{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "27ec92ee-63ca-42a7-8c16-c632f913fdf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_columns\",None)\n",
    "\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93b050a2-478a-4840-bcfe-a4d4ebbb8419",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3cef9f7d-5237-4f50-9495-f77b6d0996d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>120.20</td>\n",
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       "                               用户编码  用户实名制是否通过核实  用户年龄  是否大学生客户  是否黑名单客户  \\\n",
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       "4  f1687f3b8a6f4910bd0b13eb634056e2            1    40        0        0   \n",
       "\n",
       "   是否4G不健康客户  用户网龄（月）  用户最近一次缴费距今时长（月）  缴费用户最近一次缴费金额（元）  用户近6个月平均消费值（元）  \\\n",
       "0          0      186                1            99.80          163.86   \n",
       "1          1        5                1            29.94          153.28   \n",
       "2          0      145                1            49.90          109.64   \n",
       "3          0      234                1            99.80           92.97   \n",
       "4          0       76                1            49.90           95.47   \n",
       "\n",
       "   用户账单当月总费用（元）  用户当月账户余额（元）  缴费用户当前是否欠费缴费  用户话费敏感度  当月通话交往圈人数  是否经常逛商场的人  \\\n",
       "0        159.20          180             0        3         83          1   \n",
       "1        145.10          110             0        3         21          1   \n",
       "2        120.20           70             0        1         59          0   \n",
       "3        167.42           90             0        3         78          1   \n",
       "4        101.00           80             0        3         70          1   \n",
       "\n",
       "   近三个月月均商场出现次数  当月是否逛过福州仓山万达  当月是否到过福州山姆会员店  当月是否看电影  当月是否景点游览  当月是否体育场馆消费  \\\n",
       "0            75             0              0        0         1           1   \n",
       "1            16             0              0        0         0           0   \n",
       "2             1             0              0        0         0           0   \n",
       "3            26             0              0        0         1           1   \n",
       "4            44             0              0        0         1           0   \n",
       "\n",
       "   当月网购类应用使用次数  当月物流快递类应用使用次数  当月金融理财类应用使用总次数  当月视频播放类应用使用次数  当月飞机类应用使用次数  \\\n",
       "0          713              0            2740           7145            0   \n",
       "1          414              0            2731          44862            0   \n",
       "2         3391              0               0           4804            0   \n",
       "3          500              0            1931           3141            0   \n",
       "4          522              0              64             59            0   \n",
       "\n",
       "   当月火车类应用使用次数  当月旅游资讯类应用使用次数  信用分  \n",
       "0            0             30  664  \n",
       "1            0              0  530  \n",
       "2            0              1  643  \n",
       "3            0              5  649  \n",
       "4            0              0  648  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
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   "execution_count": 18,
   "id": "ed2a2ad8-7e4f-4f73-b315-74634361763e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(50000, 30)"
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 20,
   "id": "e0e85abf-b876-421e-b663-16ba36f8283b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "用户编码               0\n",
       "用户实名制是否通过核实        0\n",
       "用户年龄               0\n",
       "是否大学生客户            0\n",
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       "dtype: int64"
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     "metadata": {},
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   "id": "de2ee23d-48a6-4c09-80d6-0907b9da2925",
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      "Data columns (total 30 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
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      " 1   用户实名制是否通过核实      50000 non-null  int64  \n",
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      " 4   是否黑名单客户          50000 non-null  int64  \n",
      " 5   是否4G不健康客户        50000 non-null  int64  \n",
      " 6   用户网龄（月）          50000 non-null  int64  \n",
      " 7   用户最近一次缴费距今时长（月）  50000 non-null  int64  \n",
      " 8   缴费用户最近一次缴费金额（元）  50000 non-null  float64\n",
      " 9   用户近6个月平均消费值（元）   50000 non-null  float64\n",
      " 10  用户账单当月总费用（元）     50000 non-null  float64\n",
      " 11  用户当月账户余额（元）      50000 non-null  int64  \n",
      " 12  缴费用户当前是否欠费缴费     50000 non-null  int64  \n",
      " 13  用户话费敏感度          50000 non-null  int64  \n",
      " 14  当月通话交往圈人数        50000 non-null  int64  \n",
      " 15  是否经常逛商场的人        50000 non-null  int64  \n",
      " 16  近三个月月均商场出现次数     50000 non-null  int64  \n",
      " 17  当月是否逛过福州仓山万达     50000 non-null  int64  \n",
      " 18  当月是否到过福州山姆会员店    50000 non-null  int64  \n",
      " 19  当月是否看电影          50000 non-null  int64  \n",
      " 20  当月是否景点游览         50000 non-null  int64  \n",
      " 21  当月是否体育场馆消费       50000 non-null  int64  \n",
      " 22  当月网购类应用使用次数      50000 non-null  int64  \n",
      " 23  当月物流快递类应用使用次数    50000 non-null  int64  \n",
      " 24  当月金融理财类应用使用总次数   50000 non-null  int64  \n",
      " 25  当月视频播放类应用使用次数    50000 non-null  int64  \n",
      " 26  当月飞机类应用使用次数      50000 non-null  int64  \n",
      " 27  当月火车类应用使用次数      50000 non-null  int64  \n",
      " 28  当月旅游资讯类应用使用次数    50000 non-null  int64  \n",
      " 29  信用分              50000 non-null  int64  \n",
      "dtypes: float64(3), int64(26), object(1)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71e4ef93-1fe8-46ed-8fbe-d20446391976",
   "metadata": {},
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       "      <th>是否经常逛商场的人</th>\n",
       "      <th>近三个月月均商场出现次数</th>\n",
       "      <th>当月是否逛过福州仓山万达</th>\n",
       "      <th>当月是否到过福州山姆会员店</th>\n",
       "      <th>当月是否看电影</th>\n",
       "      <th>当月是否景点游览</th>\n",
       "      <th>当月是否体育场馆消费</th>\n",
       "      <th>当月网购类应用使用次数</th>\n",
       "      <th>当月物流快递类应用使用次数</th>\n",
       "      <th>当月金融理财类应用使用总次数</th>\n",
       "      <th>当月视频播放类应用使用次数</th>\n",
       "      <th>当月飞机类应用使用次数</th>\n",
       "      <th>当月火车类应用使用次数</th>\n",
       "      <th>当月旅游资讯类应用使用次数</th>\n",
       "      <th>信用分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a4651f98c82948b186bdcdc8108381b4</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>99.80</td>\n",
       "      <td>163.86</td>\n",
       "      <td>159.20</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>713</td>\n",
       "      <td>0</td>\n",
       "      <td>2740</td>\n",
       "      <td>7145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aeb10247db4e4d67b2550bbc42ff9827</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>29.94</td>\n",
       "      <td>153.28</td>\n",
       "      <td>145.10</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2731</td>\n",
       "      <td>44862</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5af23a1e0e77410abb25e9a7eee510aa</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>49.90</td>\n",
       "      <td>109.64</td>\n",
       "      <td>120.20</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4804</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43c64379d3c24a15b8478851b22049e4</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>99.80</td>\n",
       "      <td>92.97</td>\n",
       "      <td>167.42</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>3141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f1687f3b8a6f4910bd0b13eb634056e2</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>49.90</td>\n",
       "      <td>95.47</td>\n",
       "      <td>101.00</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>522</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               用户编码  用户实名制是否通过核实  用户年龄  是否大学生客户  是否黑名单客户  \\\n",
       "0  a4651f98c82948b186bdcdc8108381b4            1    44        0        0   \n",
       "1  aeb10247db4e4d67b2550bbc42ff9827            1    18        0        0   \n",
       "2  5af23a1e0e77410abb25e9a7eee510aa            1    47        0        0   \n",
       "3  43c64379d3c24a15b8478851b22049e4            1    55        0        0   \n",
       "4  f1687f3b8a6f4910bd0b13eb634056e2            1    40        0        0   \n",
       "\n",
       "   是否4G不健康客户  用户网龄（月）  用户最近一次缴费距今时长（月）  缴费用户最近一次缴费金额（元）  用户近6个月平均消费值（元）  \\\n",
       "0          0      186                1            99.80          163.86   \n",
       "1          1        5                1            29.94          153.28   \n",
       "2          0      145                1            49.90          109.64   \n",
       "3          0      234                1            99.80           92.97   \n",
       "4          0       76                1            49.90           95.47   \n",
       "\n",
       "   用户账单当月总费用（元）  用户当月账户余额（元）  缴费用户当前是否欠费缴费  用户话费敏感度  当月通话交往圈人数  是否经常逛商场的人  \\\n",
       "0        159.20          180             0        3         83          1   \n",
       "1        145.10          110             0        3         21          1   \n",
       "2        120.20           70             0        1         59          0   \n",
       "3        167.42           90             0        3         78          1   \n",
       "4        101.00           80             0        3         70          1   \n",
       "\n",
       "   近三个月月均商场出现次数  当月是否逛过福州仓山万达  当月是否到过福州山姆会员店  当月是否看电影  当月是否景点游览  当月是否体育场馆消费  \\\n",
       "0            75             0              0        0         1           1   \n",
       "1            16             0              0        0         0           0   \n",
       "2             1             0              0        0         0           0   \n",
       "3            26             0              0        0         1           1   \n",
       "4            44             0              0        0         1           0   \n",
       "\n",
       "   当月网购类应用使用次数  当月物流快递类应用使用次数  当月金融理财类应用使用总次数  当月视频播放类应用使用次数  当月飞机类应用使用次数  \\\n",
       "0          713              0            2740           7145            0   \n",
       "1          414              0            2731          44862            0   \n",
       "2         3391              0               0           4804            0   \n",
       "3          500              0            1931           3141            0   \n",
       "4          522              0              64             59            0   \n",
       "\n",
       "   当月火车类应用使用次数  当月旅游资讯类应用使用次数  信用分  \n",
       "0            0             30  664  \n",
       "1            0              0  530  \n",
       "2            0              1  643  \n",
       "3            0              5  649  \n",
       "4            0              0  648  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "999d1767-83ca-4834-afdf-782564005921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    49511\n",
       "0      489\n",
       "Name: 用户实名制是否通过核实, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['用户实名制是否通过核实'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3861d239-aa44-47c4-8619-017964ec5c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='用户年龄', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "sns.countplot(x = train['用户年龄'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6191abc6-0109-48cf-8062-6e9b2c3b10f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    49814\n",
       "1      186\n",
       "Name: 是否大学生客户, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['是否大学生客户'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b9b11971-e535-4ed7-b4f6-a8cb41a294e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    47560\n",
       "1     2440\n",
       "Name: 是否黑名单客户, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['是否黑名单客户'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "82dfb154-c057-4d82-8658-ed3415ea3656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45571\n",
       "1     4429\n",
       "Name: 是否4G不健康客户, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['是否4G不健康客户'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "294996cf-c917-40c8-aea6-f633ed52144a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      610\n",
       "7      586\n",
       "6      555\n",
       "8      537\n",
       "10     474\n",
       "      ... \n",
       "3        2\n",
       "287      2\n",
       "281      1\n",
       "280      1\n",
       "284      1\n",
       "Name: 用户网龄（月）, Length: 283, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['用户网龄（月）'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "319b94dc-6931-4007-8fb3-12bc0a4fe4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='用户网龄（月）', ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "sns.kdeplot(x = train['用户网龄（月）'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f9a08670-2e5c-44a9-ac1b-0d3b7e4dd888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户编码</th>\n",
       "      <th>用户实名制是否通过核实</th>\n",
       "      <th>用户年龄</th>\n",
       "      <th>是否大学生客户</th>\n",
       "      <th>是否黑名单客户</th>\n",
       "      <th>是否4G不健康客户</th>\n",
       "      <th>用户网龄（月）</th>\n",
       "      <th>用户最近一次缴费距今时长（月）</th>\n",
       "      <th>缴费用户最近一次缴费金额（元）</th>\n",
       "      <th>用户近6个月平均消费值（元）</th>\n",
       "      <th>用户账单当月总费用（元）</th>\n",
       "      <th>用户当月账户余额（元）</th>\n",
       "      <th>缴费用户当前是否欠费缴费</th>\n",
       "      <th>用户话费敏感度</th>\n",
       "      <th>当月通话交往圈人数</th>\n",
       "      <th>是否经常逛商场的人</th>\n",
       "      <th>近三个月月均商场出现次数</th>\n",
       "      <th>当月是否逛过福州仓山万达</th>\n",
       "      <th>当月是否到过福州山姆会员店</th>\n",
       "      <th>当月是否看电影</th>\n",
       "      <th>当月是否景点游览</th>\n",
       "      <th>当月是否体育场馆消费</th>\n",
       "      <th>当月网购类应用使用次数</th>\n",
       "      <th>当月物流快递类应用使用次数</th>\n",
       "      <th>当月金融理财类应用使用总次数</th>\n",
       "      <th>当月视频播放类应用使用次数</th>\n",
       "      <th>当月飞机类应用使用次数</th>\n",
       "      <th>当月火车类应用使用次数</th>\n",
       "      <th>当月旅游资讯类应用使用次数</th>\n",
       "      <th>信用分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a4651f98c82948b186bdcdc8108381b4</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>99.80</td>\n",
       "      <td>163.86</td>\n",
       "      <td>159.20</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>713</td>\n",
       "      <td>0</td>\n",
       "      <td>2740</td>\n",
       "      <td>7145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aeb10247db4e4d67b2550bbc42ff9827</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>29.94</td>\n",
       "      <td>153.28</td>\n",
       "      <td>145.10</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2731</td>\n",
       "      <td>44862</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5af23a1e0e77410abb25e9a7eee510aa</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>49.90</td>\n",
       "      <td>109.64</td>\n",
       "      <td>120.20</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4804</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43c64379d3c24a15b8478851b22049e4</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>99.80</td>\n",
       "      <td>92.97</td>\n",
       "      <td>167.42</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>3141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f1687f3b8a6f4910bd0b13eb634056e2</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>49.90</td>\n",
       "      <td>95.47</td>\n",
       "      <td>101.00</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>522</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               用户编码  用户实名制是否通过核实  用户年龄  是否大学生客户  是否黑名单客户  \\\n",
       "0  a4651f98c82948b186bdcdc8108381b4            1    44        0        0   \n",
       "1  aeb10247db4e4d67b2550bbc42ff9827            1    18        0        0   \n",
       "2  5af23a1e0e77410abb25e9a7eee510aa            1    47        0        0   \n",
       "3  43c64379d3c24a15b8478851b22049e4            1    55        0        0   \n",
       "4  f1687f3b8a6f4910bd0b13eb634056e2            1    40        0        0   \n",
       "\n",
       "   是否4G不健康客户  用户网龄（月）  用户最近一次缴费距今时长（月）  缴费用户最近一次缴费金额（元）  用户近6个月平均消费值（元）  \\\n",
       "0          0      186                1            99.80          163.86   \n",
       "1          1        5                1            29.94          153.28   \n",
       "2          0      145                1            49.90          109.64   \n",
       "3          0      234                1            99.80           92.97   \n",
       "4          0       76                1            49.90           95.47   \n",
       "\n",
       "   用户账单当月总费用（元）  用户当月账户余额（元）  缴费用户当前是否欠费缴费  用户话费敏感度  当月通话交往圈人数  是否经常逛商场的人  \\\n",
       "0        159.20          180             0        3         83          1   \n",
       "1        145.10          110             0        3         21          1   \n",
       "2        120.20           70             0        1         59          0   \n",
       "3        167.42           90             0        3         78          1   \n",
       "4        101.00           80             0        3         70          1   \n",
       "\n",
       "   近三个月月均商场出现次数  当月是否逛过福州仓山万达  当月是否到过福州山姆会员店  当月是否看电影  当月是否景点游览  当月是否体育场馆消费  \\\n",
       "0            75             0              0        0         1           1   \n",
       "1            16             0              0        0         0           0   \n",
       "2             1             0              0        0         0           0   \n",
       "3            26             0              0        0         1           1   \n",
       "4            44             0              0        0         1           0   \n",
       "\n",
       "   当月网购类应用使用次数  当月物流快递类应用使用次数  当月金融理财类应用使用总次数  当月视频播放类应用使用次数  当月飞机类应用使用次数  \\\n",
       "0          713              0            2740           7145            0   \n",
       "1          414              0            2731          44862            0   \n",
       "2         3391              0               0           4804            0   \n",
       "3          500              0            1931           3141            0   \n",
       "4          522              0              64             59            0   \n",
       "\n",
       "   当月火车类应用使用次数  当月旅游资讯类应用使用次数  信用分  \n",
       "0            0             30  664  \n",
       "1            0              0  530  \n",
       "2            0              1  643  \n",
       "3            0              5  649  \n",
       "4            0              0  648  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f4f3fb5-4f5c-4c10-b59c-d1bfebc8ff55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b53806f8-0107-42d4-acc9-eb718f06a38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    35005\n",
       "0    14995\n",
       "Name: 用户最近一次缴费距今时长（月）, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['用户最近一次缴费距今时长（月）'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d3a5bad2-4d9a-4ab1-a5a0-ede1fa6a25f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='缴费用户最近一次缴费金额（元）', ylabel='Density'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "sns.kdeplot(x = train['缴费用户最近一次缴费金额（元）'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "51c77306-a6fd-41bc-a06e-4ceb0588d6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      14755\n",
       "99.80     11167\n",
       "49.90     10506\n",
       "29.94      5902\n",
       "199.60     2547\n",
       "          ...  \n",
       "5.37          1\n",
       "230.00        1\n",
       "106.65        1\n",
       "52.00         1\n",
       "108.00        1\n",
       "Name: 缴费用户最近一次缴费金额（元）, Length: 326, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['缴费用户最近一次缴费金额（元）'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8aca232e-45bb-4f6a-b9c4-7b69016a03c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile = ProfileReport(train)\n",
    "# profile.to_notebook_iframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f00ba9-cb14-4b4d-bf14-bb718ceef4d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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