAltqarKV6X1PLO2hD3trSByBkXy6wtzGZ0SrXF0/ZfsGut9kgiJoPIvjXlihwcuK633zQgNiw/hjBD9q2D6RBRF4YzhCZwxPIGdlU0s+/oQn+yp5mCDnSUbD7Jk40GsRh3jBscwIzuJ7DgL4wZHE2MxnvrBhegl28obeebzErYcbAQg0qTnR2dksmDiECmI7iF/ItTscGNzeogwhabOciCTREgElX9GyBubCYDd5Qn8JRPqGSFXyiRfTHV7URxNqOaYkD5/bxs7OIaxg2O469ws1hbXsqa4lnUl9TTZ3Ww40MCGo3aZDY4xkzMoipxBkeQOiiQ3OYohsZbAcqUQvWFnZRPPf3WAz/f5Gpea9AoLJg7hpmnpxEeYNI5uYIgyG4gy62lxeKhqdoSsBclAJomQCKojS2OZABxssKMC0WYDcdbQzlKoEYPwxGSgbzqA4fA2XOmzQvr8wWI16rlgVDIXjErGq6rsr7WxvbKJgpo28vfVUNZgp7LJQWWTgzXFtYH7RRj1ZLcnRjnJUYwcFElWUmTIdvKJgUFVVb4sqWfJhjI2lflmgPQKXDIulVtmZJDavt1b9J6UaDMtDhtVzXZJhHqBJEIieFw29K2+zsie9hkh/9b5YQlWTYp7XSmT0DcdwFi1ecAkQkfTKQpZSZFkD4okKSmamppmmtrcFNa0sPdwK4XVLRRWt1Jc04rN5eHriia+rmg66v6+WqRpGXGcPjyB04bEyKyROC63x8vHe6pZsuEgRTW+I3P0OoULRw3ie9MzQrsZIsykRJsprrFJnVAvkURIBI2+qRQArzkW1RIPQKm/UDpEjRS/yZ0yGQrf9RVMh4loi4HJQ+OYPDQucJnbq1JaZ2NvdQuFh1sprG5lb3ULdTYXOyqb2VHZzIvry0iIMDI7J4lLx6YwJjVadqYJbE4P7+44xGsbD3KofedShFHP5eNTuWZymswAhYBsoe9dkgiJoAksix1VKH0gMCOkzV+LRwqmN4OqQpj+YjfofDNHWUmRzB195PJDTXY2HGjgq5J6vthfR53NxX+2VfKfbZUMT4zgyolDuHhMihRohqEGm4s3tpTz5tYKGu1uABIijFw9OY0rJgyWgvwQSo2W7tK9SRIhETT6hhLgyLIYHJkRytBqRihpLKrejM5ej66xBG/c8FPfKYykxli4dFwql45LxeXxkn+ggf/uPsyqwhr219r4/adFLP58P1dNHMI1U4aGvM5LhF5Fo51XNx7k3R2HcLi9AAyNs3BD3lAuHpuKWQ5IDbnAFnpJhHqFJEIiaI5snc8EfEWVpXX+pTGN6gf0JtyDxmE8tAlj1WYckgidkFGvC2zVb3G4WbGrin9vLqeswc6L68v49+YKrp6SxnenpssM0QBUWN3Cv/LL+N+eajyq77LRKVHcODWd2TlJ6GUbvGZkaax3SSIkguabO8ZqW500O3yHrQ6N066OwJUy+UgiNPIKzeLoT6LMBq6alMaCiUNYXVTL8+tK2VvdyotfHWD59kPcNiuTeWNSpLB6ACiuaeUfX5aysrAmcNm0jDi+Oy2dqRlxUifWB6TGHEmEVFWV/yc9JImQCJpv1gjtb99ZMjjGrOkWbbf/ANZD/bexolZ0isLsnCTOyU5kVVEtT63eR3mjnQf/u5elWyq4a3YWE9JitQ5TdENJnY3n15XycUE1KqAAc3KTuHFaunSC7mOSo3yJkMPtpbHNTVyELFH3hCRCIjjcbehbKoAjS2P7qn1t9jO0WhZr5y+YNtTuAlcbGLWpV+rPFEXh3JwkzhyewBtbynnhqwPsrmrhln9vY/64FBadnUW0RT5e+oPqFgfPflHKezsP4W1fAjs3J4lbTx9GdlKktsGJ4zIZdCREGKmzuahqdkgi1EPySSWCQt9UBoDXFINqSUAB9lX7ZoRC3VH6m7xRQ/BEpKC3VWGo3o57yDRN4+nPTAYdN0xNZ96YFP72RQnvbj/E8h1VfLm/nnvPy5aTxfuwNpeHJRvKWLLhIPb2IuizshL5wenDGJkcpXF04lRSos2+RKjFwcgU+f/VE1LuL4LiyLJYZmCL+r72pTGtZ4RQFNypvuM2jFXh008omBIjTfzfBbn84zsTyIi3UtPq5O53d/Gr93dTZ3NqHZ44ildVWb79EN9+YQPPrTuA3e3ltMExPH/1BJ68fKwkQf2EFEz3Hs0SodraWhYuXEheXh7Tp0/n0Ucfxe12H/e2q1ev5tJLL2XixInMnTuXVatWdbj+ueee46yzzmLixInccMMN7Nu3L3BdWVkZt956K9OmTWPmzJn8/Oc/p6mp6ZtPIXrZkURoWOCykkAipH3DNVfKUf2ERK+ZNDSWV2+YzHenpaNX4JM91XznpU18urda69AEsOtQMze/tpWHP95LTauTtFgLv71kNC9cM0Fqu/oZf53QYUmEekyzRGjRokVERESwdu1a3nrrLdatW8dLL710zO1KSkq4/fbbufPOO9m4cSO33347ixYtoqrKd3TDsmXLWLJkCS+88ALr169n7Nix3HHHHaiqb7H7Zz/7GdnZ2XzxxRd8+OGHVFRU8Pjjj4fypYYlf1dpf32QV1UDzRSHxmlfk+NuP4A1nDpMh4rFqOcns4bzz+smkTMokoY2F/e+t5v/+2A3jW0urcMLSw1tLn77SSE3vbqFnYeaiTTpufPsESy9KY/zRg6SXUf9UHL7jNDhFkmEekqTRKi0tJT8/HzuuecerFYr6enpLFy4kFdfffWY2y5btoy8vDzOO+88DAYD8+bNY+rUqbzxxhsALF26lGuvvZacnBzMZjN33XUXFRUVrF+/HoDi4mJUVQ18KYqC1dr1X8SKIl9d+dI3HQDAG5uBokBNqxOH24tepzA41qJ5fO6UCaiKDn3rIfStFZrHE4wvrd+3Y1Kjefn6Sdw8Ix2dAh8VVHP1vzbxxf46zcdmII738WNSWb7jEAte3MDbX1eiAnPHJPOfm/O4YepQzEad5jEOpPEO5dfRW+hlvE8e96loUixdWFhIXFwcKSkpgcuysrKoqKigqamJmJiYwOVFRUXk5uZ2uH92djYFBQWB62+99dbAdUajkczMTAoKCpgxYwa33347Tz75JC+//DIej4eJEydy9913dznmxETZPtolLQcBiE4fRXRSNEVNvjqR9HgrqckxJ7tniERDylg4tJ0EWwFkjtQ6oKDoC+/b31w+nksnp3PXm9vYV93Kord3cPXUdP7vkjFEmQfWfo2+MN5++2ta+dWy7azbVwvAqNRoHrpsHNOGJ2gcWe/pS+Mdajlpvs/UWpuLpKTQjMNAHW9NPoVaW1uPmZXxf2+z2TokQse7rcViwWazdep6RVH48Y9/zPe+9z3q6+v52c9+xm9+8xv+8Ic/dCnm2tpm2lfbxKmoXhLrS1GAOnUQ3ppmdh2oA2BwtJmammZt42sXmTQR66Ht2Aq/wJZ8rtbh9CpF8X1o9ZX3bXqEgZevncgzn5fw+qZy/r2hjDV7DvObi0aSlxGndXg91pfG2+3x8srGgzy37gAOtxezQcePz8jk6ilpGHRKn/n564m+NN5aMXs9AFQ22qmubiKYy5v9dbz9cZ+KJolQREQEbW1tHS7zfx8Z2bFvhdVqxW63d7jMbrcHbney63fs2MFf/vIXNmzYgMFgICIigp///Odcd9113H///URFdX53hKrSr94AWtK1HELxOlF1BjyRg0GFsvYzxobGWfrMOLpSJmPdsQTjoS19Jqbe1pfet2aDnp+dk8XZWYk89N89VDQ5+NHSr7l6chq3nZmpaZPN3qL1eO881MyjH++lsL1VxfRhcdx7Xk6gLq+vvBd6i9bjraVBkUeaKjbYQtNUcaCOtyY1Qjk5OTQ0NFBTc6SFe3FxMampqURHd8zecnNzKSws7HBZUVEROTk5gcc6+nqXy0VJSQm5ublUVlbi8Xjwer2B641GI4qioNf3/w/dvipQHxSVBjrfOB9s8CWrfaFQ2i/QYfrwNvBIEW+oTEmP47XvTuFb41MB+Pfmcq5bspntFbKbs7vaXB7+9FkxN7+2hcLqVmItBh64aCRPX3Fan/qZE73H31QRoEoKpntEk0QoMzOTKVOm8Nhjj9HS0kJZWRmLFy9mwYIFx9x2/vz55Ofns2LFCtxuNytWrCA/P5/LLrsMgCuuuIJXXnmFgoICHA4HTz75JElJSeTl5TFlyhSsViuPPfYYDoeD2tpannzySc4///xuFUyLztE1+hKho7fOH2zwzwj1nXH3xA3Ha45F8Tgw1O7WOpywEmky8Kvzc/nLt8cxKMrEgfo2bvn3Vp5Zuz9wwrnonHUldVz90kZe21SOV4ULRw1i6ffyuHhsSlCXS4T2pJdQ79Bs+/xTTz2F2+1mzpw5XHXVVcyaNYuFCxcCMGnSJJYvXw74iqifeeYZnn32WaZOncrixYt5+umnGT58OAALFizgpptu4rbbbmPGjBns2rWLZ599FqPRSEJCAi+88AIlJSXMmjWLyy+/nMzMTB577DGtXnZYCGydj8kAfKfOlzUcWRrrMxTdkW300k9IE6cPT+Df353CRaOT8arwUn4Z1/xrI+tL6rUOrc9rsLn4zYoC7vjPDiqaHKRGm/nzt8fxyMWjSYgwaR2eCAHpJdQ7NNuykZSUxFNPPXXc67Zs6XgY5qxZs5g1a9Zxb6soCjfffDM333zzca8fN27ccfsTieDxL435E6FGu5sWh6+wLy22DyVC+OqETAc+w3hoM/bTbtI6nLAUYzHy8LxRzM5J4omVRZQ12PnJf7Zz4ahBLDoni6RI+aV+NFVV+XD3Yf702T4a2lwowHcmp/HjMzKJMMmSfziRXkK9Y2DtXRV9wjcTofL22aDUGAsWo75PFdsFDmCVGSHNnZuTxLSMOP7+RQlvbq3go4Jqvthfxw9Pz2TBhMEY9HIiUGmdjd99WsSGAw0AZCVF8H8X5DJucF9oSSFCTZbGeockQqLX6Rv9zRR9NUL+QumMRI3PGDsOd/JEAAyNJShttajWRG0DCnNRZgN3n5vNxWNT+O0nheyuauHJVcW8ubWC22cN5+zsxLCse3G6vfxrQxkvrT+A06NiNui4eXoGN0wdilESxLCVHO2bLZWlsZ6RnyDRu1w2dG2+c6X8M0L++qBhCX0vEVItcbjjfQ07jZUbNY5G+I1Oieaf107il+dlkxBh5EB9G/cs38UPl37NzkP9vw9OV2w80MA1L2/iH1+W4vSozBgWz7+/O4WbZ2RIEhTmZEaod8iMkOhVga3z5lhUs+8Qx4ONvhmhzKTIE95PS67BUzHU78VYmY9zxIVahyPa6XUK354whAtGJbNkQxmvbipny8FGbnp1C2dnJXLLzAxGpQzMTrcAdTYnf1m9jxW7DgOQEGHkrtlZnC9ng4l2gWLpFmfgCCnRdd1KhMrKykhPT+/tWMQAcKQ+6MjWeX+NUEYfnBECcA2ZhnXXqxgr87UORRxHlNnAj88czrfGD+bvX5SwYtdhVhfXsrq4ljNHJHDLzGGMTR04CZHT7eWNLeW88NUBWp0eFOCKCYNZeOZwoi3yt6s4wp8IOdxeGu1u4qzBb6o4EHXrp2ru3LlMmjSJBQsWcOGFF2Kx9K2dQEI7gRmhmCOJcll7jdCwPlgjBOAaPA0AQ/V2cNnA2DfjDHepMRYemDuKm6Zl8ML6A3xccJjP99Xx+b46pg+L45opQ5mZGY+un/5VrKoqKwtreGrNfiraZ1FHJkdx73nZUgwtjsvfVLHO5qKq2SGJUDd1a4F59erVzJ49mxdeeIEzzzyTX//618dseRfhSfeNHWNtLg+1rb7DAYcl9M2lMW/0UDxRg1G8boxV8j7u6zITI3h43iiW3pTHxWOS0SmwvrSBRW/v4Kp/buStrRW0uTxah9klm8oa+MEb27j3vd1UNNpJijTxmwtzefn6SZIEiZOSXkI9161EKDExkZtvvpnly5fz8ssvExMTw7333svcuXN5/vnnqaur6+04RT9x7NZ531+2sRYDsSE4C6dbFCUwKyTLY/3HsIQIHpg7ire/P5Vrp6QRadJTWt/G7z4t4uJn1/P7T4vYU9WidZgnpKoq+aX1/OCNbfxo6ddsLW/CbNBxy4wM/nPzVC4dl9pvZ7dE6CRLwXSP9WjLgdvtpqKigoqKCmpra7FarWzbto0LLriAZcuW9VaMoh/RN5UBx+4YS+tDR2scz5FEaIPGkYiuSou18tNzsnj/B9O5a3YWQ+MsNDvcvLm1gutf2cx1L2/ijc3lNLT1jfPkPF6VNcW13PLvbdz21na2HGzEqFe4YsJg/nPzVH4ojRFFF6RIU8Ue61aN0NatW3n33Xf58MMPURSFSy+9lFdeeYVRo0YB8Mknn3DffffxrW99q1eDFX2cqgYSIX+N0MG+eLTGcbiGtNcJHdoEXjfopCi1v4kyG7h6chpXThzChgP1LN9RxWdFNeytbuWJVcX86bNi8jLiODd3EOdkJ4b8GIqqZgfLtx/ine2VHG7xLReb9ArfGj+YG6amB36hCdEVyVHSS6inuvVpf91113HmmWfy4IMPcu6552I0dlzyGD16NOeee26vBCj6D8Vej+K2AeCJTgOgvL3oM72Pzwh5EkbiNceiczRiqNmFO3m81iGJbtLrFGZkJjAjM4GGNhcf7T7Mezur2HO4hfWlDawvbeB3/ytk0tBYZgyLZ+qweEYlR6HX9f4yVLPdzef7a/m4oJov99fhbe+qHmsxcMnYVK7LS2NQlCRAovtSYmRprKe6lQgtWbKEyZMnH3P5mjVrOOussxg6dCiPP/54j4MT/UugPigyBfS+H86y+v4xI4Siw5Wah7n0U4yV+ZIIDRBxViPfmZzGdyancaC+jZV7q1lZWMPuqhY2lTWyqawRPi8h2mxgSnosE9JiyU6KIDspksRIU5f7srQ43Ow+1MKW8kY2Hmjg64om3N4jZ8pMHhrLt8cP5pycJMwGaYYoeu7oXkKie7qVCN1yyy1s3tzxbKaWlhbuvPNO2T0WxnTNBwHwRh/ZOu9vpji0j88Iga+xorn0U4wV62mbcIvW4YhelhFv5abpGdw0PYPyxjbWFtex8UADmw420Oxw81lRLZ8V1QZuH2c1kpUUQWq0mcRIMzEWA1ajHoNeweNVcXm8NLa5qG9zUd5op6LJwcH2xP9oIxIjOCcnibmjk8nso720RP91dHdpaarYPZ1OhEpLS7n44ovxeDyoqsro0aOPuc3xZolE+DhSKO1LhNweL1VN/qWxPj4jBLiGTAfaC6ZVFeQDZcBKi7Vy9eQ0rp6chtursqeqmfwDDew53EJhdSsHG9poaHP5Zoy6aEiMmTGpMUzNiGVqRjzp8X3/jwDRfw2Spoo91ulEaNiwYbz55ps0NTXxgx/8gOeee67D9Wazmdzc3F4PUPQf+ub2RKh9RqiyyYFHBYtBR2JkaAtTu8OdPB5Vb0bXVoO+cT+euBFahyRCwKBTGDs4hrFH9euxuzzsr7Oxv9ZGdYuT6hYHrU4PbS4PLo+KXqdg0ivEWIzEWQ0MibUwdlgiCQaItcgvIhE6ZoOOeKuR+jYXh6WpYrd0aWnMPwv0/vvvyxEb4hi6wI6xocDRW+ct/WO6Vm/GlTwRU+V6jBX5kgiFMYtRz+iUaEZ38iwzRYGkpGhqappR1VPfXojelBJtpr7N1106NzlK63D6nS4lQg888AAPPPAAixcvPuFtfvvb3/Y4KNE/6dtrhDzRvh5CBxv6x46xo7mGTPMlQpX52MdcrXU4QghxSsnRZgoOt0gvoW7q0rYFVf7UESeiqkeWxtpnhMob22eEYvtPIuQePBUAg3SYFkL0E9JLqGe6NCP04IMPAjLrI46ltNWguO2oKHijhgD9aOv8UVypeagoGBpLUFoPo0Ymax2SEEKcVIocs9Ej3WpkUVNTw2OPPQbAxo0bOf3007nkkksoLi7u1eBE/xHoKB01GPS+v06ObJ3vP4mQao7BnTQGkHPHhBD9Q+C8Mekl1C3dSoQefPBBiouLUVWVRx99lHnz5jF79mweeuih3o5P9BPf3DHmVVUq+lEPoaP5l8eMFes1jkQIIU4tcN6YzAh1S7caKm7fvp0VK1ZQXV1NQUEBL774ItHR0UyfPr234xP9xDd3jFW3OHG4veh1Cqkx/WdGCMA5ZAbW7S9hqlhHq9bBCCHEKUhTxZ7p1oxQW1sbFouFdevWkZubS3x8PHa7HYNBDqoMV0d2jHU8bHVwjBlDEM5wCiZX2ukAGGoLUGw1GkcjhBAnd3RTxSa7W+No+p9uJULjx4/ngQce4B//+Afnn38+NTU1/OpXv2LatGm9HZ/oJ47sGPMlQuUN/XNZDEC1JuBO9NUJmcrXaRyNEEKcnL+pIkjBdHd0KxF69NFHcTqd5OXl8cMf/pDy8nKcTif3339/b8cn+onA0lh0x2aKQ2P717KYn3PoGQAYy7/QOBIhhDg1f8G09BLqum6tZSUnJ3c4XX7ChAn8/e9/77WgRD+jetE3lwPgienYTLE/zghB+/LYtucwHpRESAjR9yVHmdhzWAqmu6NbiVBrayuvvfYaJSUleL3eDtdJj6Hwo7NVo3gcqIret32eI80U+20iNGQ6qqLD0LgfXXMF3ughWockhBAnJL2Euq9bS2O//OUvefnll3E4ZMDFUctiUYNBZ0BV1SNLY/2oh9DRVHMM7kHjATCWf6lxNEIIcXLSS6j7ujUjtH79et566y05eFUAR/cQ8tUHNdrdtDg8AKT10xohANfQMzAe3oqp/AscoxZoHY4QQpyQzAh1X7dmhMxmMykpKb0di+in9E2+rfPe9vqg8vbZoEFRJixGvWZx9VSgYPrg58iR4kKIvkyaKnZftxKha6+9lscff5y6urrejkf0Q7rmA8CRGaH+Xijt5xo8FVVvRt9Sib6+SOtwhBDihJKjjiRCckB613RraWzp0qVUVFTw+uuvH3Pd7t27exyU6F/8M0L+HkL9fet8gMGKa8h0TGVrMJWtpi0hR+uIhBDiuPw1Qvb2poqx7X2FxKl1KxE6euu8ELpm//Ea7V2l++kZY8fjTD8bU9kajAdW0zbhFq3DEUKI4zIbdMRZjTS0uTjc4pBEqAu6lQj5O0g3NjZSVlbGmDFjcLvdmEymXg1O9ANez5EeQtH+rtL9e8fY0ZwZZ8OXD2OqWAduOxj6/2sSQgxMyVEmGtpcVDU7yBkUpXU4/Ua3aoRaW1u56667mD59Otdffz0lJSWcf/757Nu3r7fjE32czlaF4nWh6gx4I1MBKBsgNUIAnoSReCJTUNx2jJUbtQ5HCCFOSAqmu6dbidDvf/97bDYbH374IUajkfT0dGbPns2jjz7a2/GJPk7n3zEWlQY6PW0uD7Wtvj4WA2FGCEXBlX42AKayzzQNRQghTkZ6CXVPtxKhVatW8fjjjzN8+HAURcFoNHLvvfeyffv23o5P9HHf7CHkP2w11mIgxjIw1qidGe2J0IE1GkcihBAnJr2EuqdbiZDX6w3UA/m36R19mQgf+ib/qfMdD1tNGwDLYn7OobNQUTDU7kLXWqV1OEIIcVyyNNY93UqEZsyYwUMPPURbWxuKogDw5z//OVBELcLHkR1j/sNWB8jW+aOo1gTcKRMBMJWu1DYYIYQ4gaN7CYnO6/ZZY/v27WPq1Kk0NzczadIkNmzYwC9+8Yvejk/0cYEeQv6lscDW+YGTCAE4h80BwFTyP40jEUKI4zt6aUyaKnZet7bPWywWFi5cyPbt28nKymLQoEFMmjQJvb7/HqcguudIjVB7D6GG/n3q/Ik4M88jMv8JTGVrZRu9EKJPGhTlK0+xu700O9wDpk4z2LqcCD3//PP89a9/xeE4knFGRkbys5/9jOuuu67XAxR9mNeNrqXC989AV+mBs3X+aO6ksXgiU9C3VmGs+ApXxjlahySEEB1YjPpAU8WqZockQp3UpaWxN998k7///e/cd999rFmzhh07drB69Wruvvtu/vKXv/DRRx8FK07RB+laq1C8blSdEW9kCm6Pl6qmgbk0hqLgHHYeAGZZHhNC9FHJ7bNCh5tlC31ndSkReu211/jtb3/LlVdeyaBBgzAYDKSkpHDNNdfwwAMPsGTJkmDFKfogfZP/sNU0UHRUNjnwqL5W70mRA28HoTPTXyf0qZxGL4Tok470EpKC6c7qUiJUUlLC7Nmzj3vdeeed16XO0rW1tSxcuJC8vDymT5/Oo48+itvtPu5tV69ezaWXXsrEiROZO3cuq1at6nD9c889x1lnncXEiRO54YYbOsThcDh45JFHOOOMM5gyZQrf/e53KS4u7nSc4sR0ze3NFNt3jJUddbSGfzfhQOIceqbvNPrmMvR1e7QORwghjiG9hLquS4mQoigYDMcvKzKZTNjt9k4/1qJFi4iIiGDt2rW89dZbrFu3jpdeeumY25WUlHD77bdz5513snHjRm6//XYWLVpEVZWvn8uyZctYsmQJL7zwAuvXr2fs2LHccccdgfqlBx54gJ07d7Js2TLWrVtHVlYWd955Z1detjiBQA+h9h1jB/31QbEDqz4owBiBM+10AMz7P9E4GCGEOJb0Euq6bu0a66nS0lLy8/NZs2YNVquV9PR0Fi5cyB/+8AduuaXjCd/Lli0jLy+P887z1WfMmzePt99+mzfeeIM77riDpUuXcu2115KTkwPAXXfdxdKlS1m/fj05OTm8++67rFixguTkZADuvvtu9u/fj6qqXZq1GIATHD2mP+rUeUWB8sajZ4Q63tb/fX8fR2fWRZgPrMK070Papt6udTgnNFDGu7+Q8Q4tGe8TSz4qEeqt8emv493ZeLuUCLndbt55550TXu/xeDr1OIWFhcTFxZGSkhK4LCsri4qKCpqamoiJiQlcXlRURG5ubof7Z2dnU1BQELj+1ltvDVxnNBrJzMykoKAAh8NBdHQ0W7du5bbbbqOuro4pU6bwq1/9qstLN4mJ0V26fVhoqwQgMi2XyKRoDttcAIxKjycp6fjj1e/HccoVsOpejNVfk2Soh7gMrSM6qX4/3v2MjHdoyXgfK3eobyaops11ws/h7hqo492lRCgpKYmnnnrqhNcnJiZ26nFaW1uxWjsun/i/t9lsHRKh493WYrFgs9lOeX1jYyPNzc18/PHHLFmyBKPRyEMPPcSPfvQjli1b1qW+R7W1zVIf+w3xdSXogQaScNc0s+9wi+9yA9TUNHe4raL4foj6/zhaiB0yDWPFelo2/gf7xFtOfRcNDJzx7h9kvENLxvvELF4vABX1bVRXN/VKvWZ/HW9/3KfSpURo5creOV4gIiKCtra2Dpf5v4+MjOxwudVqPab2yG63B253sutNJhMej4df/OIXJCQkAL6u2DNnzmT//v1kZ2d3OmZVlY1CHXjd6Fp8M0Ke6KF4vGqgq3RarPWEYzUQxtExYi7GivW+5bEJfTMR8hsI492fyHiHloz3sQZFHmmq2GTv3aaKA3W8u3XERk/l5OTQ0NBATU1N4LLi4mJSU1OJju6YveXm5lJYWNjhsqKiokBNUE5OTofrXS4XJSUl5ObmBhIdp/NIPwX/8p20H+8ZXUsFiupB1ZvxRiRT3eLE4fai1ymkxgywHkLf4BgxFwBjRT6KrVrjaIQQ4giLUU+81Zf8HGqSgunO0CQRyszMZMqUKTz22GO0tLRQVlbG4sWLWbBgwTG3nT9/Pvn5+axYsQK3282KFSvIz8/nsssuA+CKK67glVdeCdQEPfnkkyQlJZGXl0d2djZTp07lN7/5DXV1dbS2tvL4448zduzYQCIluqfDjjFFCRytMTjGjEHXzyrqusgbnYYreQIKKub90kRUCNG3pMb4CqYPyc6xTtEkEQJ46qmncLvdzJkzh6uuuopZs2axcOFCACZNmsTy5csBXxH1M888w7PPPsvUqVNZvHgxTz/9NMOHDwdgwYIF3HTTTdx2223MmDGDXbt28eyzz2I0+jLiv/3tb+Tk5HD55Zcza9YsbDYbixcv1uZFDyBHegi1H7Y60LfOf4N/Vshc9IHGkQghREf+LfQyI9Q5mmyfh5MXXm/ZsqXD97NmzWLWrFnHva2iKNx8883cfPPNx70+Ojqahx56qGfBimMc6Srt2zV18Kit8+HAkX0pUV89jrH8CxRbNWrEIK1DEkIIgEB5QlVz53v7hTPNZoRE/6ZvnxHytM8IldUPzMNWT8QbOwxX8kQU1Yu56H2twxFCiIBUmRHqEkmERLfomtqXxqJ9p86Xh9mMEIAj93IALIXvahuIEEIcRY7Z6BpJhES36Jvbl8Zi0lFVNXDOWFqYzAgBOLIvQUXBeGhjIDEUQgitSbF010giJLrO40TXcsj3z+h0Gu1uWhy+tgRDY8NnRsgbmYorbQYA5qLlGkcjhBA+/qWx6hYHbq+0ijkVSYREl+laKlBQUQ1WVGsi5e2zQYOiTFiMne/WPRA4cnxtHCx739E2ECGEaJcQacKgU/CqUNMis0KnIomQ6LIjPYTS23sI+bfOh89skJ8j62JUnQlD7S70Nbu0DkcIIdApimyh7wJJhESX+U+d90SnAQTqg8Jlx9jRVEs8zuHnA2ApeFPjaIQQwkfqhDpPEiHRZbr2GSFvrL+HUHhtnf8m+6irALDsfRs8Lo2jEUKII3VCsnPs1CQREl0WaKYYMwwgUCMUTlvnj+bMOBuvdRC6tlpMB1ZpHY4QQpDS3lTxUJM0VTwVSYREl+kbSwHwxPhmhMoawntGCJ0B+8hvA2ApWKpxMEIIcdQxGzIjdEqSCIkuOzIjlEGby0NtqxMI3xkhAPso34HBppJPUWw1GkcjhAh3sjTWeZIIiS5RnC3o7HUAeGPSA4etxlgMxFiMWoamKU/iaN+J9F6XzAoJITQXKJaWXWOnJImQ6BJd+2yQ15KAaormoL+jdBhunf8m+9gbALDufBVUr8bRCCHCmX9prNnhptXp1jiavk0SIdElRy+LwZGt8+nhWh90FHvOfLymaPRNpRgPfq51OEKIMBZpMhBjMQCyPHYqkgiJLvlmIlQe2DovM0IYI3CMvAIA685XNA5GCBHu/LNClbI8dlKSCIku0Tf5dox52xOhg2F42OrJtI29HgDTvo/QtVZpHI0QIpwNli30nSKJkOgSXWP7jFBsx63zsjTm40kchWvwVBTVg0VmhYQQGhrcXjBd0SgzQicjiZDoksDxGjHDcHu8VDXJ0tg3tZ32PQCsO5aARz6AhBDakBmhzpFESHSe6j1y4GpMOhVNDjwqmA06kiJNGgfXdzhGzMUTNRhdWw3mwuVahyOECFP+GaFKSYROShIh0Wm61ioUjwNV0eONGkJZva8+KCPeiqIoGkfXh+iNtI37LgDWbS+AqmockBAiHA1ub2sixdInJ4mQ6DT/jjFv9FDQGSittwFSH3Q89rHXoRosGGt2YKzM1zocIUQYGhztS4RqWp043dLb7EQkERKdpvtmD6H2GaH0eEmEvkm1xGPPbd9Kv/UfGkcjhAhHsVYDFoPv17ycOXZikgiJTjvSQygdONJMMUNmhI6rbeIPUFEw7/8Ifd1ercMRQoQZRVGOWh6TOqETkURIdNo3mykeOKpGSBzLE5+Fc8SFAERs+bvG0QghwlGgYLpREqETkURIdFqgRihmGA63N3CYnyyNnZht0kIAzHuXoWuu0DgaIUS48W+hr5SlsROSREh02tHNFMsb21CBSJOehIjwPXX+VNypk3GmzUTxurBue07rcIQQYSaQCMmM0AlJIiQ6x92G3uY7MsITk3GkUDpOts6fim3ybQBYdy5BsdVoHI0QIpz4l8akqeKJSSIkOkXfdBAAryka1Rwn9UFd4Eo/G1fyBBS3nYgtf9M6HCFEGPHPCFVIL6ETkkRIdEqHQmlFCSRCUh/UCYqCberPALDueFlmhYQQIePfNVbd4sDtkV5CxyOJkOgU3TdOnQ9snZdEqFOcw85tnxVqI2Kr7CATQoRGQoQRk17Bq0JVi8wKHY8kQqJTvrl1/ugaIdEJR88Kbf8XSuthjQMSQoQDnaKQGjh8VRKh45FESHSKvvFIItTm8nC4xQnIjFBX+GaFJqK424jc9JTW4QghwoS/YLpCdo4dlyRColP0zUcSIf9sUKzFQKxVts53mqLQOvOXAFh2voqusVTjgIQQ4cA/IyTdpY9PEiFxaqoamBHyxg4L1AdJoXTXuYaegTP9LBSvi8j8J7UORwgRBtJiZefYyUgiJE5JaatFcdtQUfBEpx3ZMSb1Qd3SOuMXgK/btL5ml8bRCCEGukAi1P5HrOhIEiFxSnr/jrGoVNCbpYdQD7mTJ2DPno+CStTn94Oqah2SEGIAG9KeCJVLjdBxSSIkTulEO8YkEeq+1pm/QtWbMZWvw7RvhdbhCCEGsLRALyEnDrf0EvomSYTEKembygDfYauA1Aj1Am/MUGyTfgxA1BePgFumrIUQwRFnNRJh1KMiBdPHI4mQOCV/M0VPTAYtDjd1NhcgNUI9ZZu8EE/UYPTNZURslQNZhRDBoSgKaXGyPHYikgiJUzqyNJYeqA9KiDASZTZoGVb/Z4ygdeavAIjY9DS6lkqNAxJCDFRD2rfQlzdIIvRNkgiJUzq6maLUB/UuR87luFLzfE0W1/1W63CEEAOUf0ZImioeSxIhcXJuO7qWCgA8sZkcaJCt871KUWiZ9SAAlr1vYzi0SeOAhBADUVpg55jUI36TJELipPRNB1BQ8RqjUK1Jcup8ELiTJ9A26jsARK2+D7xujSMSQgw0soX+xDRLhGpra1m4cCF5eXlMnz6dRx99FLf7+L8AVq9ezaWXXsrEiROZO3cuq1at6nD9c889x1lnncXEiRO54YYb2Ldv33Ef55577uGGG27o9dcykOkb9gPgiRsOihJYGhsmiVCvap15L15zLMaaHVi3Pqt1OEKIASYt1veZXdFoR5XeZR1olggtWrSIiIgI1q5dy1tvvcW6det46aWXjrldSUkJt99+O3feeScbN27k9ttvZ9GiRVRVVQGwbNkylixZwgsvvMD69esZO3Ysd9xxxzH/o9966y3ef//9ULy0AUXfWAKAJ3Y4qqrKjFCQqBGDaDnjfgAi8/+IvuH4ybwQQnSH/+DVVqeHRrvMOh9Nk0SotLSU/Px87rnnHqxWK+np6SxcuJBXX331mNsuW7aMvLw8zjvvPAwGA/PmzWPq1Km88cYbACxdupRrr72WnJwczGYzd911FxUVFaxfvz7wGEVFRSxevJgrr7wyZK9xoNA3ts8IxWbS2Oam2eH7AZIaod7nGHWl7xwyj4OoVT8HVRqfCSF6h8WoZ1CUCZDlsW/SJBEqLCwkLi6OlJSUwGVZWVlUVFTQ1NTU4bZFRUXk5uZ2uCw7O5uCgoLjXm80GsnMzAxcb7fb+elPf8r999/PoEGDuh2zooTnlz8R8sYNp6y9yC45yoTVpO/yY4XzOHbqS6fQMvtxVIMVU8VXWHa91qPHk/EO7ZeMt4x3X//y1wlVNLaFzXh3hiaNYFpbW7FaO84o+L+32WzExMSc9LYWiwWbzdap6x966CHOOOMMzj77bL7++utux5yYGN3t+/Zrzb6t89HDxlJf7VtuzEqOJimpe+MRtuPYWUljYc6v4aNfEb3uMaInXwYxQ7r9cDLeoSXjHVoy3l2TlRzNtvIm6l1qtz7DB+p4a5IIRURE0NbWcQuf//vIyMgOl1utVuz2jtN4drs9cLuTXb98+XIKCgr497//3eOYa2ubw+9sTLedxMaDKEAtyewqqwcgNcpITU1zlx5KUXw/RGE5jl2VdR2xKUsxVm3FsWwRzfOe7/yfNu1kvENLxju0ZLy7J9GiB6CosrFLn+H9dbz9cZ+KJolQTk4ODQ0N1NTUkJSUBEBxcTGpqalER3cMOjc3l507d3a4rKioiHHjxgUeq7CwkNmzZwPgcrkoKSkhNzeX559/nv3793P66acD4HA48Hg85OXlsXz5coYM6fxf2qoafoeE6xvbt86bovFaEimtOwz46oO6OxbhOI5dpuhpnv0H4pfOxbz/Ixx7/oNj5IJuPZSMd2jJeIeWjHfX+JfGyhrs3Rq3gTremtQIZWZmMmXKFB577DFaWlooKytj8eLFLFhw7If9/Pnzyc/PZ8WKFbjdblasWEF+fj6XXXYZAFdccQWvvPIKBQUFOBwOnnzySZKSksjLy+OFF15gy5YtbNy4kY0bN/KDH/yAKVOmsHHjxi4lQeEqsHU+djgoCiV1vlm74YkRWoYVFjyJo7FN/SkAUav/D1377j0hhOiuoe1b6MsbpKni0TTbPv/UU0/hdruZM2cOV111FbNmzWLhwoUATJo0ieXLlwO+IupnnnmGZ599lqlTp7J48WKefvpphg8fDsCCBQu46aabuO2225gxYwa7du3i2WefxWg0avXSBozAjrG44Xi8KgfqfXVXmQmSCIWCbfJPcA6ejs7VQswnt4PHpXVIQoh+zN/25FCTA4dbdqX6Kap0VuqUmpr+tTbaG6I+uxfrzldozbuTPbk/4dsvbsCkV1hzx5nodV2vWUlKig7LcewJXXM58f8+H52zida8O7FNv6dT95PxDi0Z79CS8e4eVVU55+kvsbk8LL0pr9Oz+/11vP1xn4ocsSFO6OilsZI632zQsISILidBovu80Wm0nPM7ACI2PoWx4iuNIxJC9FeKogRmhfzNcYUkQuIkjl4a8ydCsiwWeo6cS7GPugoFlehP7kCxN2gdkhCin/I3wy2TOqEASYTE8bnb0B916vz+Wn8iJB2ltdAy6yHcsZnoWyqIXnmXdJ0WQnRLRnz7zjGZEQqQREgcl76xFACvKQbVkhDYMSYzQtpQTVE0X/AMqs6Eef9HRGx6WuuQhBD90FCZETqGJELiuPQNxQB44rNQIbA0JlvnteNOnkDL2Y8AELH+CUwln2ockRCiv8lorxGSGaEjJBESx2Wob0+E4rKos7lodrhRgIx4SYS0ZB9zLW1jb2ivF/qJnFIvhOgSf7F0VbNsofeTREgcl76+CAB3fHZgNmhIrAWzQd4yWmuZ9SCu1Dx0zmZiVtyC4mzROiQhRD8RbzUSadKjAuWNMisEkgiJEzh6acxfKC3LYn2E3kTTRc/iiUzBUL+X6E8XSfG0EKJTFEWR5bFvkERIHEtV0R+1NCZb5/seb2QKTRf9w1c8ve+/RH75qNYhCSH6CX/BtPQS8pFESBxDZ6tC52pBVfR4YocdlQjJ1vm+xJ06heZzfw9AxNZnsW79h8YRCSH6A3+dkOwc85FESBwjMBsUkw5681E9hGRGqK9xjFxAy8xfARD1xUOY976jbUBCiD4vI7CF3q5xJH2DJELiGEfqg7Jptrs53OIEICspUsuwxAm0TfoxtvE3AxD96U8xlq3VOCIhRF+WLjVCHUgiJI7h3zHmictiX20rACnRZqLMBi3DEieiKLSe+QD27EtRvC5iPrwF/eHtWkclhOij0uN83aWrmh3YXR6No9GeJELiGIajdowV1/gSoawkWRbr0xQdzef9GWfaTHSuVmLfvQYqtmodlRCiD4qzGomx+P6wlYJpSYTEcfhrhNxx2RTX+OqDshJlWazP05tpmvsCrpRJ6BwN8PJl6A9/rXVUQog+RlGUQM2nfzNMOJNESHTkakPffBBonxGq9c8ISSLUH6jmGBrnv4YrdQrYG4h99xoMVVu1DksI0ccMl0QoQBIh0YH/yAavOQ6vOZ6ialka629UUzRN81+F9BnoHI3ELr8WQ9UWrcMSQvQhw9rboeyvlaUxSYREB4ajdozVtblptPvOGJOt8/2LaoqC69/CNWQ6OmcTse9eI7vJhBAB/pMCSutlRkgSIdGBvm4PAO6jCqXT461YjHotwxLdYY6m8ZKXcaadjs7VQuz7N2AueFPrqIQQfYD/j9vSOhser6pxNNqSREh0YGhPhDyJoylub6Q4Qs4Y679MkTReugR7zmUoXjcxn/6UiA1/AjW8P/iECHeDYyyY9ApOj0plU3g3VpRESHSgr9sLgDsh96it81Io3a/pzTSf/zS2ybcBEJn/JFEr7waPS+PAhBBa0esUhrXPCvlPDwhXkgiJI9x29I0lAHgSRrJPEqGBQ9HROvOXNJ/9OKqiw1rwBrHLr0ZpPax1ZEIIjcgWeh9JhESAvr4YRfXiNcfisQ460kNIdowNGPZx19M07594jVGYKtYTv3Quhop8rcMSQmjAf5C2JEJCtDPUFQDgThhFZbMTm8uDQacEDugTA4Mzcw4NV36AO2EkelsVce9ehXXb81I3JESYyQwsjYX3FnpJhESAob0+yJM4ksLqFsC3xdKgl7fJQOOJz6L+iuWBIuqozx8g+qMfo9gbtA5NCBEiR2+hV8P4DyH5DScCAlvnE3LZc9iXCI1MjtIyJBFMpkiaz/8rzbMeQtUZsBS/T/y/z5N+Q0KEifQ4KwrQZHdTZwvfzROSCImAwIxQwkj2HvYVSudKIjSwKQr28TfT8O1luGOHo289RNzya4hc+xtwh/d0uRADncWoZ0is7yT6cK4TkkRI+Dhb0TcdAMCdMJK97UtjuYNkx1g4cKdMov47H9E27kYAIr5+kfil8zAc2qxxZEKIYPIvj/k3x4QjSYQEAIb6QgC81kE0EE1lkwOA3EEyIxQ2jBG0nP0YDZcswRORgqG+kLj/XEbU6vtQHE1aRyeECIKc9j92i2paNI5EO5IICaBjfVBh+0GrQ2ItRFsMWoYlNOAaNpv6a/6HfdSVKKhYd/yL+NfOwVz4nuwsE2KAyW7vE+c/YDscSSIkgCP1Qe5EWRYToFriaZ7zJxouewN33Aj0tsPEfPxjYt+/PtB9XAjR/2W3f84X19jwhukfOpIICQAMNbsA8CSOYm/7jjEplBauoWdQf/UntE79GarOhOnAauL/fT5Rq3+F0lardXhCiB7KiI/AqFewuTxUNIbnmWOSCAlQVQw1OwBwJ41jT/uOMdk6LwDQm7FN+xn11/wPx4iLUFQP1h0vk/DKmVg3/w3c4fnhKcRAYNApDG9vrBiuy2OSCAl0LZXo7PWoip7WmGz2t2+jlKUxcTRP3Aia5j5Pw+VLcSWNQ+dsJmrdoyS8cgaW7S+Bx6F1iEKIbjhSMC2JkAhThpqdAHgSctjf6MHjVYm1GEiJNmscmeiLXGmn03DVCprm/AlP1BD0rVVEr/k/El45E8uOJeBxah2iEKILstt3B0siJMJWx2UxX31QTnIUiqJoGZboyxQdjlFXUnf9WprPfgxPZCr6lkqiV/+ShFfOwLrl77LlXoh+Iqd951ihLI2JcGWo9idCY9l1yJcIjUmR+iDRCXoz9nE3Unf95zTPehhPRAr6lkqivnyEhH9NI/Lzh9A1l2sdpRDiJLLal8YONrRhd3k0jib0JBESgR1j7kFj2XmoGYCxqdFahiT6G4MF+/jvUXfjlzTPfgJ3fC46VwsR2/5BwpLTifnwFoylq8Abfh+yQvR1iRFG4q1GvCrsqw2/DtOSCIU5xd6AvrkMgJa40RS19xAaI4mQ6A69GfuYq6m/5n80XvIyzrQzUFQP5n3/Je79G0h45QwiNj6FrqVS60iFEO0URQn0EwrHnWOSCIU5Q217/6CYDPY06vGokBBhlEJp0TOKDuewc2m8/A3qrv4ftvE34zXHom8+SOT635Pwr2nEvvMdLLteR3E0ah2tEGHP32G6MAwLpiURCnOGat+OMXfSmA7LYlIoLXqLJ3EUrbMeovamjTTN+TOuwdNQUDGVf0H0qntIfHESMSu+j3nPf1DsDVqHK0RY8veNK6hq1jiS0JODpMLc0TvGdlb6dvmMHSzLYiIIDFYcoxbgGLUAXdMBzIXLsexdhqFuD+b9H2He/xGqosc1ZAaOERfizDwPb0yG1lELERZGp/oToRY8XhW9Lnz+GJZEKMwFdowNGseur6VQWoSGNyaDtik/oW3KT9DX7sZc9D7m/R9hqC3AVP4FpvIvYO1vcMdm4ko/C2f6WbjSTkc1x2gduhAD0rD4CKxGHW0uLyV1NrKSwqehriRC4czZir6+EIDa6DGUNRQBMDpFEiEROp7E0dgSR2Obfg+6xhLM+z/GtP8jjIc2YWgswdBYgnXHy6iKHnfKJJzps3ClzcSVMgkMVq3DF2JA0OsURiVHsaW8id1VzWGVCGlWI1RbW8vChQvJy8tj+vTpPProo7jd7uPedvXq1Vx66aVMnDiRuXPnsmrVqg7XP/fcc5x11llMnDiRG264gX379gWuO3jwID/5yU+YMWMG06dPZ+HChZSVlQX1tfUXxuptKKoXT9QQdjZbAEiPsxBrNWocmQhX3thM2ib+gMZv/Yfa72+ncd6LtJ12E+64ESiqB+OhjURu+BNx71xF0nNjiHtrPpFfPoJp/8co9nqtwxeiXxvdvhqwu72fXLjQLBFatGgRERERrF27lrfeeot169bx0ksvHXO7kpISbr/9du688042btzI7bffzqJFi6iqqgJg2bJlLFmyhBdeeIH169czduxY7rjjDlRVBeC2224jNjaWlStXsnLlSuLi4li4cGEoX2qfZajaAoA7ZVKgUFq2zYu+QjVF4xx+AS1nPUL9dWuoveErmmf/Hnv2fDyRKSheF8aqzURs+TuxK24m6YXTiH99DlGf3Yt5z9voGkug/XNACHFqY9pXA3aHWcG0JktjpaWl5Ofns2bNGqxWK+np6SxcuJA//OEP3HLLLR1uu2zZMvLy8jjvvPMAmDdvHm+//TZvvPEGd9xxB0uXLuXaa68lJycHgLvuuoulS5eyfv16Ro8eTVJSEnfeeScREb7TdW+88UYuu+wyGhsbiY2NDe0L72OM7YmQK2UyO0ra64MGSw2G6Ju8MUOxj7kW+5hrQVXRNZdhrMjHWLkeY+UGDPVFGOr2YKjbg3XnK777WOJxJU/EnTIJd8pEXMkTUa0JGr8SIfom/4zQ3upW3B4vBn14bCzXJBEqLCwkLi6OlJSUwGVZWVlUVFTQ1NRETMyRX8ZFRUXk5uZ2uH92djYFBQWB62+99dbAdUajkczMTAoKCpgxYwYvvPBCh/t+9NFHpKWldTkJGnC7yVUVwyFfIuRMmcjXX/p2jE1MiwnKa/U/5oAbxz5qwI+3oqDGZuCMzcA5eoHvorZajJUbMFbkY6jcgKF6Jzp7PeYDqzAfOLKc7okd5kuOUifhTp6Ee9BYMFh6Gk6H/4rgkvEOjvR4C1FmPS0OD/vrbOS2b6nvr+Pd2Xg1SYRaW1uxWjsWOfq/t9lsHRKh493WYrFgs9k6df3RXn/9dV588UX+9re/dTnmxMQBtmTUeBBsVaDoOZwwnib7BiJMek4fnRrUvwIG3Dj2ceE13tGQnglc6fvW7YSq7VC+GQ5uhPJNUFuIvrEUfWMpFL7ru53OACnjYGgepE2BtDxIzAZd138Owmu8tSfj3fvGD43jy+JaylpdnJ7UcXwH6nhrkghFRETQ1tbW4TL/95GRHSvVrVYrdru9w2V2uz1wu1NdD+B0Ovntb3/LihUrePbZZ5kxY0aXY66tbR5Q5QamorXEAO6k0awq8BWZjhscTUN9cLqKKorvh2igjWNfJePdzpwLI3JhxNWA70gZw+GvMVRtwVC1FWPVFnRtNVC51fe14XkAvOZY3MkTcadOxJUyGXfKpJMuqcl4h5aMd/BkJ1j5shjyi2qYMzwe6L/j7Y/7VDRJhHJycmhoaKCmpoakpCQAiouLSU1NJTq6Y9C5ubns3Lmzw2VFRUWMGzcu8FiFhYXMnj0bAJfLRUlJSWA5ra6ujh//+Mc4nU7eeust0tPTuxWzqg6sukv/spgreRJbDvqOOJiYFhv01zjQxrGvk/HuSDXH4WzvS+S7QEXXXI7hsC8pMlZtwVD9NTpHI6ay1ZjKVgfu64kZhivFV2/kSpmEe9A40Hc8ikbGO7RkvHufv33KrkPHJj0Ddbw1SYQyMzOZMmUKjz32GA899BD19fUsXryYBQsWHHPb+fPn889//pMVK1ZwwQUX8PHHH5Ofn899990HwBVXXMHTTz/NWWedxfDhw/nTn/5EUlISeXl5uFwubrnlFuLj43nmmWewWHpWBzCQGKq2AuBMnsTWPb5EaFJaeBePizCkKHhjhuKMGYoz+xLfZR6Xr+i6agvGqs2+2aP6IvRNpeibjiypqToj7qSxvqRocB4YzgY1FuhnhRRCHGVc+8kCew+30ObyYDXqNY4o+DRrqPjUU0/x0EMPMWfOHHQ6HZdffnlgW/ukSZN48MEHmT9/PllZWTzzzDM88cQT3HfffaSlpfH0008zfPhwABYsWEBzczO33XYbdXV1nHbaaTz77LMYjUY+/vhjdu7cidlsZubMmR2e/4MPPmDIkCEhf919gteNsfprAA5EnsbhlmoMOiXwAyBEWNMbcQ8ah3vQOOzjbgBAcTRiOLwN46HN7QnSFnT2OoyHt2I8vBW2/xM+hvjIVFypebgH5+FKnYI7aSzoTdq+HiG6IDXGQkq0mapmBzsqm5iaEa91SEGnqOpAnOjqfTU1/Wtt9GQMVVuJf+sSvOZYXp75Px74byGnDY7mxWsnBe05FQWSkqIH1Dj2ZTLeQaaq6JoO+JbSDm3CWLUZY/UOUD0db6Y3+5bTUvNwpfqSI9m+33Py/g6u//tgNx8VVPODmcO49fRh/Xa8/XGfihyxEYaMFV8B4Bo8jS3lvv5BE2VZTIjOUxS8scNwxA7DkXu57wM3RkfD7i8wVG7ybeM/tAmdowFTxXpMFesDd3XHjWhPjKbgSs3Dk5ADSnj0axH9w4S0WD4qqGZreaPWoYSEJEJhyFi+DgBX2ky2bva90SdIIiREz5gicafNxDVkJm0Aqhd9wz6MlRsxHNroOzutvhBDwz4MDfuwFCwFwGuKwZ06uX3GKA93ykRUU5SmL0WEN3+96PbKJtxeFaN+YNe9SSIUbrwejJX5AJTHTaO0vgkFXyNFIUQvUnR44rPxxGfDGP/2/XpfndGhTRgPbfTVGjmbMB34DNOBzwBQFR3uxNG4U/NwDzoNd+JI3PG5YAqfQzCFtkYkRQQaK+493MLYAV4/KolQmDHU7ETnbMZriuaL5mSgiTGp0XLQqhAhoFricWbOwZk5x3eB142hdjeGyo2+xKhyI/qWcow1OzHWdGwb4olOx52QiychF3fCSDyxw/DGpOONSJalNdGrdIrChCGxfLG/jq3ljZIIiYHl6Pqg9Qd8y2LTMwf+rgAh+iSdwTfrM+g07OO/57uopQLDoc2+pbTa3ejr9qK3HUbfXIa+uQxKP+3wEKrejCd6KN6YdDwxGb5/Rw3BEzUEb9RgvJGpoJc/dETXTEyL4Yv9dWwrb+K6PK2jCS5JhMKMvz7IMWQm+V81ADBjmCRCQvQV3qghOLOHHOlrhG9JzVC3B33d3vb/FqJvOoCupQLF48DQUAwNxcd9PBUFb0SyLymKGtyeIA1pT5YG+/4dmew7akSIdv4NNFvLGxnom8vlnR9OjqoP2mmdSn1bKxFGPacN8GlPIfo71RKPa8gMXEO+cTyQx4WupQJ9Uxn65gPomsrQNx/0XdZSia6lEsXrQm+rQm+rgsNbj//4ig5vZAreyKMTpcFHEqXoIXitg0A38JvrCZ/RqdGY9Ap1NheldW0MGjRw60glEQojhtrd6ByNeI1RfNGUCLQyJT02qIesCiGCSG/EGzsMb+wwXMe7XvWitNWib6lA11KBrqWy/d9H/qtrPYTidaNvqUTfUomxavNxn0rVGfDEjsCdOBJP4ijcCSNxJ47GG5PR/44lF6dkNuiYkBbLhgMNfFVaT97IFK1DChpJhMKIsWwNAK4h01h/oAmAGVIfJMTApehQIwbhjhgEyROOfxuvB11bNbrmCnStvmRI1+xLnPStlb4EqrUKxevGUL8XQ/1eKHrvyN0t8biTJ+BKnuA7hy11CqpFPlcGghnD4tlwoIF1++v5idbBBJEkQmHEVLoSgMahc9j6WXuhtNQHCRHedHq8kam+ouoT8brRtRzCUL8Xfe0eX51SbQGGuj3o7PUdt/+j4E4ag6u9p5JryDRJjPqpGZnxPL12P5vKGnC4Pae+Qz8liVCYUBxNGCs3APCFbhouTzWDY8xkxFs1jkwI0efpDIHDaRl27pHLPQ4MNbt957Ad3orh0GYMDcVHtv9ve75jYjR0Fs4hM6QnUj+RMyiSxEgTta1ONpbUMzLOrHVIQSGJUJgwlq1BUT2447NZWeGrCTorKxFF1vaFEN2lN+NOmYg7ZSJ2vguA0noYU8V6jBXrMJavw1Bf2DEx0hl9x4ukn4UzfRbuQeOlCLuPUhSFGcPi+GDXYdbsrWbktKFahxQUkgiFCVPpKgBs6XNY83UtAOdkJ2kZkhBiAFIjk3HkXIoj51LgqMTo4OeYytagby7DVPEVpoqviFz/e7zmWFxDz8SZPgtn+lm+4mvRZ8zITPAlQoU13CqJkOi3VC/m9vqg9RHn0Gh3E2sxMHGonC8mhAiuDomRqqJrLMF0cC2msjUYD36JztGIufgDzMUfAOCJGYYz42yc6bNwpZ2OapbPKS1NHxaHAuyubKKm1UlihEnrkHqdJEJhwFC9A11bNV5jJP9rSAaqmJWViEEny2JCiBBSFLxxw7HHDcc+7kbfESOHt2EqW4OpbC2Gqs3om0qx7ngZ646XfeeuJU9sny06G3fKJOmSHWLxESZGpUSxu6qFL/fVcem4kxTV91OSCIUBU3tLfmfamXxW3ADIspgQog/QGXCnTsGdOgXb1J+iOJsxlq/zzRaVrfUVXldtxli1mciNf8FrjMI1ZBqe5PGQNQ2dORtP5GDpYxRkZ2UlsruqhVWFNZIIif7JXPQ+AFsT5lFV4MBq1DF9WJy2QQkhxDeopmicwy/AOfwCAHTN5YGkyHRwLTp7vW+Zv3QlbIAEwGtJwBOfjSd2GJ6YYXhiM/HEpOO1JKBaE1FN0ZIo9dCc3CSe/bKUr0rraXG4iTIPrNRhYL0acQx/zw9VZ+SDtjFANTMzE7AYZZeGEKJv80anYR9zDfYx14DqxVCzE0PlBozVO7DU70at3o3OXoeuMj9wfNA3qToTXms8qjEK1RgBBiuqwYpqsPj+a4xo/4o86r/t/zbH+Y4aiRwMxvBtNTIiKZLs5CiKDrewpriWeWMGVpdpSYQGOHPRcgBs6bP5cK+vieJFo5O1DEkIIbpO0eEedBruQafhUMCSFE3toWr0tXvRN+5H31iKrrEUfVMJ+uYKdG21KG4biteJvrUKqOrR03vNcXijhuBOyMGdOBp38njcKZN8M05hYN5pg3nq00JW7q2RREj0I6qKudCXCH0W+21q9jiJtRg4c0SCxoEJIUQvMFh8CUny+ONf72pD11aLzl6L4rKhuNvA3YbibkNx2dv/2+pLmFz+r9YjX/Z69M0VKG4bOkcDOkcDhtpdUPgu4O+iPRZXxjk4MufgTp0CysA8u3Heaak89Wkh60rqBtzy2MB5JeIYhpodGBr3oxosLKsbBtRz0ehkjHLIqhAiHBiteI1D8cb0oP+NqqI4m3wH1TYfRF+3B0PNLoxVW9A3HcBYswNjzQ4iNv8VT2QqjuxLsY++Ck/i6N57HX3AyJRoMuKtHKhv44t9dVw4gFYWJBEawMztf7UcHjqXNYUNAFwydmBNaQohRFApCqo5Fo85Fk/iKMg8L3CVrrUK48G1mEpXYSpdhb71EBHbniNi23O4UibTNv5mHNmXDojO2YqiMCc3iX+uL+O/BYcHVCIkUwMDlceFee8yAJabL8XpUclOimRkcpTGgQkhxMDgjUzBMXIBzRc8Q+3NW2ic9yKOrHmoOgPGqs3EfPIT4l87B/PuN8Dj0jrcHru4vTboy/11VDU7NI6m90giNECZSj5B31qFx5LEfyp9NUGXjE2Rs8WEECIY9Gacwy+g6aJ/UHtjPq3T7sJrjsPQuJ+YlXeR8OosLDuW9OuEKDMxgklDY/GqsHzHIa3D6TWSCA1Q1h0vA7Bm6A8pOGzDbNAFsnkhhBDBo0YmY5v6U2pvXE/L6f+H1zoIffNBolf/kvg3LsB4YLXWIXbbt8b7Giq+u/0QHq+qcTS9QxKhAUhfX4Tp4OeoKPyzaSrgmw2Ki5DW9EIIETKmSNom/YjaG7+k5cwH8VoSMNQXEvfedcR88D30Dfu0jrDLzs0ZRIzFQFWzg69K6rUOp1dIIjQAWXYsAaBg8LdZc8AGwDWT07QMSQghwpfBStuE71N3/VpsE25F1Rkwl3xC/OtziFz3OLjtWkfYaUevLrz9daXG0fQOSYQGGMXZjKXgTQBeVL4FwKwRCQxLiNAyLCGECHuqOZbWM++n/ur/4cw4B8XrImLzX4l/4wIMFcfvjN0XfWv8YADWFtdSUmfTOJqek0RogLF+/SI6ZxMHoyeyvNTXHeG6vB700BBCCNGrPPHZNF76Co1zn8MTkYyhYR/xy75N1Jr7UJwtWod3SsMTIzgrKxEVeGn9Aa3D6TFJhAYQxdGEdes/AHjSegcOj5fxQ2KYPDRW48iEEEJ8k3PEXOqvWUnb6KsBsG7/F/Gvn4updKXGkZ3a92dkAPDf3Yc52NCmcTQ9I4nQAGL9+gV0jkZ2R53Ou2UWAO44a7hsmRdCiD5KtcTRcu4TNMz/N56YDPQtFcS+fyPRn9yBYu+7xchjUqOZmRmPR4WX8su0DqdHJBEaIBRHI9ZtzwPwO8MP8KpwTnYiE9JkNkgIIfo6V/qZ1F39P2wTfoCq6LDsfZuE12ZjKnof1L65Td0/K/T+zqp+PSskidAAEbn+9+gcjayOuIjPDpnQK3DbmcO1DksIIURnGSNoPfM3NHz7HdwJI9G11RD70Y+I+e+t6FqrtI7uGBPSYpmRGY/Hq/LEymLUPpqwnYokQgOAoWoLlu0v06JauNd+IwBXTBhCZqLsFBNCiP7GnTqZ+qtW0Jp3p2+r/b7/Ev/6uZh3L+1zs0N3z87CqFf4Yn8dnxXVah1Ot0gi1N953UR9di8KKg9H/R+VNhgSY+a2WTIbJIQQ/ZbejG36PdRfuQLXoPHoHI3ErPwZse9fj67poNbRBQxLiOCG9p3JT64qps3l0TiirpNEqJ+L2LwYY81OPtPN5I3aEQD8+sKRRJj6/2nHQggR7jxJY2hYsJyWmb9E1ZsxHVhNwuvnYt28GDxOrcMD4HvTMxgcY6aq2cEfVxVrHU6XSSLUjxnL1hCR/wT7vanc4boNgAUTBpOXEadtYEIIIXqPzkDb5Nuo/87HuAZPQ3HbiFr3GPH/Ph9j2Rqto8Ni1HPfBbkowDvbD/HBzr5Xz3Qykgj1U7qmg8R8fBtNXgs3Kw/S5NJx2uBoFp2TpXVoQgghgsATn0XDt96iac6f8VoHYWgoJm75tcR8eCu6Jm0bG04fFs+tM4cB8Nv/FVJU3appPF0hiVA/pLTVEvvBd2lrs/EDfs1+RzTJUSZ+f9lYzAb5XyqEEAOWosMxagF1163GNv77qIoe874PSXj1bKJW/wpd6yHNQrt5RgbTh8XhcHtZtGwH5Y39Y0u9/NbsZ5S2WuLeuYrW2jKu9/yG9Y5hRBj1/PHycSRFmrQOTwghRAio5hhaZz1I/Xf+izP9bBSvC+uOl0lYcgaRXzyM0lYX8pj0OoVH5o0mM8FKVbODHy/9mkNNff9AWUmE+hF9wz7ill3JwZoGvuN+kC3u4cRYDCy+8jRGpkRpHZ4QQogQ8ySOpnH+qzRc/iauwVNRPA4itj5L4svTifrsl+jrCkMaT1yEkb9dOZ6MeCuVTQ5u/fc2CqqaQxpDV0ki1E+Y9n1E3JsX82FNApc4f0uBJ42ECCN/v2o8YwfHaB2eEEIIDbnSZtLwrbdpvORlXINOQ3G3Yd25hITXZxO7/FpMJf8Db2i2tidFmVncngwdanZwy7+38f7OQ3224aKi9tXI+piammZN+ljpWiqI/PJRyvfk86j7OlZ6JwMwMS2GRy4eTUq0OfRBdYOiQFJStGbjGG5kvENLxju0ZLxPQVUxVqzD+vWLmPZ/jKJ6AfBaB+HInocjez6uwVNB6dxcSHfHu8nu4jcr9vDFft8y3RnDE7hrdhbp8dYuv6Tu8Md9yttJItQ5of6B0zWXY932Avu/Xs0/nefwjucM3BjQ6xS+Oy2dW2cOw6DrP4epygdXaMl4h5aMd2jJeHeerqkM6/aXsOx+A52jIXC5JzIF54iLcKadgSttJqol/oSP0ZPx9qoq/1x/gOfXHcDtVTHqFeaPS+WayWkMSwju6Qd9PhGqra3l17/+Nfn5+ej1eubPn88vfvELDAbDMbddvXo1TzzxBGVlZQwePJif//znzJ49O3D9c889x5IlS2hqauK0007jwQcfZMQIX3NBm83Gww8/zMqVK3G73cyZM4f777+fyMjILsUbih84xdmCsfhDDu1ew2dlblZ4prJNzQ5cf+aIBO48ewSZQX7zBIN8cIWWjHdoyXiHlox3N3hcmA6uxVz0HqZ9/0XnPFK3o6LgSRyNc+jpuAedhjtxNJ74bND7NuD0xniX1tl4YmUxX5XW+x4TyMuI47yRgzgvN4kYi7Gnr/AYfT4RuuGGG0hJSeHhhx+mpqaGH//4x1x++eXccsstHW5XUlLC/Pnz+eMf/8g555zDxx9/zC9/+Us+/vhjUlJSWLZsGX/605944YUXyMjI4E9/+hOff/457733Hoqi8Mtf/pLKykr+/Oc/4/F4WLRoEdnZ2dx///1dijcYP3A2p4cD9TZK69oorbdxcNsnbLIlc5gjmbleUTk3dxBXTx7K+CH9txZIPrhCS8Y7tGS8Q0vGu4c8DkwH1mAq+wzjwXUY6vcecxNVZ8QTn407PgdvTBoRg7NpVJLwRA3FG5GEao4DXddOMFBVlc0HG3l140HW7juyqy0t1sI7t0zr4Ys6Vp9OhEpLS7ngggtYs2YNKSkpAKxYsYI//OEPrFq1qsNt//SnP7F9+3ZefPHFwGW33HIL48eP54477uCaa67h7LPP5kc/+hEALpeL6dOns3jxYiZMmMDUqVN5+eWXmTzZV1uzbds2brzxRr766ius1s6vU/b2D9yjH+/lne3H7/dgULxMSrUwe0wGs3OSBsS2ePngCi0Z79CS8Q4tGe/epdiqMZWvw1iZj75mN4ba3eicTSe9j4qCao7Fa01ANUWjGiNQDRG+/7b/G2MEqs4IOgOqzgCKvv3fesrsVj6uiuLTwxGkDhrEY/PH9/7r6mQidOw6VAgUFhYSFxcXSIIAsrKyqKiooKmpiZiYIzMfRUVF5Obmdrh/dnY2BQUFgetvvfXWwHVGo5HMzEwKCgqIi4vD5XJ1uH9WVhZ2u52SkhJGjx7d6Zh1ut499Le+zUWU2UCsxUB6vJWMeAvp8RGMSo5iTGoUFuPAOitMaS9n6u1xFMcn4x1aMt6hJePdy6IG4Ro5H9fI+b7vVRWluRJDXQG6xgPoWw5idVTjritF11KJztmEAih40LVVQ1t1l59yTPvXIqAt+QZsuuAkQp2hSSLU2tp6zGyM/3ubzdYhETrebS0WCzab7ZTXt7S0ABARcaSmxn/b1tautf9OSDh1VtkV/7plRq8+Xn/R2+MoTk7GO7RkvENLxjuIkmJg+MgOFwUrYbC2f2lFkz5CERERtLV1bL3t//6bRcxWqxW7vWNnSrvdHrjdya73J0BHP5f/31FR0oBQCCGECHeaJEI5OTk0NDRQU1MTuKy4uJjU1FSioztm+Lm5uRQWduyMWVRURE5OTuCxjr7e5XJRUlJCbm4uw4cPx2g0UlRU1OF5/MtnQgghhAhvmiRCmZmZTJkyhccee4yWlhbKyspYvHgxCxYsOOa28+fPJz8/nxUrVuB2u1mxYgX5+flcdtllAFxxxRW88sorFBQU4HA4ePLJJ0lKSiIvLw+r1crcuXN54oknqKuro66ujieeeIJLLrkEi8US6pcthBBCiD5Gs+3zNTU1PPTQQ6xfvx6dTsfll1/O3XffjV6vZ9KkSTz44IPMn+8r3Fq7di1PPPEEBw4cIC0tjXvuuYezzz4b8G3H++c//8mrr75KXV1doI/Q8OHDAWhpaeF3v/sdK1euxOVyMWfOHH796193qBsSQgghRHiSztJCCCGECFty6KoQQgghwpYkQkIIIYQIW5IICSGEECJsSSIkhBBCiLAliZAIutraWhYuXEheXh7Tp0/n0Ucfxe12ax3WgFFQUMD3vvc9pk2bxhlnnMHPf/5z6up8Bxpu27aNK6+8kkmTJnHuuefy5ptvahztwODxeLjhhhu49957A5fJWAdHQ0MDP//5z5k+fTpTp05l4cKFHD58GJAx7207d+7kuuuuIy8vjzPPPJNHHnkEp9MJDPCxVoUIsuuvv1696667VJvNph44cEC9+OKL1eeee07rsAaEtrY29YwzzlD/8pe/qA6HQ62rq1NvvfVW9Yc//KHa0NCgTps2TX3llVdUl8ulfvnll+qkSZPUbdu2aR12v/fnP/9ZHTVqlPqLX/xCVVVVxjqIrr/+evW2225TGxsb1ebmZvUnP/mJ+oMf/EDGvJd5PB71jDPOUP/1r3+pHo9HraysVC+88EL1r3/964Afa5kREkFVWlpKfn4+99xzD1arlfT0dBYuXMirr76qdWgDQkVFBaNGjeK2227DZDIRHx/Pd77zHTZs2MDHH39MXFwc1113HQaDgZkzZ3LppZfK2PfQunXr+Pjjj7ngggsCl8lYB8eOHTvYtm0bjz/+ODExMURFRfHwww9z9913y5j3ssbGRqqrq/F6vajtXXV0Oh1Wq3XAj7UkQiKoCgsLiYuLIyUlJXBZVlYWFRUVNDU1aRjZwDBixAief/559Hp94LKPPvqIsWPHUlhYSG5ubofbZ2dnU1BQEOowB4za2lruu+8+nnzyyQ6HPctYB8fXX39NdnY2S5cu5fzzz+fMM8/kd7/7HYMGDZIx72Xx8fHcdNNN/O53v+O0007j7LPPJjMzk5tuumnAj7UkQiKoWltbO/zCAALf22w2LUIasFRV5U9/+hOrVq3ivvvuO+7YWywWGfdu8nq93HPPPXzve99j1KhRHa6TsQ6OxsZG9uzZQ0lJCcuWLeOdd96hqqqKX/ziFzLmvczr9WKxWPj1r3/N1q1bef/99ykuLuapp54a8GMtiZAIqoiICNra2jpc5v8+MjJSi5AGpJaWFu644w7ee+89XnnlFUaOHInVasVut3e4nd1ul3HvpmeffRaTycQNN9xwzHUy1sFhMpkAuO+++4iKiiIpKYlFixaxevVqVFWVMe9Fn3zyCR999BHXXnstJpOJnJwcbrvtNl5//fUB//6WREgEVU5ODg0NDdTU1AQuKy4uJjU1lejoaA0jGzgOHDjAFVdcQUtLC2+99RYjR44EIDc3l8LCwg63LSoqIicnR4sw+713332X/Px88vLyyMvL4/333+f9998nLy/v/9u7n5Ao+jiO45913chDQobipZAUpJNhK4J/CnbLi+ufdIkoRaiDtoQgesqDsFRMRG4gVGCuXgS9bh3Ck6CI4aJFCNEhNLd/JompB1mdnsNDC/scwifcZ5/deb9u85uZ/X1/P+bw4TczO8x1ghQVFck0TUWj0VibaZqSpFOnTjHnB+jz58+xN8R+yczMlMPhSPvrmyCEhCooKNCZM2d09+5dbW1taWVlRY8ePZLX6012aWlhY2NDbW1tKi0t1dDQkHJycmL7Lly4oLW1NY2MjCgajWp2dlbPnj1Tc3NzEitOXS9evND8/LzC4bDC4bA8Ho88Ho/C4TBznSAVFRU6fvy4bt26pe3tbX3//l2BQEDnz5+Xx+Nhzg9QVVWVvn37pidPnmhvb08rKyt6/Pix6urq0v765qOrSLi1tTX5/X69fPlSGRkZamxsVE9PT9wDvvgzw8PDMgxDWVlZstlscfsWFhb05s0b3blzR+/evVNOTo58Pp+ampqSVG16+fUfQoZhSBJznSBfv36VYRiam5vTzs6OXC6Xent7lZ2dzZwfsJmZGT18+FDv37/XkSNHVF9fH3sjNZ3nmiAEAAAsi1tjAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAFLOzs6Ovnz5kuwyAKQBghCAlHPlyhXNzMwkuwwAaYAgBCDlrK+vJ7sEAGmCIAQgpVy7dk2fPn1SX1+f/H6/FhcX1draqrKyMtXU1GhkZES/vhw0MDCgzs5O9fT0yOl06uzZs3rw4EHst1pbWzUwMBDbjkQiKi4uViQSkSQVFxfr9u3bKi8vV0dHh6S/v8fk9XrldDpVW1urUCj0H44ewEHLTHYBAPBvBINBuVwu3bx5U5WVlaqtrVVXV5eCwaCWl5fl8/l0+PBhXb58WZI0MTEhwzB07949TU9Pq729XW63W6dPn95Xfx8+fNDk5KSi0ajevn2rGzdu6P79+3K73Xr9+rV8Pp+OHj2q6urqBI4aQKKwIgQgZYVCIRUWFurq1atyOBwqKirS9evXNTo6GjumoKBAjY2NstvtOnfunHJzc7W0tLTvPjwej7KyspSdna2xsTG53W7V1NTIbrertLRUly5diusPQGphRQhAyvr48aMWFxfldDpjbaZpym63x7Zzc3PjznE4HDJNc9995OXlxfU3Ozsb19/e3p5OnDjxJ+UD+B8gCAFIWfn5+SovL9fQ0FCsbX19Xdvb2/s6PyMjQ9FoNO7cf7LZbHH9Xbx4UX6/P9a2uroaeyYJQOrh1hiAlHPo0CFtbm6qrq5Or169UigU0u7urlZXV9XR0SHDMPb1O4WFhZqamtKPHz+0ubmpwcHB3x7v9Xr1/PlzTU9PyzRNLS0tqaWlRcFg8CCGBSAJCEIAUo7X61UgEFAgENDTp081Pj6uiooKNTQ06OTJk/sOQu3t7Tp27JjcbrcaGhrkcrl+e3xJSYn6+/vV39+vsrIytbS0yOVyqbu7+yCGBSAJbD9Z0wUAABbFihAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALCsvwChsQZOQjStswAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kdeplot : 히스토그램을 곡선으로 그려보자\n",
    "# 처음엔 서비스 가입도 많이 하고 이탈도 많이 하는것으로 보이고\n",
    "# 오래동안 서비스를 사용할수록 이탈이 적으며, 70개월 이상 충성고객이 많이 있음을 알수 있다.\n",
    "\n",
    "sns.kdeplot(data=df, x='tenure', hue='Churn')\n",
    "# sns.kdeplot(data=df, x='TotalCharges', hue='Churn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SfYkzDiOzt6Q"
   },
   "source": [
    "#### TotalCharges 컬럼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "XBH2Gxwxzt6Q",
    "outputId": "32dd2c8c-caad-4ddd-c1cc-5964bf634157"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='TotalCharges', ylabel='Count'>"
     },
     "execution_count": 93,
     "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": [
    "# TotalCharges (서비스 총요금)에 대한 히스토그램\n",
    "# 처음에 많이 사용하고 금액이 커질수록 사용자수가 줄어든다\n",
    "\n",
    "sns.histplot(data=df, x='TotalCharges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "j1a0QRevzt6Q",
    "outputId": "a7d58f12-adad-4466-a868-16d3bff1756f"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2fb178750>"
      ]
     },
     "metadata": {},
     "execution_count": 51
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# kdeplot : 히스토그램을 곡선으로 그려보자\n",
    "# 최근 가입자가 이탈하기 쉽다.\n",
    "# MonthlyCharges(서비스 총요금)이 클수록 이탈하기 쉽다.\n",
    "\n",
    "sns.kdeplot(data=df, x='TotalCharges', hue='Churn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ASDnxztzt6Q"
   },
   "source": [
    "####  \n",
    "### Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "-44H6Dunzt6Q",
    "outputId": "b515c877-06e6-4870-be11-0a9f46aef859"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='MultipleLines', ylabel='count'>"
     },
     "execution_count": 96,
     "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": [
    "# MultipleLines 서비스를 사용하는 고객이 약간 더 높은 이탈율을 보인다.\n",
    "# sns.countplot(data=df, x='MultipleLines',  hue='Churn')\n",
    "sns.countplot(data=df, x='MultipleLines', )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y8_NKeymzt6Q"
   },
   "source": [
    "####  \n",
    "### heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "BotYj0ECzt6R",
    "outputId": "530efbf6-c0e3-4d2c-cbdd-cdf28c30cc83"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                  tenure  MonthlyCharges  TotalCharges\ntenure          1.000000        0.247630      0.826172\nMonthlyCharges  0.247630        1.000000      0.651049\nTotalCharges    0.826172        0.651049      1.000000",
      "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>tenure</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>tenure</th>\n      <td>1.000000</td>\n      <td>0.247630</td>\n      <td>0.826172</td>\n    </tr>\n    <tr>\n      <th>MonthlyCharges</th>\n      <td>0.247630</td>\n      <td>1.000000</td>\n      <td>0.651049</td>\n    </tr>\n    <tr>\n      <th>TotalCharges</th>\n      <td>0.826172</td>\n      <td>0.651049</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'tenure','MonthlyCharges','TotalCharges' 컬럼간의 상관관계를 확인해 보자 .corr()\n",
    "df[['tenure','MonthlyCharges','TotalCharges']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "QyJT99spzt6R",
    "outputId": "c5ca3de8-0d42-4fb0-86fc-b7d39a41ca27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tenure','MonthlyCharges','TotalCharges' 컬럼간의 상관관계를 heatmap으로 그려보자\n",
    "# tenure(서비스 사용기간)과 TotalCharges(서비스 총요금)간의 깊은 상관관계가 있어 보인다.\n",
    "\n",
    "sns.heatmap(df[['tenure','MonthlyCharges','TotalCharges',]].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FwM1bubDzt6R"
   },
   "source": [
    "####  \n",
    "### boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "tAPwIMtrzt6R",
    "outputId": "2cdb955a-a896-4fc9-e063-8f49bc05163e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Churn', ylabel='TotalCharges'>"
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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na96Ije3g9XPQurhc37/9Y2PbN3hvAWh9fvGLFC1Z8sp566GhAf/oQxAJ+Kvftm1bSdK0adMUHh6u9u3b69FHH9Vdd92l5ORkud3ueo93u92KjIyUJEVERDRaj46OrvvQOjvfp7Hne6O0tEwej9dPQyty7nuptLRcDke1id0A8IXCwv3nrf/tb3sUH98jQN0gkAyjeYMWAQ8+CQkJqq2tVVVVlcLDwyVJtbW1kqRLL71U//M//1Pv8QUFBerVq5ckqVevXtq7d2+D+nXXXaeOHTuqc+fOKigoqDvdVVxcrBMnTjQ4fdYcHo8IPhZ37uvL6w1YQ3V1TZN1jnV7C/jk5iFDhig+Pl7PPPOMKioqdOzYMb3wwgsaMWKEbr/9dpWUlCg7O1tVVVXasmWL1qxZUzevJyUlRWvWrNGWLVtUVVWl7OxslZaWKikpSZKUnJyszMxMFRYWqry8XHPmzNHAgQPVowfpHgDs4Msvd7WoDusL+IhPWFiYli5dqnnz5umWW25RZWWlhg8frmnTpumiiy7SG2+8oYyMDC1atEgxMTGaPn26rrnmGknS4MGDNXPmTM2aNUtHjhxRQkKCsrKyFBUVJUmaPHmyqqurlZqaqoqKCg0aNEgLFy4M9C4CAEwSG+tsUR3WZ3g8DPo1pqSEOT5W53a7NX78OEnS0qU5cjgcJncEoKVqa2v1y1+OU1XV6Qa1tm3baunSHFZotyjDkJzOpuf48OoDACzDMIwfHNWJiXHK4Fp22yP4AAAso7Bwvw4fLmq0dvhwUZNXfcH6CD4AAMs4dKjx0NPcOqyP4AMAsIySkuIW1WF9BB8AgGU0NXGZic3gHQAAsIwRI25pUR3Wx4IlAIB6PB6PKisrzW7jguzYsf289W3btioxsX+AuvGN8PBwrkbzIYIPAKCOx+PRjBlPas+eL81uxS9++9t5ZrfgtT59LlV6+nzCj49wqgsAANgGIz4AgDqGYSg9fX6rPdVVU1OjX/86VTU1DRcrDQkJ0ZIlyxUSEmJCZxeOU12+RfABANRjGEarXsLloYce1ssvL2ywPS3tN4qMjAx8QwgqnOoCAFjKDTfcpJiY2HrbYmOduv764SZ1hGBC8AEAWM6MGen1vk9Pn29SJwg2BB8AgOU4nZ3qvh44cLA6dbrYxG4QTAg+AABLe/jhfze7BQQRgg8AALANgg8AALANgg8AALANgg8AALANgg8AALCNCwo+X3zxhSTp22+/1YIFC7RkyRJVV1f7tDEAAABf83rJiszMTL3++uvavn27Zs+erS+++EJt2rTR4cOHNW3aNH/0CAAA4BNej/isXbtWy5cv1+nTp/XRRx/pt7/9rd58802tW7fOH/0BAAD4jNcjPkePHlXfvn21efNmdejQQX379pUkuVwunzcHAADgS16P+HTu3Fl5eXl67733NHjwYElnRoHi4+N93hwAAIAveT3i8/DDD+v++++Xw+HQihUrtHnzZj399NP63e9+54/+AAAAfMbr4HPLLbfohhtukCSFh4fr4osv1p/+9CddfDELwAEAgOB2QZezV1RUaMWKFcrIyJAk7dq1y6dNAQAA+IPXwWfXrl36+c9/rg8//FArV67U8ePH9cgjj2jVqlX+6A8AAMBnvA4+c+fO1VNPPaW33npLoaGhio+P18svv6wlS5b4oz8AAACf8Tr4fPXVV7rjjjskSYZhSJKGDRumI0eO+LYzAAAAH/M6+MTExGjfvn31tu3bt09Op9NnTQEAAPiD18Hn3/7t3/Tggw/qnXfeUXV1tdatW6dHHnlEd999tz/6AwAA8BmvL2f/1a9+pZCQEL355puqra3Viy++qLvvvlsTJkzwQ3sAAAC+43XwkaTU1FSlpqb6uhcAAAC/8jr4PP30041uDwsLU0xMjG644Qb169evpX0BAAD4nNdzfMLCwrRmzRq5XC45nU6dPn1aa9eu1ZEjR7Rv3z7de++9rNQOAACCktcjPocOHdLChQs1YsSIum1//vOftWLFCi1atEi5ubmaPXu2brvtNp82CgAA0FJej/jk5+dr+PDh9bYNGzZM27ZtkyQNGjRIBw8e9E13AAAAPnRB9/HZuHFjvW2bN29WVFSUJKmwsFAdO3b0SXMAAAC+5PWprocfflhTpkzRzTffrO7du+vgwYNav369Zs2apX379umee+7RL3/5S3/0CgAA0CJeB5+RI0eqW7duevfdd7Vr1y5169ZNy5Yt089+9jP985//1MyZM+vN/wEAAAgWXgeftLQ0LViwQOnp6Q1qPXv2VM+ePX3RFwAAgM95Pcdnx44datu2rT96AQAA8CuvR3xuv/12/eY3v9GoUaPUqVOnuhXaJenqq6/2aXMAAAC+5HXwWbZsmSTp//7v/+ptNwxDX375pU+aAgAA8Aevg8/u3bv90QcAAIDfXdAipS6XSydPnlRtba0kqaqqSl999ZWSkpJ82hwAAIAveR18Vq1apfT0dFVWVtbbHhsbS/ABAABBzevg88orr+jRRx9VZGSk8vLydM8992jBggW69tpr/dEfAACAz3h9OXtxcbHuueceDR48WPv379dll12mOXPmKCcnxx/9AQAA+IzXwSc2NlZVVVXq2rWr/vGPf0iSunXrptLSUp83BwAA4EteB58rrrhCzz77rNxut3r27KkVK1Zo9erVdYuUAgAABCuv5/g8/fTTmj59uioqKvT444/roYcektvt1ty5c/3RHwAAgM94HXwuvvhivfbaa3Vfb9myRVVVVYqIiPB5cwAAAL50Qffxyc/P1/79+1VTU1Nv+5gxY3zREwAAgF94HXxeeOEFvfbaa3I6nQoLC6vbbhgGwQcAAAQ1r4PPO++8o+zsbA0aNMgf/QAAAPiN11d1hYSEEHoAAECr5PWIz4033qi1a9fq9ttv90c/lubxeBos9QHzuN3uRr+GucLDw2UYhtltALCoZgef8ePHyzAMVVRUaNWqVXrttdca3Lvn97//va/7s5TKykqNHz/O7DbQiIkTx5vdAr6zdGmOHA6H2W0AsKhmB59zT2/deOONfmkGAADAn5odfKZMmeLPPmynvN8v5GlzQXcTgC95PGf+l1MrpjJqq9X+ryvMbgOADXj1ybthwwYVFBRo4sSJks7Mi0hOTtbUqVM1fPhwvzRoVZ42oVJIWNMPBGzAY3YDAGyj2Vd1bdmyRY899phCQkLqttXU1GjYsGF69NFHtXXrVr80CAAA4CvNHvF59dVXNW3aNI0b9/3k3MjISD399NPq2rWrXn31VQ0cONAvTQIAAPhCs0d8du/e/YN3Zh43bpx27drlq54AAAD8otnBp7q6ut5prnO1a9euwbpdAAAAwabZwadnz57Kz89vtPbXv/5VXbp08fqX19TUaPz48XrqqafqtuXn52vcuHFKTEzU8OHDlZOTU+85q1evVlJSkvr166fk5GTt2LGj3s+bP3++hgwZosTERKWlpeno0aNe9wUAAKyp2cHnzjvv1MyZM3XkyJF6248cOaLnnntOI0eO9PqXv/TSS9q2bVvd9ydPntQDDzygMWPGKC8vTxkZGZo7d6527twpScrNzVV6errmzZunvLw8jR49WmlpaXK5XJKkzMxMbdq0SatWrdLGjRvlcDg0ffp0r/sCAADW1OzJzb/4xS+0adMm3XzzzbrqqqvkdDpVXFysHTt2aNCgQbr//vu9+sWbN2/Wxx9/rJtvvrlu28cff6yoqCilpqZKkgYPHqxRo0Zp+fLluuKKK5STk6ORI0eqf//+kqQJEybo7bff1rp16zR27Fjl5ORo6tSp6tq1qyRp2rRpGjp0qAoLCxUfH+9Vf/64rQu3igGaZhgcK2i5c99DvKfsobmvcbODj2EYevnll/XBBx9ow4YNOnbsmLp27arU1FQlJSV51VxpaammTZumxYsXKzs7u2773r171bt373qPTUhI0MqVKyVJBQUFGjt2bIP67t27VVZWpsOHD9d7vtPpVMeOHbVnzx6vg09sbAevHt8cLhc3LASaEhvbXhEREWa3gVbu3L+3vKdwLq8/iW+99VbdeuutF/wLa2tr9fjjj+vee+9V375969UqKioavDkdDodOnTrVZL2iokLSmYnW/1o/W/NGaWlZ3U19fYWFMIGmlZaWy+GoNrsNtHLn/r3lPWUPhtG8QYtmB5+XXnqpycc0Z1mLV199VW3bttX48Q0XhYyIiFBZWVm9bW63W5GRkXX1fw0Pbrdb0dHRdYHo7Hyfxp7vDY9HPg8+vv55gBX549iD/Zz7HuI9hXM1O/jk5uaet2408+TaH/7wBx09elQDBgyQ9H0qX79+vZ544glt2rSp3uMLCgrUq1cvSVKvXr20d+/eBvXrrrtOHTt2VOfOnVVQUFB3uqu4uFgnTpxocPoMAADYU7ODz9KlS33yCz/88MN635+9lH3evHk6fvy4FixYoOzsbKWmpmr79u1as2aNFi9eLElKSUnR5MmTdeutt6p///5avny5SktL6+YYJScnKzMzU5dffrmio6M1Z84cDRw4UD169PBJ7wAAoHW7oNm2W7Zs0ZEjR+T5buywqqpKe/bsafGl49HR0XrjjTeUkZGhRYsWKSYmRtOnT9c111wj6cxVXjNnztSsWbN05MgRJSQkKCsrS1FRUZKkyZMnq7q6WqmpqaqoqNCgQYO0cOHCFvUEAACsw/B4vDvzOXv2bL311lt182ZqampUUVGhYcOG6bXXXvNLk2YoKfHP5Obx48+sdVZ21XhWZwfOqqlSh8/PjCovXZojh8NhckNo7c79e8t7yh4MQ3I6fTi5+awPPvhAy5Ytk8vl0vvvv685c+Zo/vz5dVdeAQAABCuvg4/L5VK/fv1UXFysXbt2yTAMTZkyRbfddps/+gMAAPCZZi9ZcVaXLl1UWlqqTp066fDhw6qqqpLD4VB5ebk/+gMAAPAZr0d8rr/+ek2YMEFvvvmmrr76aj3zzDMKDw9Xz549/dAeAACA73g94vPv//7vuuOOOxQWFqZnn31Wx48fV0FBgWbPnu2P/gAAAHzG6xGf9evX1y1I2qFDB73++uuSpLfffls//elPfdsdAACADzUr+LhcLh0/flyS9Mwzz6hfv3469yr4srIyzZs3T3fffbd/ugQAAPCBZgWf8vJyjRw5sm55ieHDh9fVPB6PDMPQiBEj/NMhAACAjzQr+HTq1Enr16+Xy+XSqFGjtHbt2nr18PBwOZ1OvzQIAADgK82e4xMbGytJ2r59u9q0aaPS0lIdPHhQnTp1IvQAAIBWwevJzadOndITTzyh//3f/5V0ZlX2wYMHa+HChbrooot83iAAAICveH05+/PPP69Tp07pj3/8o/Lz8/WHP/xBtbW1WrBggT/6AwAA8Bmvg8+GDRv0/PPP65JLLlF4eLh69+6tBQsWaP369f7oDwAAwGe8Dj4ul0sdOtRf/fSiiy5SbW2tz5oCAADwh2YHn+3bt0uSrrzySr344ot19/HxeDx68cUXdfnll/unQwAAAB9p9uTmiRMn6vPPP9d//Md/6Fe/+pXef/99xcXF6eDBgzIMQ//93//tzz4BAABarNnB5+wIT58+ffTRRx9p/fr1OnbsmOLi4nT99derffv2fmsSAADAF5odfAzDqPs6KipKKSkpfmkIAADAX5odfFwul2666abzPuZPf/pTixsCAADwl2YHn7CwME2ZMsWfvQAAAPhVs4NPaGio7rzzTn/2AgAA4FfNvpz97ORmAACA1qrZwWf06NH+7AMAAMDvmh18nnvuOX/2AQAA4HdeL1kBAADQWhF8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbYSa3QAAWIHH41FlZaXZbeA7bre70a9hrvDwcBmGYWoPBB8A8IHKykqNHz/O7DbQiIkTx5vdAr6zdGmOHA6HqT1wqgsAANgGIz4A4GMvDT2m8BCP2W3Ynue7l8DkMyu2V1ljaMpnMWa3UYfgAwA+Fh7iUXiI2V0AwSK4/iOAU10AAMA2CD4AAMA2CD4AAMA2CD4AAMA2CD4AAMA2CD4AAMA2TAk+u3fv1r333quBAwfq2muv1RNPPKFjx45JkvLz8zVu3DglJiZq+PDhysnJqffc1atXKykpSf369VNycrJ27NhRV6upqdH8+fM1ZMgQJSYmKi0tTUePHg3ovgEAgOAV8ODjdrt1//33KzExUZ999pnWrl2rEydO6JlnntHJkyf1wAMPaMyYMcrLy1NGRobmzp2rnTt3SpJyc3OVnp6uefPmKS8vT6NHj1ZaWppcLpckKTMzU5s2bdKqVau0ceNGORwOTZ8+PdC7CAAAglTAg09RUZH69u2ryZMnq23btoqOjtbdd9+tvLw8ffzxx4qKilJqaqpCQ0M1ePBgjRo1SsuXL5ck5eTkaOTIkerfv7/CwsI0YcIERUdHa926dXX1iRMnqmvXrmrfvr2mTZumTz/9VIWFhYHeTQAAEIQCHnx+8pOf6PXXX1dIyPe3Nf3oo4902WWXae/everdu3e9xyckJGj37t2SpIKCgh+sl5WV6fDhw/XqTqdTHTt21J49e7zu0zD88w/A+fnr2AvEPwDnFwzHoKlLVng8Hi1cuFAbNmzQsmXL9Pvf/14RERH1HuNwOHTq1ClJUkVFxQ/WKyoqJEnt2rVrUD9b80ZsbAevn9MUl4sVQoCmxMa2b3CctwYc30DTguH4Nu1ILS8v19NPP61du3Zp2bJl6tOnjyIiIlRWVlbvcW63W5GRkZKkiIgIud3uBvXo6Oi6/yPPzvdp7PneKC0tq1vgzlf+tXcADZWWlsvhqDa7Da9xfANN8+fxbRjNG7Qw5aqu/fv3a+zYsSovL9fKlSvVp08fSVLv3r21d+/eeo8tKChQr169JEm9evX6wXrHjh3VuXNnFRQU1NWKi4t14sSJBqfHmsPj8c8/AOfnr2MvEP8AnF8wHIMBDz4nT57UPffco6uuukpLlixRTMz3S9UnJSWppKRE2dnZqqqq0pYtW7RmzRqNHTtWkpSSkqI1a9Zoy5YtqqqqUnZ2tkpLS5WUlCRJSk5OVmZmpgoLC1VeXq45c+Zo4MCB6tGjR6B3EwAABKGAn+p69913VVRUpA8++EAffvhhvdqOHTv0xhtvKCMjQ4sWLVJMTIymT5+ua665RpI0ePBgzZw5U7NmzdKRI0eUkJCgrKwsRUVFSZImT56s6upqpaamqqKiQoMGDdLChQsDvIcAACBYGR4PA7SNKSnxzxyf8ePHSZLKrhovhYT59hcArVVNlTp8vlSStHRpjhwOh8kNee/c4zvr+lKFhzTxBMAmKmukiX+OleTf49swJKczSOf4AAAAmIHgAwAAbIPgAwAAbIPgAwAAbINbjZqlpsrsDoDgwfEAIEAIPgF07gV0HfLfMrETIHhxoSkAf+JUFwAAsA1GfALIOGfp2LIr/x/38QHOqqmqGwU1WOYcgB8RfMwSEkbwAQAgwDjVBQAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbCPU7AYAwGoqa8zuAAgewXY8EHwAwAc8Hk/d11M+izWxEyB4nXucmIVTXQAAwDYY8QEAHzAMo+7rl4aWKjzExGaAIFJZ8/0o6LnHiVkIPgDgY+EhIvgAQYrgYxKjtlrmn+mEzp5vDoL/CrEzo7ba7BYA2ATBxyTt/7rC7BYAALAdJjcDAADbYMQngMLDw7V0aY7ZbeA7brdbEyeOlyRlZS2Vw+EwuSNIZ44TAPAXgk8AGYbBh2uQcjgcvDYAYAOc6gIAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALZB8AEAALbBIqUA4GOVNYYkj9lt2J7nu5fAMMztw+7OHA/Bg+ADAD425bMYs1sA8AM41QUAAGzDkiM+paWlmjFjhrZu3aqQkBCNHj1aTz75pEJDLbm7AIJAeHi4li7NMbsNfMftdmvixPGSpKyspXI4HCZ3BOnMcWI2SyaBRx99VJ07d9bGjRtVUlKitLQ0ZWdn6/777ze7NQAWZRiGZT5cPR6PKisrzW4D3wkPD5fBRCWfsVzw+eabb7R161Z9+umnioiIUHx8vCZNmqQFCxYQfHzICn8Y3W53o1+3RvxhhK94PB7NmPGk9uz50uxWfObsyE9r1afPpUpPn88x7iOWCz579+5VVFSUOnfuXLftkksuUVFRkb799ltddNFFzfo5vL9+GH8Yg0/fvvxhhO/wNgouhvH9P/yw5v7/Y7ngU1FRoYiIiHrbzn5/6tSpZgef2NgOPu/NKjwej8LCQsxuA+cIDQ2R09mB4AOfyMxc3OpHQaUzf6sktfrjwuFwtPp9CCaWCz7t2rWTy+Wqt+3s95GRkc3+OaWlZXX3gEBDM2fOafWnuiTr/GEMDw9XaWm52W0A8IOKCo7t5jCM5g1aWC749OrVSydOnFBJSYmcTqck6euvv1aXLl3UoUPzR3E8HhF8zstQeLg1JnJaBe9XAGia5e7j07NnT/Xv319z5sxReXm5CgsLtXjxYqWkpJjdGgAAMJnlgo8kLVq0SNXV1brpppt01113adiwYZo0aZLZbQEAAJMZHg8D5I0pKWGODwAArYVhSE5n01NaLDniAwAA0BiCDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA2CDwAAsA3Lrc7uK4ZhdgcAAKC5mvu5zVpdAADANjjVBQAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgAwAAbIPgA1sqLS3VpEmTNGDAAA0aNEgZGRmqrq42uy0APnTs2DElJSUpNzfX7FYQRAg+sKVHH31U7dq108aNG7Vy5Upt3rxZ2dnZZrcFwEe2b9+uu+++W/v37ze7FQQZgg9s55tvvtHWrVv1+OOPKyIiQvHx8Zo0aZKWL19udmsAfGD16tWaOnWqHnvsMbNbQRAi+MB29u7dq6ioKHXu3Llu2yWXXKKioiJ9++23JnYGwBeGDh2qTz75RLfddpvZrSAIEXxgOxUVFYqIiKi37ez3p06dMqMlAD7UqVMnhYaGmt0GghTBB7bTrl07uVyuetvOfh8ZGWlGSwCAACH4wHZ69eqlEydOqKSkpG7b119/rS5duqhDhw4mdgYA8DeCD2ynZ8+e6t+/v+bMmaPy8nIVFhZq8eLFSklJMbs1AICfEXxgS4sWLVJ1dbVuuukm3XXXXRo2bJgmTZpkdlsAAD8zPB6Px+wmAAAAAoERHwAAYBsEHwAAYBsEHwAAYBsEHwAAYBsEHwAAYBsEHwAAYBsEHwAAYBsEHwAAYBsEHwCtxj/+8Q89+eSTuu6665SYmKgRI0bov/7rv1RRUSFJ6tOnj3Jzc03uEkAwI/gAaBU+//xz3XnnnYqLi9N7772nHTt2KCsrS/n5+brvvvtUU1NjdosAWgGCD4BW4dlnn9WYMWP0m9/8RjExMZKkH//4x3rhhRcUGxurwsJCSdKmTZt0xx13KDExUSkpKfrqq68kSbm5uerTp0+9n/nUU0/pqaeekiT97ne/03333aexY8dq4MCBysvL0/Dhw/Xqq69qzJgxSkxM1JgxY7Rly5YA7jUAXyP4AAh6+/fv1969e3X77bc3qDmdTi1evFg9e/aUJG3dulVLlizR5s2bFR0drfnz5zf792zevFlTp07Vhg0blJiYKElatWqVXnzxRf3lL39R3759NWvWLF/sEgCTEHwABL1jx45JOhNymnLvvffK6XTK4XBoxIgR2r9/f7N/T3x8vAYPHqzIyEiFhoZKklJSUvSjH/1IERERGjVqlP75z39e0D4ACA4EHwBBr1OnTpKk4uLiRuslJSV1X0dFRdV9HRYW5tXcn4svvrjBtnPDVmhoqDweT7N/HoDgQ/ABEPTi4uLUu3dvrVu3rkGttLRUN954o9auXXvenxESEiJJOn36dN2248eP13uMYRg+6BZAMCP4AGgVZsyYoVWrVumll17S8ePH5fF49OWXX+qhhx7SZZddpltuueW8z+/Ro4dCQ0P1xz/+UZL0l7/8hYnKgA2Fmt0AADTHwIEDtWzZMr3yyisaOXKkXC6XnE6nfv7zn+vBBx9UWFjYeZ9/8cUX65lnntHixYuVnp6ua665RsnJyXK5XAHaAwDBwPBwwhoAANgEp7oAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBt/H/b4LAFJ0EPfAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이탈하는 고객이 이탈하지 않는 고객에 비해 총사용금액이 낮으며, Outlier 보인다.\n",
    "sns.boxplot(data=df, x='Churn', y='TotalCharges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Ec61f24zt6S"
   },
   "source": [
    "#  \n",
    "# 5. 결과 저장하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DhIQgEEBzt6S"
   },
   "source": [
    "## 결과를 csv 파일로 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "hUhvHzC8zt6S"
   },
   "outputs": [],
   "source": [
    "# index=False 주어야 기존 인덱스 값이 저장되지 않음\n",
    "df.to_csv('data_v1_save.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "DEt2b1N7zt6S",
    "outputId": "205721dc-b4f9-4aff-efd4-0b6e106e9326"
   },
   "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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data_v1_save.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SYJwVbbxzt6S"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GePgMkKbzt6S"
   },
   "source": [
    "##  \n",
    "## 배운 내용 정리\n",
    "1. 필요 라이브러리 임포트 및 파일 읽어오기 : pd.read_csv()\n",
    "2. EDA (Exploratory Data Analysis) 탐색적 데이터 분석 : df.info(), df.head(), df.tail()\n",
    "3. 데이터 전처리 수행\n",
    " + 불필요 컬럼 삭제 : df.drop()\n",
    " + 컬럼 내용 변경하기 : df.replace()\n",
    " + Null 처리 : df.replace(), df.fillna()\n",
    " + 컬럼 type 변경하기 : df['col'].astype(int)\n",
    "4. 시각화\n",
    " + matplotlib, seaborn\n",
    " + bar, scatter, countplot, boxplot \n",
    "5. 결과 저장하기\n",
    " + to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hbihEcMmzt6T"
   },
   "outputs": [],
   "source": []
  }
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