SOLUTION1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务1 - 数据分析（2天）\n",
    "数据集下载 \n",
    "\n",
    "这是我们本次实践数据的下载地址 https://pan.baidu.com/s/1wO9qJRjnrm8uhaSP67K0lw\n",
    "\n",
    "说明：这份数据集是金融数据（非原始数据，已经处理过了），我们要做的是预测贷款用户是否会逾期。表格中 \"status\" 是结果标签：0表示未逾期，1表示逾期。\n",
    "\n",
    "\n",
    "\n",
    "数据集涉密，不要开源到网上，谢谢~\n",
    "\n",
    "要求：数据切分方式 - 三七分，其中测试集30%，训练集70%，随机种子设置为2018\n",
    "\n",
    "任务1：对数据进行探索和分析。时间：2天\n",
    "\n",
    "  * 数据类型的分析\n",
    "  * 无关特征删除\n",
    "  * **数据类型转换**\n",
    "  * **缺失值处理**\n",
    "  * ……以及你能想到和借鉴的数据分析处理\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# task1 数据预处理分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>custid</th>\n",
       "      <th>trade_no</th>\n",
       "      <th>bank_card_no</th>\n",
       "      <th>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>...</th>\n",
       "      <th>loans_max_limit</th>\n",
       "      <th>loans_avg_limit</th>\n",
       "      <th>consfin_credit_limit</th>\n",
       "      <th>consfin_credibility</th>\n",
       "      <th>consfin_org_count_current</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2791858</td>\n",
       "      <td>20180507115231274000000023057383</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>...</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>534047</td>\n",
       "      <td>20180507121002192000000023073000</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>...</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2849787</td>\n",
       "      <td>20180507125159718000000023114911</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1809708</td>\n",
       "      <td>20180507121358683000000388283484</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>...</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2499829</td>\n",
       "      <td>20180507115448545000000388205844</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>...</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   custid                          trade_no bank_card_no  \\\n",
       "0           5  2791858  20180507115231274000000023057383          卡号1   \n",
       "1          10   534047  20180507121002192000000023073000          卡号1   \n",
       "2          12  2849787  20180507125159718000000023114911          卡号1   \n",
       "3          13  1809708  20180507121358683000000388283484          卡号1   \n",
       "4          14  2499829  20180507115448545000000388205844          卡号1   \n",
       "\n",
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day        ...         \\\n",
       "0                  0.55               0.313        ...          \n",
       "1                  1.00               0.458        ...          \n",
       "2                  1.00               0.114        ...          \n",
       "3                  0.57               0.777        ...          \n",
       "4                  1.00               0.175        ...          \n",
       "\n",
       "   loans_max_limit  loans_avg_limit  consfin_credit_limit  \\\n",
       "0           2900.0           1688.0                1200.0   \n",
       "1           3500.0           1758.0               15100.0   \n",
       "2           1600.0           1250.0                4200.0   \n",
       "3           3200.0           1541.0               16300.0   \n",
       "4           2300.0           1630.0                8300.0   \n",
       "\n",
       "   consfin_credibility  consfin_org_count_current  consfin_product_count  \\\n",
       "0                 75.0                        1.0                    2.0   \n",
       "1                 80.0                        5.0                    6.0   \n",
       "2                 87.0                        1.0                    1.0   \n",
       "3                 80.0                        5.0                    5.0   \n",
       "4                 79.0                        2.0                    2.0   \n",
       "\n",
       "   consfin_max_limit  consfin_avg_limit  latest_query_day  loans_latest_day  \n",
       "0             1200.0             1200.0              12.0              18.0  \n",
       "1            22800.0             9360.0               4.0               2.0  \n",
       "2             4200.0             4200.0               2.0               6.0  \n",
       "3            30000.0            12180.0               2.0               4.0  \n",
       "4             8400.0             8250.0              22.0             120.0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"./data.csv\",encoding='gbk')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4754 entries, 0 to 4753\n",
      "Data columns (total 90 columns):\n",
      "Unnamed: 0                                    4754 non-null int64\n",
      "custid                                        4754 non-null int64\n",
      "trade_no                                      4754 non-null object\n",
      "bank_card_no                                  4754 non-null object\n",
      "low_volume_percent                            4752 non-null float64\n",
      "middle_volume_percent                         4752 non-null float64\n",
      "take_amount_in_later_12_month_highest         4754 non-null int64\n",
      "trans_amount_increase_rate_lately             4751 non-null float64\n",
      "trans_activity_month                          4752 non-null float64\n",
      "trans_activity_day                            4752 non-null float64\n",
      "transd_mcc                                    4752 non-null float64\n",
      "trans_days_interval_filter                    4746 non-null float64\n",
      "trans_days_interval                           4752 non-null float64\n",
      "regional_mobility                             4752 non-null float64\n",
      "student_feature                               1756 non-null float64\n",
      "repayment_capability                          4754 non-null int64\n",
      "is_high_user                                  4754 non-null int64\n",
      "number_of_trans_from_2011                     4752 non-null float64\n",
      "first_transaction_time                        4752 non-null float64\n",
      "historical_trans_amount                       4754 non-null int64\n",
      "historical_trans_day                          4752 non-null float64\n",
      "rank_trad_1_month                             4752 non-null float64\n",
      "trans_amount_3_month                          4754 non-null int64\n",
      "avg_consume_less_12_valid_month               4752 non-null float64\n",
      "abs                                           4754 non-null int64\n",
      "top_trans_count_last_1_month                  4752 non-null float64\n",
      "avg_price_last_12_month                       4754 non-null int64\n",
      "avg_price_top_last_12_valid_month             4650 non-null float64\n",
      "reg_preference_for_trad                       4752 non-null object\n",
      "trans_top_time_last_1_month                   4746 non-null float64\n",
      "trans_top_time_last_6_month                   4746 non-null float64\n",
      "consume_top_time_last_1_month                 4746 non-null float64\n",
      "consume_top_time_last_6_month                 4746 non-null float64\n",
      "cross_consume_count_last_1_month              4328 non-null float64\n",
      "trans_fail_top_count_enum_last_1_month        4738 non-null float64\n",
      "trans_fail_top_count_enum_last_6_month        4738 non-null float64\n",
      "trans_fail_top_count_enum_last_12_month       4738 non-null float64\n",
      "consume_mini_time_last_1_month                4728 non-null float64\n",
      "max_cumulative_consume_later_1_month          4754 non-null int64\n",
      "max_consume_count_later_6_month               4746 non-null float64\n",
      "railway_consume_count_last_12_month           4742 non-null float64\n",
      "pawns_auctions_trusts_consume_last_1_month    4754 non-null int64\n",
      "pawns_auctions_trusts_consume_last_6_month    4754 non-null int64\n",
      "jewelry_consume_count_last_6_month            4742 non-null float64\n",
      "status                                        4754 non-null int64\n",
      "source                                        4754 non-null object\n",
      "first_transaction_day                         4752 non-null float64\n",
      "trans_day_last_12_month                       4752 non-null float64\n",
      "id_name                                       4478 non-null object\n",
      "apply_score                                   4450 non-null float64\n",
      "apply_credibility                             4450 non-null float64\n",
      "query_org_count                               4450 non-null float64\n",
      "query_finance_count                           4450 non-null float64\n",
      "query_cash_count                              4450 non-null float64\n",
      "query_sum_count                               4450 non-null float64\n",
      "latest_query_time                             4450 non-null object\n",
      "latest_one_month_apply                        4450 non-null float64\n",
      "latest_three_month_apply                      4450 non-null float64\n",
      "latest_six_month_apply                        4450 non-null float64\n",
      "loans_score                                   4457 non-null float64\n",
      "loans_credibility_behavior                    4457 non-null float64\n",
      "loans_count                                   4457 non-null float64\n",
      "loans_settle_count                            4457 non-null float64\n",
      "loans_overdue_count                           4457 non-null float64\n",
      "loans_org_count_behavior                      4457 non-null float64\n",
      "consfin_org_count_behavior                    4457 non-null float64\n",
      "loans_cash_count                              4457 non-null float64\n",
      "latest_one_month_loan                         4457 non-null float64\n",
      "latest_three_month_loan                       4457 non-null float64\n",
      "latest_six_month_loan                         4457 non-null float64\n",
      "history_suc_fee                               4457 non-null float64\n",
      "history_fail_fee                              4457 non-null float64\n",
      "latest_one_month_suc                          4457 non-null float64\n",
      "latest_one_month_fail                         4457 non-null float64\n",
      "loans_long_time                               4457 non-null float64\n",
      "loans_latest_time                             4457 non-null object\n",
      "loans_credit_limit                            4457 non-null float64\n",
      "loans_credibility_limit                       4457 non-null float64\n",
      "loans_org_count_current                       4457 non-null float64\n",
      "loans_product_count                           4457 non-null float64\n",
      "loans_max_limit                               4457 non-null float64\n",
      "loans_avg_limit                               4457 non-null float64\n",
      "consfin_credit_limit                          4457 non-null float64\n",
      "consfin_credibility                           4457 non-null float64\n",
      "consfin_org_count_current                     4457 non-null float64\n",
      "consfin_product_count                         4457 non-null float64\n",
      "consfin_max_limit                             4457 non-null float64\n",
      "consfin_avg_limit                             4457 non-null float64\n",
      "latest_query_day                              4450 non-null float64\n",
      "loans_latest_day                              4457 non-null float64\n",
      "dtypes: float64(70), int64(13), object(7)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检查数据，drop无关特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4754 entries, 0 to 4753\n",
      "Data columns (total 81 columns):\n",
      "low_volume_percent                            4752 non-null float64\n",
      "middle_volume_percent                         4752 non-null float64\n",
      "take_amount_in_later_12_month_highest         4754 non-null int64\n",
      "trans_amount_increase_rate_lately             4751 non-null float64\n",
      "trans_activity_month                          4752 non-null float64\n",
      "trans_activity_day                            4752 non-null float64\n",
      "transd_mcc                                    4752 non-null float64\n",
      "trans_days_interval_filter                    4746 non-null float64\n",
      "trans_days_interval                           4752 non-null float64\n",
      "regional_mobility                             4752 non-null float64\n",
      "student_feature                               1756 non-null float64\n",
      "repayment_capability                          4754 non-null int64\n",
      "is_high_user                                  4754 non-null int64\n",
      "number_of_trans_from_2011                     4752 non-null float64\n",
      "historical_trans_amount                       4754 non-null int64\n",
      "historical_trans_day                          4752 non-null float64\n",
      "rank_trad_1_month                             4752 non-null float64\n",
      "trans_amount_3_month                          4754 non-null int64\n",
      "avg_consume_less_12_valid_month               4752 non-null float64\n",
      "abs                                           4754 non-null int64\n",
      "top_trans_count_last_1_month                  4752 non-null float64\n",
      "avg_price_last_12_month                       4754 non-null int64\n",
      "avg_price_top_last_12_valid_month             4650 non-null float64\n",
      "reg_preference_for_trad                       4752 non-null object\n",
      "trans_top_time_last_1_month                   4746 non-null float64\n",
      "trans_top_time_last_6_month                   4746 non-null float64\n",
      "consume_top_time_last_1_month                 4746 non-null float64\n",
      "consume_top_time_last_6_month                 4746 non-null float64\n",
      "cross_consume_count_last_1_month              4328 non-null float64\n",
      "trans_fail_top_count_enum_last_1_month        4738 non-null float64\n",
      "trans_fail_top_count_enum_last_6_month        4738 non-null float64\n",
      "trans_fail_top_count_enum_last_12_month       4738 non-null float64\n",
      "consume_mini_time_last_1_month                4728 non-null float64\n",
      "max_cumulative_consume_later_1_month          4754 non-null int64\n",
      "max_consume_count_later_6_month               4746 non-null float64\n",
      "railway_consume_count_last_12_month           4742 non-null float64\n",
      "pawns_auctions_trusts_consume_last_1_month    4754 non-null int64\n",
      "pawns_auctions_trusts_consume_last_6_month    4754 non-null int64\n",
      "jewelry_consume_count_last_6_month            4742 non-null float64\n",
      "status                                        4754 non-null int64\n",
      "first_transaction_day                         4752 non-null float64\n",
      "trans_day_last_12_month                       4752 non-null float64\n",
      "apply_score                                   4450 non-null float64\n",
      "apply_credibility                             4450 non-null float64\n",
      "query_org_count                               4450 non-null float64\n",
      "query_finance_count                           4450 non-null float64\n",
      "query_cash_count                              4450 non-null float64\n",
      "query_sum_count                               4450 non-null float64\n",
      "latest_one_month_apply                        4450 non-null float64\n",
      "latest_three_month_apply                      4450 non-null float64\n",
      "latest_six_month_apply                        4450 non-null float64\n",
      "loans_score                                   4457 non-null float64\n",
      "loans_credibility_behavior                    4457 non-null float64\n",
      "loans_count                                   4457 non-null float64\n",
      "loans_settle_count                            4457 non-null float64\n",
      "loans_overdue_count                           4457 non-null float64\n",
      "loans_org_count_behavior                      4457 non-null float64\n",
      "consfin_org_count_behavior                    4457 non-null float64\n",
      "loans_cash_count                              4457 non-null float64\n",
      "latest_one_month_loan                         4457 non-null float64\n",
      "latest_three_month_loan                       4457 non-null float64\n",
      "latest_six_month_loan                         4457 non-null float64\n",
      "history_suc_fee                               4457 non-null float64\n",
      "history_fail_fee                              4457 non-null float64\n",
      "latest_one_month_suc                          4457 non-null float64\n",
      "latest_one_month_fail                         4457 non-null float64\n",
      "loans_long_time                               4457 non-null float64\n",
      "loans_credit_limit                            4457 non-null float64\n",
      "loans_credibility_limit                       4457 non-null float64\n",
      "loans_org_count_current                       4457 non-null float64\n",
      "loans_product_count                           4457 non-null float64\n",
      "loans_max_limit                               4457 non-null float64\n",
      "loans_avg_limit                               4457 non-null float64\n",
      "consfin_credit_limit                          4457 non-null float64\n",
      "consfin_credibility                           4457 non-null float64\n",
      "consfin_org_count_current                     4457 non-null float64\n",
      "consfin_product_count                         4457 non-null float64\n",
      "consfin_max_limit                             4457 non-null float64\n",
      "consfin_avg_limit                             4457 non-null float64\n",
      "latest_query_day                              4450 non-null float64\n",
      "loans_latest_day                              4457 non-null float64\n",
      "dtypes: float64(69), int64(11), object(1)\n",
      "memory usage: 2.9+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>transd_mcc</th>\n",
       "      <th>trans_days_interval_filter</th>\n",
       "      <th>trans_days_interval</th>\n",
       "      <th>regional_mobility</th>\n",
       "      <th>...</th>\n",
       "      <th>loans_max_limit</th>\n",
       "      <th>loans_avg_limit</th>\n",
       "      <th>consfin_credit_limit</th>\n",
       "      <th>consfin_credibility</th>\n",
       "      <th>consfin_org_count_current</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day  transd_mcc  \\\n",
       "0                  0.55               0.313        17.0   \n",
       "1                  1.00               0.458        19.0   \n",
       "2                  1.00               0.114        13.0   \n",
       "3                  0.57               0.777        22.0   \n",
       "4                  1.00               0.175        13.0   \n",
       "\n",
       "   trans_days_interval_filter  trans_days_interval  regional_mobility  \\\n",
       "0                        27.0                 26.0                3.0   \n",
       "1                        30.0                 14.0                4.0   \n",
       "2                        68.0                 22.0                1.0   \n",
       "3                        14.0                  6.0                3.0   \n",
       "4                        66.0                 42.0                1.0   \n",
       "\n",
       "         ...         loans_max_limit  loans_avg_limit  consfin_credit_limit  \\\n",
       "0        ...                  2900.0           1688.0                1200.0   \n",
       "1        ...                  3500.0           1758.0               15100.0   \n",
       "2        ...                  1600.0           1250.0                4200.0   \n",
       "3        ...                  3200.0           1541.0               16300.0   \n",
       "4        ...                  2300.0           1630.0                8300.0   \n",
       "\n",
       "   consfin_credibility  consfin_org_count_current  consfin_product_count  \\\n",
       "0                 75.0                        1.0                    2.0   \n",
       "1                 80.0                        5.0                    6.0   \n",
       "2                 87.0                        1.0                    1.0   \n",
       "3                 80.0                        5.0                    5.0   \n",
       "4                 79.0                        2.0                    2.0   \n",
       "\n",
       "   consfin_max_limit  consfin_avg_limit  latest_query_day  loans_latest_day  \n",
       "0             1200.0             1200.0              12.0              18.0  \n",
       "1            22800.0             9360.0               4.0               2.0  \n",
       "2             4200.0             4200.0               2.0               6.0  \n",
       "3            30000.0            12180.0               2.0               4.0  \n",
       "4             8400.0             8250.0              22.0             120.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns = [\"Unnamed: 0\", \"custid\",\"trade_no\",\"bank_card_no\",\"first_transaction_time\",\"source\",\"id_name\",\"latest_query_time\",\"loans_latest_time\"]\n",
    "data_drop_1 = data.drop(drop_columns, axis=1)\n",
    "data_drop_1.info()\n",
    "data_drop_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 类型转换\n",
    "- 转换object为int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>transd_mcc</th>\n",
       "      <th>trans_days_interval_filter</th>\n",
       "      <th>trans_days_interval</th>\n",
       "      <th>regional_mobility</th>\n",
       "      <th>...</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "      <th>reg_preference_for_trad_一线城市</th>\n",
       "      <th>reg_preference_for_trad_三线城市</th>\n",
       "      <th>reg_preference_for_trad_二线城市</th>\n",
       "      <th>reg_preference_for_trad_其他城市</th>\n",
       "      <th>reg_preference_for_trad_境外</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day  transd_mcc  \\\n",
       "0                  0.55               0.313        17.0   \n",
       "1                  1.00               0.458        19.0   \n",
       "2                  1.00               0.114        13.0   \n",
       "3                  0.57               0.777        22.0   \n",
       "4                  1.00               0.175        13.0   \n",
       "\n",
       "   trans_days_interval_filter  trans_days_interval  regional_mobility  \\\n",
       "0                        27.0                 26.0                3.0   \n",
       "1                        30.0                 14.0                4.0   \n",
       "2                        68.0                 22.0                1.0   \n",
       "3                        14.0                  6.0                3.0   \n",
       "4                        66.0                 42.0                1.0   \n",
       "\n",
       "              ...              consfin_product_count  consfin_max_limit  \\\n",
       "0             ...                                2.0             1200.0   \n",
       "1             ...                                6.0            22800.0   \n",
       "2             ...                                1.0             4200.0   \n",
       "3             ...                                5.0            30000.0   \n",
       "4             ...                                2.0             8400.0   \n",
       "\n",
       "   consfin_avg_limit  latest_query_day  loans_latest_day  \\\n",
       "0             1200.0              12.0              18.0   \n",
       "1             9360.0               4.0               2.0   \n",
       "2             4200.0               2.0               6.0   \n",
       "3            12180.0               2.0               4.0   \n",
       "4             8250.0              22.0             120.0   \n",
       "\n",
       "   reg_preference_for_trad_一线城市  reg_preference_for_trad_三线城市  \\\n",
       "0                             1                             0   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             0                             1   \n",
       "4                             1                             0   \n",
       "\n",
       "   reg_preference_for_trad_二线城市  reg_preference_for_trad_其他城市  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   reg_preference_for_trad_境外  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_type_2 = pd.get_dummies(data_drop_1,columns=['reg_preference_for_trad'])\n",
    "data_type_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 缺失值处理\n",
    "- 观察数据特征，删除含有缺失值的特征\n",
    "- 插值方法填充缺失值\n",
    "    - 均值\n",
    "    - 众数\n",
    "    - 特殊值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4754 entries, 0 to 4753\n",
      "Data columns (total 85 columns):\n",
      "low_volume_percent                            4754 non-null float64\n",
      "middle_volume_percent                         4754 non-null float64\n",
      "take_amount_in_later_12_month_highest         4754 non-null int64\n",
      "trans_amount_increase_rate_lately             4754 non-null float64\n",
      "trans_activity_month                          4754 non-null float64\n",
      "trans_activity_day                            4754 non-null float64\n",
      "transd_mcc                                    4754 non-null float64\n",
      "trans_days_interval_filter                    4754 non-null float64\n",
      "trans_days_interval                           4754 non-null float64\n",
      "regional_mobility                             4754 non-null float64\n",
      "student_feature                               4754 non-null float64\n",
      "repayment_capability                          4754 non-null int64\n",
      "is_high_user                                  4754 non-null int64\n",
      "number_of_trans_from_2011                     4754 non-null float64\n",
      "historical_trans_amount                       4754 non-null int64\n",
      "historical_trans_day                          4754 non-null float64\n",
      "rank_trad_1_month                             4754 non-null float64\n",
      "trans_amount_3_month                          4754 non-null int64\n",
      "avg_consume_less_12_valid_month               4754 non-null float64\n",
      "abs                                           4754 non-null int64\n",
      "top_trans_count_last_1_month                  4754 non-null float64\n",
      "avg_price_last_12_month                       4754 non-null int64\n",
      "avg_price_top_last_12_valid_month             4754 non-null float64\n",
      "trans_top_time_last_1_month                   4754 non-null float64\n",
      "trans_top_time_last_6_month                   4754 non-null float64\n",
      "consume_top_time_last_1_month                 4754 non-null float64\n",
      "consume_top_time_last_6_month                 4754 non-null float64\n",
      "cross_consume_count_last_1_month              4754 non-null float64\n",
      "trans_fail_top_count_enum_last_1_month        4754 non-null float64\n",
      "trans_fail_top_count_enum_last_6_month        4754 non-null float64\n",
      "trans_fail_top_count_enum_last_12_month       4754 non-null float64\n",
      "consume_mini_time_last_1_month                4754 non-null float64\n",
      "max_cumulative_consume_later_1_month          4754 non-null int64\n",
      "max_consume_count_later_6_month               4754 non-null float64\n",
      "railway_consume_count_last_12_month           4754 non-null float64\n",
      "pawns_auctions_trusts_consume_last_1_month    4754 non-null int64\n",
      "pawns_auctions_trusts_consume_last_6_month    4754 non-null int64\n",
      "jewelry_consume_count_last_6_month            4754 non-null float64\n",
      "status                                        4754 non-null int64\n",
      "first_transaction_day                         4754 non-null float64\n",
      "trans_day_last_12_month                       4754 non-null float64\n",
      "apply_score                                   4754 non-null float64\n",
      "apply_credibility                             4754 non-null float64\n",
      "query_org_count                               4754 non-null float64\n",
      "query_finance_count                           4754 non-null float64\n",
      "query_cash_count                              4754 non-null float64\n",
      "query_sum_count                               4754 non-null float64\n",
      "latest_one_month_apply                        4754 non-null float64\n",
      "latest_three_month_apply                      4754 non-null float64\n",
      "latest_six_month_apply                        4754 non-null float64\n",
      "loans_score                                   4754 non-null float64\n",
      "loans_credibility_behavior                    4754 non-null float64\n",
      "loans_count                                   4754 non-null float64\n",
      "loans_settle_count                            4754 non-null float64\n",
      "loans_overdue_count                           4754 non-null float64\n",
      "loans_org_count_behavior                      4754 non-null float64\n",
      "consfin_org_count_behavior                    4754 non-null float64\n",
      "loans_cash_count                              4754 non-null float64\n",
      "latest_one_month_loan                         4754 non-null float64\n",
      "latest_three_month_loan                       4754 non-null float64\n",
      "latest_six_month_loan                         4754 non-null float64\n",
      "history_suc_fee                               4754 non-null float64\n",
      "history_fail_fee                              4754 non-null float64\n",
      "latest_one_month_suc                          4754 non-null float64\n",
      "latest_one_month_fail                         4754 non-null float64\n",
      "loans_long_time                               4754 non-null float64\n",
      "loans_credit_limit                            4754 non-null float64\n",
      "loans_credibility_limit                       4754 non-null float64\n",
      "loans_org_count_current                       4754 non-null float64\n",
      "loans_product_count                           4754 non-null float64\n",
      "loans_max_limit                               4754 non-null float64\n",
      "loans_avg_limit                               4754 non-null float64\n",
      "consfin_credit_limit                          4754 non-null float64\n",
      "consfin_credibility                           4754 non-null float64\n",
      "consfin_org_count_current                     4754 non-null float64\n",
      "consfin_product_count                         4754 non-null float64\n",
      "consfin_max_limit                             4754 non-null float64\n",
      "consfin_avg_limit                             4754 non-null float64\n",
      "latest_query_day                              4754 non-null float64\n",
      "loans_latest_day                              4754 non-null float64\n",
      "reg_preference_for_trad_一线城市                  4754 non-null uint8\n",
      "reg_preference_for_trad_三线城市                  4754 non-null uint8\n",
      "reg_preference_for_trad_二线城市                  4754 non-null uint8\n",
      "reg_preference_for_trad_其他城市                  4754 non-null uint8\n",
      "reg_preference_for_trad_境外                    4754 non-null uint8\n",
      "dtypes: float64(69), int64(11), uint8(5)\n",
      "memory usage: 2.9 MB\n"
     ]
    },
    {
     "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>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>transd_mcc</th>\n",
       "      <th>trans_days_interval_filter</th>\n",
       "      <th>trans_days_interval</th>\n",
       "      <th>regional_mobility</th>\n",
       "      <th>...</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "      <th>reg_preference_for_trad_一线城市</th>\n",
       "      <th>reg_preference_for_trad_三线城市</th>\n",
       "      <th>reg_preference_for_trad_二线城市</th>\n",
       "      <th>reg_preference_for_trad_其他城市</th>\n",
       "      <th>reg_preference_for_trad_境外</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day  transd_mcc  \\\n",
       "0                  0.55               0.313        17.0   \n",
       "1                  1.00               0.458        19.0   \n",
       "2                  1.00               0.114        13.0   \n",
       "3                  0.57               0.777        22.0   \n",
       "4                  1.00               0.175        13.0   \n",
       "\n",
       "   trans_days_interval_filter  trans_days_interval  regional_mobility  \\\n",
       "0                        27.0                 26.0                3.0   \n",
       "1                        30.0                 14.0                4.0   \n",
       "2                        68.0                 22.0                1.0   \n",
       "3                        14.0                  6.0                3.0   \n",
       "4                        66.0                 42.0                1.0   \n",
       "\n",
       "              ...              consfin_product_count  consfin_max_limit  \\\n",
       "0             ...                                2.0             1200.0   \n",
       "1             ...                                6.0            22800.0   \n",
       "2             ...                                1.0             4200.0   \n",
       "3             ...                                5.0            30000.0   \n",
       "4             ...                                2.0             8400.0   \n",
       "\n",
       "   consfin_avg_limit  latest_query_day  loans_latest_day  \\\n",
       "0             1200.0              12.0              18.0   \n",
       "1             9360.0               4.0               2.0   \n",
       "2             4200.0               2.0               6.0   \n",
       "3            12180.0               2.0               4.0   \n",
       "4             8250.0              22.0             120.0   \n",
       "\n",
       "   reg_preference_for_trad_一线城市  reg_preference_for_trad_三线城市  \\\n",
       "0                             1                             0   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             0                             1   \n",
       "4                             1                             0   \n",
       "\n",
       "   reg_preference_for_trad_二线城市  reg_preference_for_trad_其他城市  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   reg_preference_for_trad_境外  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# student有特殊意义，空值填充0\n",
    "data_type_2[\"student_feature\"].fillna(0, inplace=True)\n",
    "# 其余特征众数填充即可\n",
    "data_drop_cols = data_type_2.columns.values\n",
    "for data_col in data_drop_cols:\n",
    "    fill_value = data_type_2[data_col].value_counts().index[0]\n",
    "    data_type_2[data_col].fillna(fill_value, inplace=True)\n",
    "data_type_2.info()\n",
    "data_type_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存数据集\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type_2.to_pickle(\"./final.pkl\")"
   ]
  }
 ],
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