한재원_개별 미션 (6주차 2일).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34b233a6",
   "metadata": {},
   "source": [
    "### 1. 18_Charge_data.csv 와 18_Customer_data.csv를 불러와 각각 df_charge, df_customer로 선언 후, 데이터의 구조와 타입확인\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "b628a562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 291334 entries, 0 to 291333\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count   Dtype \n",
      "---  ------      --------------   ----- \n",
      " 0   Unnamed: 0  291334 non-null  int64 \n",
      " 1   고객ID        291334 non-null  object\n",
      " 2   과금일         291334 non-null  object\n",
      " 3   과금액         291334 non-null  int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 8.9+ MB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6863 entries, 0 to 6862\n",
      "Data columns (total 26 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  6863 non-null   int64 \n",
      " 1   고객ID        6863 non-null   object\n",
      " 2   성별          6863 non-null   object\n",
      " 3   연령          6863 non-null   int64 \n",
      " 4   결혼여부        6863 non-null   object\n",
      " 5   부양자유무       6863 non-null   object\n",
      " 6   부양자수        6863 non-null   int64 \n",
      " 7   추천횟수        6863 non-null   int64 \n",
      " 8   영수증발급여부     6863 non-null   object\n",
      " 9   과금방식        6863 non-null   object\n",
      " 10  보안서비스       6863 non-null   object\n",
      " 11  백업서비스       6863 non-null   object\n",
      " 12  기술지원서비스     6863 non-null   object\n",
      " 13  데이터무제한      6863 non-null   object\n",
      " 14  데이터사용량      6863 non-null   int64 \n",
      " 15  시작일         6863 non-null   object\n",
      " 16  종료일         6863 non-null   object\n",
      " 17  고객이탈여부      6863 non-null   object\n",
      " 18  이탈가능점수      6863 non-null   int64 \n",
      " 19  이탈유형        1702 non-null   object\n",
      " 20  이탈사유        1702 non-null   object\n",
      " 21  LTV         6863 non-null   int64 \n",
      " 22  만족도         6863 non-null   int64 \n",
      " 23  데이터사용료      6863 non-null   int64 \n",
      " 24  로밍사용료       6863 non-null   int64 \n",
      " 25  총로밍요금       6863 non-null   int64 \n",
      "dtypes: int64(11), object(15)\n",
      "memory usage: 1.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_charge= pd.read_csv(r\"C:\\Users\\hanjw\\Desktop\\python\\실습파일\\18_Charge_data.csv\", encoding='cp949')\n",
    "df_customer= pd.read_csv(r\"C:\\Users\\hanjw\\Desktop\\python\\실습파일\\18_Customer_data.csv\", encoding='cp949')\n",
    "print(df_charge.info())\n",
    "print(df_customer.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec06e65",
   "metadata": {},
   "source": [
    "### 2. 매출에 대한 추세를 분석하고자 한다. 시간이 지남에 따라 고객들이 지불하는 총과금액과 평균과금액에 대한 추세를 시각화로 확인하시오.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "e03994da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>과금일</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>33668700</td>\n",
       "      <td>94575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>47767700</td>\n",
       "      <td>91860.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>57274900</td>\n",
       "      <td>91057.074722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>64441700</td>\n",
       "      <td>89751.671309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>72681700</td>\n",
       "      <td>89180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>438558700</td>\n",
       "      <td>74496.127060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>425799900</td>\n",
       "      <td>74090.812598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>416085400</td>\n",
       "      <td>73800.177368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>394237700</td>\n",
       "      <td>72966.444568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>382419300</td>\n",
       "      <td>72634.245014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          과금일        sum          mean\n",
       "0  2015-01-01   33668700  94575.000000\n",
       "1  2015-02-01   47767700  91860.961538\n",
       "2  2015-03-01   57274900  91057.074722\n",
       "3  2015-04-01   64441700  89751.671309\n",
       "4  2015-05-01   72681700  89180.000000\n",
       "..        ...        ...           ...\n",
       "79 2021-08-01  438558700  74496.127060\n",
       "80 2021-09-01  425799900  74090.812598\n",
       "81 2021-10-01  416085400  73800.177368\n",
       "82 2021-11-01  394237700  72966.444568\n",
       "83 2021-12-01  382419300  72634.245014\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df= df_charge.merge(df_customer, on=\"고객ID\")\n",
    "df['과금일'] =pd.to_datetime(df['과금일'])\n",
    "grouped_df = df.groupby('과금일')['과금액'].agg(['sum', 'mean']).reset_index()\n",
    "grouped_df\n",
    "\n",
    "# plt.figure(figsize=(12, 6))\n",
    "# sns.barplot(x='과금일', y='sum', data=grouped_df, hue='mean')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "28e5acfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='과금일', ylabel='sum'>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import koreanize_matplotlib\n",
    "sns.lineplot(data = grouped_df, x = '과금일',y='sum')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "20796c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='과금일', ylabel='mean'>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data = grouped_df, x = '과금일',y='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "b24355b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0_x', '고객ID', '과금일', '과금액', 'Unnamed: 0_y', '성별', '연령',\n",
       "       '결혼여부', '부양자유무', '부양자수', '추천횟수', '영수증발급여부', '과금방식', '보안서비스', '백업서비스',\n",
       "       '기술지원서비스', '데이터무제한', '데이터사용량', '시작일', '종료일', '고객이탈여부', '이탈가능점수', '이탈유형',\n",
       "       '이탈사유', 'LTV', '만족도', '데이터사용료', '로밍사용료', '총로밍요금'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12eeed27",
   "metadata": {},
   "source": [
    "### 3. 고객의 기본적인 특성(성별/연령대/결혼여부/부양자여부)와 서비스유지/이탈고객의 유의미한 차이가 있는지 확인하시오.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "480f46fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def age(x):\n",
    "    if x//10 < 2:\n",
    "        return '미성년'\n",
    "    elif x//10==2:\n",
    "        return \"20대\"\n",
    "    elif x//10==3:\n",
    "        return \"30대\"\n",
    "    elif x//10==4:\n",
    "        return \"40대\"\n",
    "    elif x//10==5:\n",
    "        return \"50대\"\n",
    "    elif x//10==6:\n",
    "        return \"60대\"\n",
    "    else:\n",
    "        return \"노년층\"\n",
    "    \n",
    "df['연령대']=df['연령'].apply(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "116e55cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "성별:카이제곱 통계량:47.48506380021811, p-value값:5.542463916444489e-12\n",
      "연령대:카이제곱 통계량:4276.48950843648, p-value값:0.0\n",
      "결혼여부:카이제곱 통계량:488.08552568429144, p-value값:3.718617996394689e-108\n",
      "부양자유무:카이제곱 통계량:9771.92338646142, p-value값:0.0\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "idx= ['성별','연령대','결혼여부','부양자유무']\n",
    "idx2= ['고객이탈여부']\n",
    "\n",
    "for i in idx:\n",
    "    p1= pd.crosstab(index= df['고객이탈여부'], columns= df[i])\n",
    "    p1\n",
    "    a, p, n, table=stats.chi2_contingency(p1)\n",
    "    print(f\"{i}:카이제곱 통계량:{a}, p-value값:{p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef37a12d",
   "metadata": {},
   "source": [
    "### 4. 고객의 정보를 입력했을 때, 해당 고객이 이탈할 확률에 대한 분류모델을 생성하고, 평가 한 뒤, 각 고객정보가 이탈 확률에 얼마나 영향을 주고 있는가를 확인하시오.\n",
    "    - 필수로 들어가야 할 정보 : 고객 기본정보 (성별 / 연령 등)\n",
    "    - 추가로 고려할 정보 : 고객의 서비스 가입 정보 (보안 / 기술지원 등), 만족도 등\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "72e8900e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>고객이탈여부</th>\n",
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       "      <td>No</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>291329</th>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>291332</th>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>291333</th>\n",
       "      <td>No</td>\n",
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      "text/plain": [
       "       고객이탈여부\n",
       "0          No\n",
       "1          No\n",
       "2          No\n",
       "3          No\n",
       "4          No\n",
       "...       ...\n",
       "291329     No\n",
       "291330     No\n",
       "291331     No\n",
       "291332     No\n",
       "291333     No\n",
       "\n",
       "[291334 rows x 1 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['고객이탈여부']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f1c00b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "d9f4d5a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8803443446741906\n",
      "0.8790755148741419\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder= LabelEncoder()\n",
    "df['성별']= encoder.fit_transform(df['성별']) \n",
    "df['보안서비스']= encoder.fit_transform(df['보안서비스']) \n",
    "df['기술지원서비스']= encoder.fit_transform(df['기술지원서비스']) \n",
    "data= df[['성별','연령','보안서비스','기술지원서비스','만족도']] # 성별 보안서비스 기술지원서비스 라벨 인코딩\n",
    "target= df['고객이탈여부']\n",
    "\n",
    "ss = StandardScaler()\n",
    "train_scaled = ss.fit_transform(train_x)\n",
    "test_scaled = ss.transform(test_x)\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(data, target)\n",
    "\n",
    "lg= LogisticRegression()\n",
    "lg.fit(train_scaled, train_y)\n",
    "print(lg.score(test_scaled, test_y))\n",
    "print(lg.score(train_scaled, train_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b734a73",
   "metadata": {},
   "source": [
    "### 5. 두 데이터를 활용해 한 명의 고객이 결제한 총 결제금액을 고객 ID 별로 정리하고, 한명의 고객 금액, 과금 빈도, 최근성등을 이용해, RFM 분석을 실시해 고객을 5등급으로 분류하시오.\n",
    "    - 금액은 0.5 / 빈도는 기간 내 전체 빈도로 0.3 / 최근성은 -0.2 가중치 부여\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9708c198",
   "metadata": {},
   "source": [
    "# reset_index 꼭 해줄 것!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "7913a717",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>고객ID</th>\n",
       "      <th>과금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C-10001</td>\n",
       "      <td>1755400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C-10002</td>\n",
       "      <td>1447500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C-10003</td>\n",
       "      <td>490900</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C-10004</td>\n",
       "      <td>1578400</td>\n",
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       "      <td>C-10005</td>\n",
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       "      <td>1610700</td>\n",
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       "    <tr>\n",
       "      <th>6859</th>\n",
       "      <td>C-17040</td>\n",
       "      <td>2343500</td>\n",
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       "    <tr>\n",
       "      <th>6860</th>\n",
       "      <td>C-17041</td>\n",
       "      <td>792400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6861</th>\n",
       "      <td>C-17042</td>\n",
       "      <td>6328500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6862</th>\n",
       "      <td>C-17043</td>\n",
       "      <td>5196100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6863 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         고객ID      과금액\n",
       "0     C-10001  1755400\n",
       "1     C-10002  1447500\n",
       "2     C-10003   490900\n",
       "3     C-10004  1578400\n",
       "4     C-10005   378000\n",
       "...       ...      ...\n",
       "6858  C-17039  1610700\n",
       "6859  C-17040  2343500\n",
       "6860  C-17041   792400\n",
       "6861  C-17042  6328500\n",
       "6862  C-17043  5196100\n",
       "\n",
       "[6863 rows x 2 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = max(df['과금일']) - df['과금일']\n",
    "df[\"Recency\"]=a.dt.days\n",
    "\n",
    "df['freq']=1\n",
    "p2=df.pivot_table(index='고객ID', values= 'freq', aggfunc = 'sum').reset_index()\n",
    "A=df[['고객ID','Recency']]\n",
    "\n",
    "p3=df.pivot_table(index='고객ID', values='과금액', aggfunc='sum').reset_index()\n",
    "p3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "85764c99",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2020-06-01</td>\n",
       "      <td>85700</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2205</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>47910</td>\n",
       "      <td>431100</td>\n",
       "      <td>30대</td>\n",
       "      <td>548</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>C-10001</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>83500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>431100</td>\n",
       "      <td>30대</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>C-10001</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>84600</td>\n",
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       "      <td>2205</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>47910</td>\n",
       "      <td>431100</td>\n",
       "      <td>30대</td>\n",
       "      <td>487</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291329</th>\n",
       "      <td>291329</td>\n",
       "      <td>C-17043</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>68300</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>5639</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30대</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291330</th>\n",
       "      <td>291330</td>\n",
       "      <td>C-17043</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>69500</td>\n",
       "      <td>6862</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5639</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30대</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291331</th>\n",
       "      <td>291331</td>\n",
       "      <td>C-17043</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>68300</td>\n",
       "      <td>6862</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>30대</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291332</th>\n",
       "      <td>291332</td>\n",
       "      <td>C-17043</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>69500</td>\n",
       "      <td>6862</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5639</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30대</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291333</th>\n",
       "      <td>291333</td>\n",
       "      <td>C-17043</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>69500</td>\n",
       "      <td>6862</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5639</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30대</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291334 rows × 32 columns</p>\n",
       "</div>"
      ],
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       "        Unnamed: 0_x     고객ID        과금일    과금액  Unnamed: 0_y  성별  연령 결혼여부  \\\n",
       "0                  0  C-10001 2020-04-01  84600             0   1  37  Yes   \n",
       "1                  1  C-10001 2020-05-01  83500             0   1  37  Yes   \n",
       "2                  2  C-10001 2020-06-01  85700             0   1  37  Yes   \n",
       "3                  3  C-10001 2020-07-01  83500             0   1  37  Yes   \n",
       "4                  4  C-10001 2020-08-01  84600             0   1  37  Yes   \n",
       "...              ...      ...        ...    ...           ...  ..  ..  ...   \n",
       "291329        291329  C-17043 2021-08-01  68300          6862   0  36  Yes   \n",
       "291330        291330  C-17043 2021-09-01  69500          6862   0  36  Yes   \n",
       "291331        291331  C-17043 2021-10-01  68300          6862   0  36  Yes   \n",
       "291332        291332  C-17043 2021-11-01  69500          6862   0  36  Yes   \n",
       "291333        291333  C-17043 2021-12-01  69500          6862   0  36  Yes   \n",
       "\n",
       "       부양자유무  부양자수  ...  이탈유형 이탈사유   LTV  만족도 데이터사용료  로밍사용료   총로밍요금  연령대  \\\n",
       "0         No     0  ...   NaN  NaN  2205    3      0  47910  431100  30대   \n",
       "1         No     0  ...   NaN  NaN  2205    3      0  47910  431100  30대   \n",
       "2         No     0  ...   NaN  NaN  2205    3      0  47910  431100  30대   \n",
       "3         No     0  ...   NaN  NaN  2205    3      0  47910  431100  30대   \n",
       "4         No     0  ...   NaN  NaN  2205    3      0  47910  431100  30대   \n",
       "...      ...   ...  ...   ...  ...   ...  ...    ...    ...     ...  ...   \n",
       "291329    No     0  ...   NaN  NaN  5639    3      0      0       0  30대   \n",
       "291330    No     0  ...   NaN  NaN  5639    3      0      0       0  30대   \n",
       "291331    No     0  ...   NaN  NaN  5639    3      0      0       0  30대   \n",
       "291332    No     0  ...   NaN  NaN  5639    3      0      0       0  30대   \n",
       "291333    No     0  ...   NaN  NaN  5639    3      0      0       0  30대   \n",
       "\n",
       "       Recency freq  \n",
       "0          609    1  \n",
       "1          579    1  \n",
       "2          548    1  \n",
       "3          518    1  \n",
       "4          487    1  \n",
       "...        ...  ...  \n",
       "291329     122    1  \n",
       "291330      91    1  \n",
       "291331      61    1  \n",
       "291332      30    1  \n",
       "291333       0    1  \n",
       "\n",
       "[291334 rows x 32 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "ad0bc9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2598072.5</td>\n",
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       "<p>291334 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           고객ID  Frequency  Recency  Monetary      total\n",
       "0       C-10001         21      609   1755400   877584.5\n",
       "1       C-10001         21      579   1755400   877590.5\n",
       "2       C-10001         21      548   1755400   877596.7\n",
       "3       C-10001         21      518   1755400   877602.7\n",
       "4       C-10001         21      487   1755400   877608.9\n",
       "...         ...        ...      ...       ...        ...\n",
       "291329  C-17043         75      122   5196100  2598048.1\n",
       "291330  C-17043         75       91   5196100  2598054.3\n",
       "291331  C-17043         75       61   5196100  2598060.3\n",
       "291332  C-17043         75       30   5196100  2598066.5\n",
       "291333  C-17043         75        0   5196100  2598072.5\n",
       "\n",
       "[291334 rows x 5 columns]"
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     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "new=new.rename(columns={\"과금액\":\"Monetary\",'freq':'Frequency'})\n",
    "new['total']=new['Recency']*-0.2 + new['Frequency']*0.3+new['Monetary']*0.5\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "a01cc745",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hanjw\\AppData\\Local\\Temp\\ipykernel_25236\\341364884.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new['등급']=new['total'].apply(func)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "             고객ID  Frequency  Recency  Monetary      total 등급 연령대_x 연령대_y\n",
       "0         C-10001         21      609   1755400   877584.5  D   30대   30대\n",
       "21        C-10001         21      579   1755400   877590.5  D   30대   30대\n",
       "42        C-10001         21      548   1755400   877596.7  D   30대   30대\n",
       "63        C-10001         21      518   1755400   877602.7  D   30대   30대\n",
       "84        C-10001         21      487   1755400   877608.9  D   30대   30대\n",
       "...           ...        ...      ...       ...        ... ..   ...   ...\n",
       "17093929  C-17043         75      122   5196100  2598048.1  C   30대   30대\n",
       "17094004  C-17043         75       91   5196100  2598054.3  C   30대   30대\n",
       "17094079  C-17043         75       61   5196100  2598060.3  C   30대   30대\n",
       "17094154  C-17043         75       30   5196100  2598066.5  C   30대   30대\n",
       "17094229  C-17043         75        0   5196100  2598072.5  C   30대   30대\n",
       "\n",
       "[291334 rows x 8 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def func(x):\n",
    "    \n",
    "    global a,b,c,d\n",
    "    \n",
    "    if x >= a:\n",
    "        return \"A\"\n",
    "    elif x>=b:\n",
    "        return \"B\"\n",
    "    elif x>=c:\n",
    "        return 'C'\n",
    "    elif x>=d:\n",
    "        return 'D'\n",
    "    else:\n",
    "        return 'E'\n",
    "\n",
    "# new['total'].apply(func)\n",
    "\n",
    "a = new['total'].quantile(0.8)\n",
    "b = new['total'].quantile(0.6)\n",
    "c = new['total'].quantile(0.4)\n",
    "d = new['total'].quantile(0.2)\n",
    "\n",
    "new['등급']=new['total'].apply(func)\n",
    "new=new.drop_duplicates()\n",
    "new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe4dab4",
   "metadata": {},
   "source": [
    "### 6. 5등급으로 부여된 고객 등급 별 고객 연령대의 비율이 어떻게 달라지는지 계산하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "11e85bdf",
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <td>0.195788</td>\n",
       "      <td>0.182934</td>\n",
       "      <td>0.173752</td>\n",
       "      <td>0.180685</td>\n",
       "      <td>0.128958</td>\n",
       "      <td>0.137883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.195243</td>\n",
       "      <td>0.171730</td>\n",
       "      <td>0.177702</td>\n",
       "      <td>0.168417</td>\n",
       "      <td>0.153812</td>\n",
       "      <td>0.133096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.206412</td>\n",
       "      <td>0.170422</td>\n",
       "      <td>0.196646</td>\n",
       "      <td>0.196063</td>\n",
       "      <td>0.144953</td>\n",
       "      <td>0.085503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.208094</td>\n",
       "      <td>0.199015</td>\n",
       "      <td>0.227642</td>\n",
       "      <td>0.185971</td>\n",
       "      <td>0.131481</td>\n",
       "      <td>0.047797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.198288</td>\n",
       "      <td>0.181304</td>\n",
       "      <td>0.192322</td>\n",
       "      <td>0.178235</td>\n",
       "      <td>0.141999</td>\n",
       "      <td>0.107852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연령대_x       20대       30대       40대       50대       60대       노년층\n",
       "등급                                                               \n",
       "A      0.185903  0.182419  0.185869  0.160039  0.150789  0.134982\n",
       "B      0.195788  0.182934  0.173752  0.180685  0.128958  0.137883\n",
       "C      0.195243  0.171730  0.177702  0.168417  0.153812  0.133096\n",
       "D      0.206412  0.170422  0.196646  0.196063  0.144953  0.085503\n",
       "E      0.208094  0.199015  0.227642  0.185971  0.131481  0.047797\n",
       "All    0.198288  0.181304  0.192322  0.178235  0.141999  0.107852"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index= new['등급'], columns=new['연령대_x'], normalize='index',margins= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cabea1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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