data_manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import MysqlIO\n",
    "import pandas as pd\n",
    "# import pandas_profiling as pp\n",
    "# from IPython.display import display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from utils import add_prefix_to_colnames, onehot, str_to_datetime, summary_df, day_to_int\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# db = MysqlIO(host='relational.fit.cvut.cz', \n",
    "#             database='financial', \n",
    "#             user='guest', \n",
    "#             password='relational')\n",
    "\n",
    "# print(db.execute('SHOW TABLES'))\n",
    "\n",
    "# loan_df = db.execute_to_df('SELECT * FROM loan')\n",
    "# order_df = db.execute_to_df('SELECT * FROM `order`')\n",
    "# trans_df = db.execute_to_df('''\n",
    "#     SELECT * FROM trans WHERE account_id IN \n",
    "#     (SELECT account_id FROM loan)\n",
    "#    ''')\n",
    "# disp_df = db.execute_to_df('SELECT * FROM disp')\n",
    "# account_df = db.execute_to_df('SELECT * FROM account')\n",
    "# client_df = db.execute_to_df('SELECT * FROM client')\n",
    "# card_df = db.execute_to_df('SELECT * FROM card')\n",
    "# district_df = db.execute_to_df('SELECT * FROM district')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_df = pd.read_csv('data/loan.csv')\n",
    "order_df = pd.read_csv('data/order.csv')\n",
    "trans_df = pd.read_csv('data/trans.csv')\n",
    "disp_df = pd.read_csv('data/disp.csv')\n",
    "account_df = pd.read_csv('data/account.csv')\n",
    "client_df = pd.read_csv('data/client.csv')\n",
    "card_df = pd.read_csv('data/card.csv')\n",
    "district_df = pd.read_csv('data/district.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan\n",
      "(682, 7)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>payments</th>\n",
       "      <th>status</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4962</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308.0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_id  account_id        date  amount  duration  payments status\n",
       "0     4959           2  1994-01-05   80952        24    3373.0      A\n",
       "1     4961          19  1996-04-29   30276        12    2523.0      B\n",
       "2     4962          25  1997-12-08   30276        12    2523.0      A\n",
       "3     4967          37  1998-10-14  318480        60    5308.0      D\n",
       "4     4968          38  1998-04-19  110736        48    2307.0      C"
      ]
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     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "loan_id       0\n",
       "account_id    0\n",
       "date          0\n",
       "amount        0\n",
       "duration      0\n",
       "payments      0\n",
       "status        0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order\n",
      "(6471, 6)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>bank_to</th>\n",
       "      <th>account_to</th>\n",
       "      <th>amount</th>\n",
       "      <th>k_symbol</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29401</td>\n",
       "      <td>1</td>\n",
       "      <td>YZ</td>\n",
       "      <td>87144583</td>\n",
       "      <td>2452.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29402</td>\n",
       "      <td>2</td>\n",
       "      <td>ST</td>\n",
       "      <td>89597016</td>\n",
       "      <td>3372.7</td>\n",
       "      <td>UVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29403</td>\n",
       "      <td>2</td>\n",
       "      <td>QR</td>\n",
       "      <td>13943797</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29404</td>\n",
       "      <td>3</td>\n",
       "      <td>WX</td>\n",
       "      <td>83084338</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29405</td>\n",
       "      <td>3</td>\n",
       "      <td>CD</td>\n",
       "      <td>24485939</td>\n",
       "      <td>327.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   order_id  account_id bank_to  account_to  amount k_symbol\n",
       "0     29401           1      YZ    87144583  2452.0     SIPO\n",
       "1     29402           2      ST    89597016  3372.7     UVER\n",
       "2     29403           2      QR    13943797  7266.0     SIPO\n",
       "3     29404           3      WX    83084338  1135.0     SIPO\n",
       "4     29405           3      CD    24485939   327.0      NaN"
      ]
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     "text": [
      "missing value\n"
     ]
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     "data": {
      "text/plain": [
       "order_id         0\n",
       "account_id       0\n",
       "bank_to          0\n",
       "account_to       0\n",
       "amount           0\n",
       "k_symbol      1379\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trans\n",
      "(191556, 10)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
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       "      <th>0</th>\n",
       "      <td>276</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>VKLAD</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>281</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-05-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>54631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>30354</td>\n",
       "      <td>67530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   trans_id  account_id        date    type      operation  amount  balance  \\\n",
       "0       276           2  1993-02-26  PRIJEM          VKLAD    1100     1100   \n",
       "1       279           2  1993-03-12  PRIJEM  PREVOD Z UCTU   20236    21336   \n",
       "2       280           2  1993-04-12  PRIJEM  PREVOD Z UCTU   20236    45286   \n",
       "3       281           2  1993-05-12  PRIJEM  PREVOD Z UCTU   20236    54631   \n",
       "4       282           2  1993-06-12  PRIJEM  PREVOD Z UCTU   30354    67530   \n",
       "\n",
       "  k_symbol bank     account  \n",
       "0      NaN  NaN         NaN  \n",
       "1      NaN   ST  66487163.0  \n",
       "2      NaN   ST  66487163.0  \n",
       "3      NaN   ST  66487163.0  \n",
       "4      NaN   ST  66487163.0  "
      ]
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      "missing value\n"
     ]
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     "data": {
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       "trans_id           0\n",
       "account_id         0\n",
       "date               0\n",
       "type               0\n",
       "operation      31338\n",
       "amount             0\n",
       "balance            0\n",
       "k_symbol       92447\n",
       "bank          141043\n",
       "account       128931\n",
       "dtype: int64"
      ]
     },
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     "text": [
      "disp\n",
      "(5369, 4)\n"
     ]
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       "      <td>OWNER</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>OWNER</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>OWNER</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   disp_id  client_id  account_id       type\n",
       "0        1          1           1      OWNER\n",
       "1        2          2           2      OWNER\n",
       "2        3          3           2  DISPONENT\n",
       "3        4          4           3      OWNER\n",
       "4        5          5           3  DISPONENT"
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      "missing value\n"
     ]
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     "data": {
      "text/plain": [
       "disp_id       0\n",
       "client_id     0\n",
       "account_id    0\n",
       "type          0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "account\n",
      "(4500, 4)\n"
     ]
    },
    {
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       "      <th>district_id</th>\n",
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       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>POPLATEK MESICNE</td>\n",
       "      <td>1995-03-24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>POPLATEK MESICNE</td>\n",
       "      <td>1993-02-26</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>POPLATEK MESICNE</td>\n",
       "      <td>1997-07-07</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>POPLATEK MESICNE</td>\n",
       "      <td>1996-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>POPLATEK MESICNE</td>\n",
       "      <td>1997-05-30</td>\n",
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      ],
      "text/plain": [
       "   account_id  district_id         frequency        date\n",
       "0           1           18  POPLATEK MESICNE  1995-03-24\n",
       "1           2            1  POPLATEK MESICNE  1993-02-26\n",
       "2           3            5  POPLATEK MESICNE  1997-07-07\n",
       "3           4           12  POPLATEK MESICNE  1996-02-21\n",
       "4           5           15  POPLATEK MESICNE  1997-05-30"
      ]
     },
     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "missing value\n"
     ]
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    {
     "data": {
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       "account_id     0\n",
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     },
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     "text": [
      "client\n",
      "(5369, 4)\n"
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
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       "      <td>5</td>\n",
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       "   client_id gender  birth_date  district_id\n",
       "0          1      F  1970-12-13           18\n",
       "1          2      M  1945-02-04            1\n",
       "2          3      F  1940-10-09            1\n",
       "3          4      M  1956-12-01            5\n",
       "4          5      F  1960-07-03            5"
      ]
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      "missing value\n"
     ]
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "card\n",
      "(892, 4)\n"
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       "   card_id  disp_id     type      issued\n",
       "0        1        9     gold  1998-10-16\n",
       "1        2       19  classic  1998-03-13\n",
       "2        3       41     gold  1995-09-03\n",
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     "text": [
      "missing value\n"
     ]
    },
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     "data": {
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       "card_id    0\n",
       "disp_id    0\n",
       "type       0\n",
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      ]
     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "district\n",
      "(77, 16)\n"
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    },
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       "      <th>A2</th>\n",
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       "      <td>75232</td>\n",
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       "      <td>2824.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kladno</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>149893</td>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
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       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>9753</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>109</td>\n",
       "      <td>5244.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kolin</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>95616</td>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>51.4</td>\n",
       "      <td>9307</td>\n",
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       "      <td>118</td>\n",
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      "text/plain": [
       "   district_id           A2               A3       A4  A5  A6  A7  A8  A9  \\\n",
       "0            1  Hl.m. Praha           Prague  1204953   0   0   0   1   1   \n",
       "1            2      Benesov  central Bohemia    88884  80  26   6   2   5   \n",
       "2            3       Beroun  central Bohemia    75232  55  26   4   1   5   \n",
       "3            4       Kladno  central Bohemia   149893  63  29   6   2   6   \n",
       "4            5        Kolin  central Bohemia    95616  65  30   4   1   6   \n",
       "\n",
       "     A10    A11  A12   A13  A14      A15    A16  \n",
       "0  100.0  12541  0.2  0.43  167  85677.0  99107  \n",
       "1   46.7   8507  1.6  1.85  132   2159.0   2674  \n",
       "2   41.7   8980  1.9  2.21  111   2824.0   2813  \n",
       "3   67.4   9753  4.6  5.05  109   5244.0   5892  \n",
       "4   51.4   9307  3.8  4.43  118   2616.0   3040  "
      ]
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     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "district_id    0\n",
       "A2             0\n",
       "A3             0\n",
       "A4             0\n",
       "A5             0\n",
       "A6             0\n",
       "A7             0\n",
       "A8             0\n",
       "A9             0\n",
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       "dtype: int64"
      ]
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     "metadata": {},
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   ],
   "source": [
    "summary_df(loan_df, \"loan\")\n",
    "summary_df(order_df, \"order\")\n",
    "summary_df(trans_df, \"trans\")\n",
    "summary_df(disp_df, \"disp\")\n",
    "summary_df(account_df, \"account\")\n",
    "summary_df(client_df, \"client\")\n",
    "summary_df(card_df, \"card\")\n",
    "summary_df(district_df, \"district\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facts you need to know about Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/Data_description.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Description\n",
    "* relation account (4500 records) - each record describes static characteristics of an account,\n",
    "* relation client (5369 records) - each record describes characteristics of a client,\n",
    "* relation disposition (5369 records) - each record relates together a client with an account i.e. this relation describes the rights of clients to operate accounts,\n",
    "* relation permanent order (6471 records) - each record describes characteristics of a payment order,\n",
    "* relation transaction (1056320 records) - each record describes one transaction on an account,\n",
    "* relation loan (682 records) - each record describes a loan granted for a given account,\n",
    "* relation credit card (892 records) - each record describes a credit card issued to an account,\n",
    "* relation demographic data (77 records) - each record describes demographic characteristics of a district."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deeper (you don't need to know)\n",
    "* 1 client can have multiple accounts.\n",
    "Also, 1 account can have multiple clients (owner, desponent).\n",
    "* sanction if negative balance. sometimes amount is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loan table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(682, 7)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
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       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0          4959           2 1994-01-05        80952             24   \n",
       "1          4961          19 1996-04-29        30276             12   \n",
       "2          4962          25 1997-12-08        30276             12   \n",
       "3          4967          37 1998-10-14       318480             60   \n",
       "4          4968          38 1998-04-19       110736             48   \n",
       "\n",
       "   loan_payments loan_status  \n",
       "0           3373           A  \n",
       "1           2523           B  \n",
       "2           2523           A  \n",
       "3           5308           D  \n",
       "4           2307           C  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df_ = add_prefix_to_colnames(loan_df, prefix='loan_')\n",
    "loan_df_['loan_payments'] = loan_df_['loan_payments'].astype('int64')\n",
    "loan_df_['loan_date'] = str_to_datetime(loan_df_['loan_date'])\n",
    "print(loan_df_.shape)\n",
    "loan_df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loan_df_.to_csv('table_csv/loan.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## account table\n",
    "* Edit 'frequency' field to English.\n",
    "* Create feature 'day_before_loan'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
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       "      <th>account_id</th>\n",
       "      <th>district_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1995-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1993-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1997-07-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1996-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1997-05-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  district_id frequency        date\n",
       "0           1           18   monthly  1995-03-24\n",
       "1           2            1   monthly  1993-02-26\n",
       "2           3            5   monthly  1997-07-07\n",
       "3           4           12   monthly  1996-02-21\n",
       "4           5           15   monthly  1997-05-30"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account_df.loc[account_df['frequency']==\"POPLATEK MESICNE\", 'frequency'] = 'monthly'\n",
    "account_df.loc[account_df['frequency']==\"POPLATEK TYDNE\", 'frequency'] = 'weekly'\n",
    "account_df.loc[account_df['frequency']==\"POPLATEK PO OBRATU\", 'frequency'] = 'after_transaction'\n",
    "account_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "# account_df.to_csv('table_csv/account.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4500, 4)\n"
     ]
    },
    {
     "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",
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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>account_id</th>\n",
       "      <th>acc_district_id</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>acc_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1995-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1993-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1997-07-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1996-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>monthly</td>\n",
       "      <td>1997-05-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  acc_district_id acc_frequency   acc_date\n",
       "0           1               18       monthly 1995-03-24\n",
       "1           2                1       monthly 1993-02-26\n",
       "2           3                5       monthly 1997-07-07\n",
       "3           4               12       monthly 1996-02-21\n",
       "4           5               15       monthly 1997-05-30"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account_df_ = add_prefix_to_colnames(account_df, prefix='acc_')\n",
    "account_df_['acc_date'] = str_to_datetime(account_df_['acc_date'])\n",
    "print(account_df_.shape)\n",
    "account_df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(682, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_district_id</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4962</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>68</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>20</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0          4959           2 1994-01-05        80952             24   \n",
       "1          4961          19 1996-04-29        30276             12   \n",
       "2          4962          25 1997-12-08        30276             12   \n",
       "3          4967          37 1998-10-14       318480             60   \n",
       "4          4968          38 1998-04-19       110736             48   \n",
       "\n",
       "   loan_payments loan_status  acc_district_id acc_frequency  day_before_loan  \n",
       "0           3373           A                1       monthly              313  \n",
       "1           2523           B               21       monthly              388  \n",
       "2           2523           A               68       monthly              498  \n",
       "3           5308           D               20       monthly              422  \n",
       "4           2307           C               19        weekly              254  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(loan_df_, account_df_, how='left', on=['account_id'])\n",
    "df['day_before_loan'] = df['loan_date'] - df['acc_date']\n",
    "df['day_before_loan'] = day_to_int(df['day_before_loan'])\n",
    "df = df.drop(['acc_date'], axis=1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## order manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "each record describes characteristics of a payment order (monthly).\n",
    "\n",
    "We can extract all of the features in this table from the transaction table.\n",
    "\n",
    "So, we decided not to use this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "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",
       "        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>order_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>bank_to</th>\n",
       "      <th>account_to</th>\n",
       "      <th>amount</th>\n",
       "      <th>k_symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29401</td>\n",
       "      <td>1</td>\n",
       "      <td>YZ</td>\n",
       "      <td>87144583</td>\n",
       "      <td>2452.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29402</td>\n",
       "      <td>2</td>\n",
       "      <td>ST</td>\n",
       "      <td>89597016</td>\n",
       "      <td>3372.7</td>\n",
       "      <td>UVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29403</td>\n",
       "      <td>2</td>\n",
       "      <td>QR</td>\n",
       "      <td>13943797</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29404</td>\n",
       "      <td>3</td>\n",
       "      <td>WX</td>\n",
       "      <td>83084338</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>SIPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29405</td>\n",
       "      <td>3</td>\n",
       "      <td>CD</td>\n",
       "      <td>24485939</td>\n",
       "      <td>327.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  account_id bank_to  account_to  amount k_symbol\n",
       "0     29401           1      YZ    87144583  2452.0     SIPO\n",
       "1     29402           2      ST    89597016  3372.7     UVER\n",
       "2     29403           2      QR    13943797  7266.0     SIPO\n",
       "3     29404           3      WX    83084338  1135.0     SIPO\n",
       "4     29405           3      CD    24485939   327.0      NaN"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "order_df there are \"spacing\". so, we add NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df['k_symbol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df = order_df.replace(\"\", np.nan)\n",
    "# order_df['k_symbol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df.loc[order_df['k_symbol']==\"POJISTNE\", 'k_symbol'] = 'insurrance'\n",
    "# order_df.loc[order_df['k_symbol']==\"SIPO\", 'k_symbol'] = 'household'\n",
    "# order_df.loc[order_df['k_symbol']==\"LEASING\", 'k_symbol'] = 'leasing'\n",
    "# order_df.loc[order_df['k_symbol']==\"UVER\", 'k_symbol'] = 'loan'\n",
    "\n",
    "# # print(order_df.shape)\n",
    "# order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add_prefix_to_colnames(order_df, prefix='order_').to_csv('table_csv/order.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop k_symbol nan because we don't know what k_symbol it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df = order_df[order_df['k_symbol'].notna()]\n",
    "# order_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k_symbol onehot encoding\n",
    "\n",
    "this tells us, each account have had monthly payment in each k_symbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df_ = onehot(order_df, col_name='k_symbol', prefix='monthly_payment_for')\n",
    "# order_df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "multiple each k_symbol with its amount. because this tells us each mont the account need to pay how much. not just have had pay for what."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df_['monthly_payment_for_household'] = order_df_['monthly_payment_for_household'] * order_df_['amount'] \n",
    "# order_df_['monthly_payment_for_insurrance'] = order_df_['monthly_payment_for_insurrance'] * order_df_['amount'] \n",
    "# order_df_['monthly_payment_for_leasing'] = order_df_['monthly_payment_for_leasing'] * order_df_['amount'] \n",
    "# order_df_['monthly_payment_for_loan'] = order_df_['monthly_payment_for_loan'] * order_df_['amount']\n",
    "# order_df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop bank_to, account_to, k_symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df_ = order_df_.drop(['order_id', 'bank_to', 'account_to', 'amount'], axis=1)\n",
    "# order_df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order_df_ = add_prefix_to_colnames(order_df_, prefix='order_')\n",
    "# order_df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Order -> useless เพราะ k_symbol ของ transaction ละเอียดกว่า"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_district_id</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4962</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>68</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>20</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0          4959           2 1994-01-05        80952             24   \n",
       "1          4961          19 1996-04-29        30276             12   \n",
       "2          4962          25 1997-12-08        30276             12   \n",
       "3          4967          37 1998-10-14       318480             60   \n",
       "4          4968          38 1998-04-19       110736             48   \n",
       "\n",
       "   loan_payments loan_status  acc_district_id acc_frequency  day_before_loan  \n",
       "0           3373           A                1       monthly              313  \n",
       "1           2523           B               21       monthly              388  \n",
       "2           2523           A               68       monthly              498  \n",
       "3           5308           D               20       monthly              422  \n",
       "4           2307           C               19        weekly              254  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.merge(df, order_df_, how='left', on=['account_id'])\n",
    "# df = pd.merge(df, order_ksymbol, how='left', on=['account_id'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Replace None (null values), spacing with NaN\n",
    "* Rename feature 'type', 'operation', 'k_symbol' to English.\n",
    "* Money in, Money out -> separate amount to amount_in, amount_out\n",
    "* Separate data to Before loan and After loan (we can use only Before loan data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(191556, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>VKLAD</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>279</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>281</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-05-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>20236</td>\n",
       "      <td>54631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-12</td>\n",
       "      <td>PRIJEM</td>\n",
       "      <td>PREVOD Z UCTU</td>\n",
       "      <td>30354</td>\n",
       "      <td>67530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trans_id  account_id        date    type      operation  amount  balance  \\\n",
       "0       276           2  1993-02-26  PRIJEM          VKLAD    1100     1100   \n",
       "1       279           2  1993-03-12  PRIJEM  PREVOD Z UCTU   20236    21336   \n",
       "2       280           2  1993-04-12  PRIJEM  PREVOD Z UCTU   20236    45286   \n",
       "3       281           2  1993-05-12  PRIJEM  PREVOD Z UCTU   20236    54631   \n",
       "4       282           2  1993-06-12  PRIJEM  PREVOD Z UCTU   30354    67530   \n",
       "\n",
       "  k_symbol bank     account  \n",
       "0      NaN  NaN         NaN  \n",
       "1      NaN   ST  66487163.0  \n",
       "2      NaN   ST  66487163.0  \n",
       "3      NaN   ST  66487163.0  \n",
       "4      NaN   ST  66487163.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "trans_id           0\n",
       "account_id         0\n",
       "date               0\n",
       "type               0\n",
       "operation      31338\n",
       "amount             0\n",
       "balance            0\n",
       "k_symbol       92447\n",
       "bank          141043\n",
       "account       128931\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_df(trans_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "replace None (null values), spacing with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>6294</td>\n",
       "      <td>25</td>\n",
       "      <td>1996-10-06</td>\n",
       "      <td>VYDAJ</td>\n",
       "      <td>PREVOD NA UCET</td>\n",
       "      <td>1110</td>\n",
       "      <td>79600</td>\n",
       "      <td></td>\n",
       "      <td>AB</td>\n",
       "      <td>79838293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>6295</td>\n",
       "      <td>25</td>\n",
       "      <td>1996-11-06</td>\n",
       "      <td>VYDAJ</td>\n",
       "      <td>PREVOD NA UCET</td>\n",
       "      <td>1110</td>\n",
       "      <td>65325</td>\n",
       "      <td></td>\n",
       "      <td>AB</td>\n",
       "      <td>79838293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>6296</td>\n",
       "      <td>25</td>\n",
       "      <td>1996-12-06</td>\n",
       "      <td>VYDAJ</td>\n",
       "      <td>PREVOD NA UCET</td>\n",
       "      <td>1110</td>\n",
       "      <td>68099</td>\n",
       "      <td></td>\n",
       "      <td>AB</td>\n",
       "      <td>79838293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>6297</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-01-06</td>\n",
       "      <td>VYDAJ</td>\n",
       "      <td>PREVOD NA UCET</td>\n",
       "      <td>1110</td>\n",
       "      <td>58744</td>\n",
       "      <td></td>\n",
       "      <td>AB</td>\n",
       "      <td>79838293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>6298</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-02-06</td>\n",
       "      <td>VYDAJ</td>\n",
       "      <td>PREVOD NA UCET</td>\n",
       "      <td>1110</td>\n",
       "      <td>81076</td>\n",
       "      <td></td>\n",
       "      <td>AB</td>\n",
       "      <td>79838293.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trans_id  account_id        date   type       operation  amount  balance  \\\n",
       "888      6294          25  1996-10-06  VYDAJ  PREVOD NA UCET    1110    79600   \n",
       "889      6295          25  1996-11-06  VYDAJ  PREVOD NA UCET    1110    65325   \n",
       "890      6296          25  1996-12-06  VYDAJ  PREVOD NA UCET    1110    68099   \n",
       "891      6297          25  1997-01-06  VYDAJ  PREVOD NA UCET    1110    58744   \n",
       "892      6298          25  1997-02-06  VYDAJ  PREVOD NA UCET    1110    81076   \n",
       "\n",
       "    k_symbol bank     account  \n",
       "888            AB  79838293.0  \n",
       "889            AB  79838293.0  \n",
       "890            AB  79838293.0  \n",
       "891            AB  79838293.0  \n",
       "892            AB  79838293.0  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check values not in list (show spacing)\n",
    "trans_df.loc[~trans_df['k_symbol'].isin([\"POJISTNE\", \"SLUZBY\", \"UROK\", \"SANKC. UROK\", \"SIPO\", \"DUCHOD\", \"UVER\", np.nan]) , :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [trans_id, account_id, date, type, operation, amount, balance, k_symbol, bank, account]\n",
       "Index: []"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill spacing with nan\n",
    "trans_df = trans_df.replace(' ', np.nan)\n",
    "# fill None with nan\n",
    "trans_df = trans_df.fillna(value=np.nan)\n",
    "# check\n",
    "trans_df.loc[~trans_df['k_symbol'].isin([\"POJISTNE\", \"SLUZBY\", \"UROK\", \"SANKC. UROK\", \"SIPO\", \"DUCHOD\", \"UVER\", np.nan]) , :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "type have class \"PRIJEM\", \"VYDAJ\". Also \"VYBER\" (not show in https://web.archive.org/web/20180506035658/http://lisp.vse.cz/pkdd99/Challenge/berka.htm)\n",
    "\n",
    "include \"VYBER\" to \"VYDAJ\" as withdrawal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276</td>\n",
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       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>279</td>\n",
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       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>697</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>3530483</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191318</th>\n",
       "      <td>3424092</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-08</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>56</td>\n",
       "      <td>51420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "      <td>78507822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191284</th>\n",
       "      <td>3424044</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-10</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>4780</td>\n",
       "      <td>46640</td>\n",
       "      <td>household</td>\n",
       "      <td>YZ</td>\n",
       "      <td>70641225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191420</th>\n",
       "      <td>3424236</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-12</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>5392</td>\n",
       "      <td>41248</td>\n",
       "      <td>loan</td>\n",
       "      <td>MN</td>\n",
       "      <td>61540514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191425</th>\n",
       "      <td>3424248</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-19</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>2880</td>\n",
       "      <td>38368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191555</th>\n",
       "      <td>3530036</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163</td>\n",
       "      <td>38531</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191556 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        trans_id  account_id       date        type  \\\n",
       "0            276           2 1993-02-26      credit   \n",
       "1            279           2 1993-03-12      credit   \n",
       "338          697           2 1993-03-28      credit   \n",
       "408      3530483           2 1993-03-31      credit   \n",
       "2            280           2 1993-04-12      credit   \n",
       "...          ...         ...        ...         ...   \n",
       "191318   3424092       11362 1998-12-08  withdrawal   \n",
       "191284   3424044       11362 1998-12-10  withdrawal   \n",
       "191420   3424236       11362 1998-12-12  withdrawal   \n",
       "191425   3424248       11362 1998-12-19  withdrawal   \n",
       "191555   3530036       11362 1998-12-31      credit   \n",
       "\n",
       "                          operation  amount  balance           k_symbol bank  \\\n",
       "0                    credit_in_cash    1100     1100                NaN  NaN   \n",
       "1       collection_from_anotherbank   20236    21336                NaN   ST   \n",
       "338                  credit_in_cash    3700    25036                NaN  NaN   \n",
       "408                             NaN      14    25050  interest_credited  NaN   \n",
       "2       collection_from_anotherbank   20236    45286                NaN   ST   \n",
       "...                             ...     ...      ...                ...  ...   \n",
       "191318    remittance_to_anotherbank      56    51420                NaN   MN   \n",
       "191284    remittance_to_anotherbank    4780    46640          household   YZ   \n",
       "191420    remittance_to_anotherbank    5392    41248               loan   MN   \n",
       "191425           withdrawal_in_cash    2880    38368                NaN  NaN   \n",
       "191555                          NaN     163    38531  interest_credited  NaN   \n",
       "\n",
       "           account  \n",
       "0              NaN  \n",
       "1       66487163.0  \n",
       "338            NaN  \n",
       "408            NaN  \n",
       "2       66487163.0  \n",
       "...            ...  \n",
       "191318  78507822.0  \n",
       "191284  70641225.0  \n",
       "191420  61540514.0  \n",
       "191425         NaN  \n",
       "191555         NaN  \n",
       "\n",
       "[191556 rows x 10 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df = trans_df.sort_values(by=['account_id', 'date'])\n",
    "trans_df = trans_df.copy()\n",
    "# type\n",
    "trans_df.loc[trans_df['type']==\"PRIJEM\", 'type'] = 'credit'\n",
    "trans_df.loc[trans_df['type']==\"VYDAJ\", 'type'] = 'withdrawal'\n",
    "trans_df.loc[trans_df['type']==\"VYBER\", 'type'] = 'withdrawal'\n",
    "\n",
    "# operation\n",
    "trans_df.loc[trans_df['operation']==\"VYBER KARTOU\", 'operation'] = 'credit_card_withdrawal'\n",
    "trans_df.loc[trans_df['operation']==\"VKLAD\", 'operation'] = 'credit_in_cash'\n",
    "trans_df.loc[trans_df['operation']==\"PREVOD Z UCTU\", 'operation'] = 'collection_from_anotherbank'\n",
    "trans_df.loc[trans_df['operation']==\"VYBER\", 'operation'] = 'withdrawal_in_cash'\n",
    "trans_df.loc[trans_df['operation']==\"PREVOD NA UCET\", 'operation'] = 'remittance_to_anotherbank'\n",
    "\n",
    "# k_symbol\n",
    "trans_df.loc[trans_df['k_symbol']==\"POJISTNE\", 'k_symbol'] = 'insurrance'\n",
    "trans_df.loc[trans_df['k_symbol']==\"SLUZBY\", 'k_symbol'] = 'payment_for_statement'\n",
    "trans_df.loc[trans_df['k_symbol']==\"UROK\", 'k_symbol'] = 'interest_credited'\n",
    "trans_df.loc[trans_df['k_symbol']==\"SANKC. UROK\", 'k_symbol'] = 'sanction_interest_neg_bal'\n",
    "trans_df.loc[trans_df['k_symbol']==\"SIPO\", 'k_symbol'] = 'household'\n",
    "trans_df.loc[trans_df['k_symbol']==\"DUCHOD\", 'k_symbol'] = 'old-age_pension'\n",
    "trans_df.loc[trans_df['k_symbol']==\"UVER\", 'k_symbol'] = 'loan'\n",
    "\n",
    "# date\n",
    "trans_df['date'] = str_to_datetime(trans_df['date'])\n",
    "\n",
    "trans_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add_prefix_to_colnames(trans_df, prefix='trans_').to_csv('table_csv/transaction.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### money in, money out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
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       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>697</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>3530483</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trans_id  account_id       date    type                    operation  \\\n",
       "0         276           2 1993-02-26  credit               credit_in_cash   \n",
       "1         279           2 1993-03-12  credit  collection_from_anotherbank   \n",
       "338       697           2 1993-03-28  credit               credit_in_cash   \n",
       "408   3530483           2 1993-03-31  credit                          NaN   \n",
       "2         280           2 1993-04-12  credit  collection_from_anotherbank   \n",
       "\n",
       "     amount  balance           k_symbol bank     account  \n",
       "0      1100     1100                NaN  NaN         NaN  \n",
       "1     20236    21336                NaN   ST  66487163.0  \n",
       "338    3700    25036                NaN  NaN         NaN  \n",
       "408      14    25050  interest_credited  NaN         NaN  \n",
       "2     20236    45286                NaN   ST  66487163.0  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_in = trans_df.copy()\n",
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
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       "      <th>k_symbol</th>\n",
       "      <th>bank</th>\n",
       "      <th>account</th>\n",
       "      <th>amount_in</th>\n",
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       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
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       "      <td>20236.0</td>\n",
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       "      <th>338</th>\n",
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       "      <td>credit</td>\n",
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       "      <th>408</th>\n",
       "      <td>3530483</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>credit</td>\n",
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       "      <td>66487163.0</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trans_id  account_id       date    type                    operation  \\\n",
       "0         276           2 1993-02-26  credit               credit_in_cash   \n",
       "1         279           2 1993-03-12  credit  collection_from_anotherbank   \n",
       "338       697           2 1993-03-28  credit               credit_in_cash   \n",
       "408   3530483           2 1993-03-31  credit                          NaN   \n",
       "2         280           2 1993-04-12  credit  collection_from_anotherbank   \n",
       "\n",
       "     amount  balance           k_symbol bank     account  amount_in  \\\n",
       "0      1100     1100                NaN  NaN         NaN     1100.0   \n",
       "1     20236    21336                NaN   ST  66487163.0    20236.0   \n",
       "338    3700    25036                NaN  NaN         NaN     3700.0   \n",
       "408      14    25050  interest_credited  NaN         NaN       14.0   \n",
       "2     20236    45286                NaN   ST  66487163.0    20236.0   \n",
       "\n",
       "     amount_out  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "338         NaN  \n",
       "408         NaN  \n",
       "2           NaN  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def money_in_out(df, focus_colname, cond_colname='type', in_colname='credit', out_colname='withdrawal'):\n",
    "    money_in = df[df[cond_colname] == in_colname][focus_colname]\n",
    "    money_out = df[df[cond_colname] == out_colname][focus_colname]\n",
    "\n",
    "    return [money_in, money_out]\n",
    "\n",
    "trans_df_in.loc[trans_df_in['type'] == 'credit', 'amount_in'] = money_in_out(trans_df_in, focus_colname='amount')[0] # trans_df_before_loan[trans_df_before_loan['type'] == 'credit']['amount']\n",
    "trans_df_in.loc[trans_df_in['type'] == 'withdrawal', 'amount_out'] = money_in_out(trans_df_in, focus_colname='amount')[1]\n",
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
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       "      <td>25050</td>\n",
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       "      <td>3424092</td>\n",
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       "      <td>1998-12-08</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>56</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>191284</th>\n",
       "      <td>3424044</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-10</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>4780</td>\n",
       "      <td>46640</td>\n",
       "      <td>household</td>\n",
       "      <td>YZ</td>\n",
       "      <td>70641225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191420</th>\n",
       "      <td>3424236</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-12</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>5392</td>\n",
       "      <td>41248</td>\n",
       "      <td>loan</td>\n",
       "      <td>MN</td>\n",
       "      <td>61540514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191425</th>\n",
       "      <td>3424248</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-19</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>2880</td>\n",
       "      <td>38368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191555</th>\n",
       "      <td>3530036</td>\n",
       "      <td>11362</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163</td>\n",
       "      <td>38531</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191556 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        trans_id  account_id       date        type  \\\n",
       "0            276           2 1993-02-26      credit   \n",
       "1            279           2 1993-03-12      credit   \n",
       "338          697           2 1993-03-28      credit   \n",
       "408      3530483           2 1993-03-31      credit   \n",
       "2            280           2 1993-04-12      credit   \n",
       "...          ...         ...        ...         ...   \n",
       "191318   3424092       11362 1998-12-08  withdrawal   \n",
       "191284   3424044       11362 1998-12-10  withdrawal   \n",
       "191420   3424236       11362 1998-12-12  withdrawal   \n",
       "191425   3424248       11362 1998-12-19  withdrawal   \n",
       "191555   3530036       11362 1998-12-31      credit   \n",
       "\n",
       "                          operation  amount  balance           k_symbol bank  \\\n",
       "0                    credit_in_cash    1100     1100                NaN  NaN   \n",
       "1       collection_from_anotherbank   20236    21336                NaN   ST   \n",
       "338                  credit_in_cash    3700    25036                NaN  NaN   \n",
       "408                             NaN      14    25050  interest_credited  NaN   \n",
       "2       collection_from_anotherbank   20236    45286                NaN   ST   \n",
       "...                             ...     ...      ...                ...  ...   \n",
       "191318    remittance_to_anotherbank      56    51420                NaN   MN   \n",
       "191284    remittance_to_anotherbank    4780    46640          household   YZ   \n",
       "191420    remittance_to_anotherbank    5392    41248               loan   MN   \n",
       "191425           withdrawal_in_cash    2880    38368                NaN  NaN   \n",
       "191555                          NaN     163    38531  interest_credited  NaN   \n",
       "\n",
       "           account  amount_in  amount_out  \n",
       "0              NaN     1100.0         0.0  \n",
       "1       66487163.0    20236.0         0.0  \n",
       "338            NaN     3700.0         0.0  \n",
       "408            NaN       14.0         0.0  \n",
       "2       66487163.0    20236.0         0.0  \n",
       "...            ...        ...         ...  \n",
       "191318  78507822.0        0.0        56.0  \n",
       "191284  70641225.0        0.0      4780.0  \n",
       "191420  61540514.0        0.0      5392.0  \n",
       "191425         NaN        0.0      2880.0  \n",
       "191555         NaN      163.0         0.0  \n",
       "\n",
       "[191556 rows x 12 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace amount in out nan with 0\n",
    "trans_df_in.loc[:, ['amount_in', 'amount_out']] = trans_df_in.loc[:, ['amount_in', 'amount_out']].replace(np.nan, 0)\n",
    "trans_df_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Focus only transaction before loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>account_id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1993-02-26</td>\n",
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       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>3530483</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST</td>\n",
       "      <td>66487163.0</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trans_id  account_id       date    type                    operation  \\\n",
       "0         276           2 1993-02-26  credit               credit_in_cash   \n",
       "1         279           2 1993-03-12  credit  collection_from_anotherbank   \n",
       "338       697           2 1993-03-28  credit               credit_in_cash   \n",
       "408   3530483           2 1993-03-31  credit                          NaN   \n",
       "2         280           2 1993-04-12  credit  collection_from_anotherbank   \n",
       "\n",
       "     amount  balance           k_symbol bank     account  amount_in  \\\n",
       "0      1100     1100                NaN  NaN         NaN     1100.0   \n",
       "1     20236    21336                NaN   ST  66487163.0    20236.0   \n",
       "338    3700    25036                NaN  NaN         NaN     3700.0   \n",
       "408      14    25050  interest_credited  NaN         NaN       14.0   \n",
       "2     20236    45286                NaN   ST  66487163.0    20236.0   \n",
       "\n",
       "     amount_out  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "338         0.0  \n",
       "408         0.0  \n",
       "2           0.0  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unnecessary features\n",
    "trans_df_in = trans_df_in.drop(['trans_id', 'bank', 'account'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54694, 11)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1993-02-26</td>\n",
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       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>283</td>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id       date    type                    operation  amount  \\\n",
       "0           2 1993-02-26  credit               credit_in_cash    1100   \n",
       "1           2 1993-03-12  credit  collection_from_anotherbank   20236   \n",
       "2           2 1993-03-28  credit               credit_in_cash    3700   \n",
       "3           2 1993-03-31  credit                          NaN      14   \n",
       "4           2 1993-04-12  credit  collection_from_anotherbank   20236   \n",
       "\n",
       "   balance           k_symbol  amount_in  amount_out  loan_date  \\\n",
       "0     1100                NaN     1100.0         0.0 1994-01-05   \n",
       "1    21336                NaN    20236.0         0.0 1994-01-05   \n",
       "2    25036                NaN     3700.0         0.0 1994-01-05   \n",
       "3    25050  interest_credited       14.0         0.0 1994-01-05   \n",
       "4    45286                NaN    20236.0         0.0 1994-01-05   \n",
       "\n",
       "   day_after_loan  \n",
       "0             313  \n",
       "1             299  \n",
       "2             283  \n",
       "3             280  \n",
       "4             268  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_before_loan = pd.merge(trans_df_in, loan_df_[['account_id', 'loan_date']], how='left', on='account_id')\n",
    "trans_df_before_loan['day_after_loan'] = day_to_int(trans_df_before_loan['loan_date'] - trans_df_before_loan['date'])\n",
    "\n",
    "# after loan\n",
    "trans_df_after_loan = trans_df_before_loan[trans_df_before_loan['day_after_loan']<=0]\n",
    "# before loan\n",
    "trans_df_before_loan = trans_df_before_loan[trans_df_before_loan['day_after_loan']>0]\n",
    "\n",
    "# trans_df_before_loan = trans_df_before_loan.drop(['day_after_loan', 'loan_date'], axis=1)\n",
    "print(trans_df_before_loan.shape)\n",
    "trans_df_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(136862, 11)\n"
     ]
    },
    {
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       "      <td>3373</td>\n",
       "      <td>35338</td>\n",
       "      <td>loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3373.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>-7</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-12</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>2100</td>\n",
       "      <td>38711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>-7</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-18</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>2400</td>\n",
       "      <td>32938</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2400.0</td>\n",
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       "      <td>-13</td>\n",
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      ],
      "text/plain": [
       "    account_id       date        type                    operation  amount  \\\n",
       "54           2 1994-01-05  withdrawal    remittance_to_anotherbank    7266   \n",
       "55           2 1994-01-12      credit  collection_from_anotherbank   20236   \n",
       "56           2 1994-01-12  withdrawal    remittance_to_anotherbank    3373   \n",
       "57           2 1994-01-12  withdrawal           withdrawal_in_cash    2100   \n",
       "58           2 1994-01-18  withdrawal           withdrawal_in_cash    2400   \n",
       "\n",
       "    balance   k_symbol  amount_in  amount_out  loan_date  day_after_loan  \n",
       "54    20575  household        0.0      7266.0 1994-01-05               0  \n",
       "55    40811        NaN    20236.0         0.0 1994-01-05              -7  \n",
       "56    35338       loan        0.0      3373.0 1994-01-05              -7  \n",
       "57    38711        NaN        0.0      2100.0 1994-01-05              -7  \n",
       "58    32938        NaN        0.0      2400.0 1994-01-05             -13  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trans_df_after_loan = trans_df_after_loan.drop(['day_after_loan', 'loan_date'], axis=1)\n",
    "print(trans_df_after_loan.shape)\n",
    "trans_df_after_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(trans_df_before_loan['type'].value_counts(), '\\n')\n",
    "# print(trans_df_before_loan['operation'].value_counts(), '\\n')\n",
    "# print(trans_df_before_loan['k_symbol'].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(trans_df_after_loan['type'].value_counts(), '\\n')\n",
    "# print(trans_df_after_loan['operation'].value_counts(), '\\n')\n",
    "# print(trans_df_after_loan['k_symbol'].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_df_out = trans_df_before_loan.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trans_df_before_loan.to_csv('data/trans_before_loan.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trans_df_after_loan.to_csv('data/trans_after_loan.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Operation & k_symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_df_in = trans_df_out.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>Others</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>Others</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>Others</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>Others</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id       date    type                    operation  amount  \\\n",
       "0           2 1993-02-26  credit               credit_in_cash    1100   \n",
       "1           2 1993-03-12  credit  collection_from_anotherbank   20236   \n",
       "2           2 1993-03-28  credit               credit_in_cash    3700   \n",
       "3           2 1993-03-31  credit                       Others      14   \n",
       "4           2 1993-04-12  credit  collection_from_anotherbank   20236   \n",
       "\n",
       "   balance           k_symbol  amount_in  amount_out  loan_date  \\\n",
       "0     1100             Others     1100.0         0.0 1994-01-05   \n",
       "1    21336             Others    20236.0         0.0 1994-01-05   \n",
       "2    25036             Others     3700.0         0.0 1994-01-05   \n",
       "3    25050  interest_credited       14.0         0.0 1994-01-05   \n",
       "4    45286             Others    20236.0         0.0 1994-01-05   \n",
       "\n",
       "   day_after_loan  \n",
       "0             313  \n",
       "1             299  \n",
       "2             283  \n",
       "3             280  \n",
       "4             268  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_in['operation'].fillna('Others', inplace=True)\n",
    "trans_df_in['k_symbol'].fillna('Others', inplace=True)\n",
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=trans_df_in, x='type', hue='operation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=trans_df_in, x='type', hue='k_symbol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "sns.countplot(data=trans_df_in, x='operation', hue='k_symbol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The operation categorical simply tells us how money enters and leaves.\n",
    "As a result, it is unimportant. So we don't mind the operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_df_out = trans_df_in.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monthly payment (permanent order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "focus only monthly transaction. \\\n",
    "We can see this using k_symbol.\n",
    "1. one-hot encoding to k_symbol\n",
    "\n",
    "2. Create features for total monthly payments \n",
    "(count sanction, money amount in each k_symbol) \n",
    "\n",
    "*This tells us how much each account has paid us and whether or not they have been sanctioned.*\n",
    "\n",
    "3. Create features for current monthly payments by determining the previous month's payment for each account. (amount of money paid in each k symbol last month)\n",
    "\n",
    "*This tells us what you have to pay each month and how much you have to pay now.*\n",
    "\n",
    "4. merge Total monthly payment features and Current monthly payment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20272, 11)\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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>110</td>\n",
       "      <td>34395</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-05-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>145</td>\n",
       "      <td>37176</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>160</td>\n",
       "      <td>45290</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-07-31</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>15</td>\n",
       "      <td>39069</td>\n",
       "      <td>payment_for_statement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id       date        type           operation  amount  balance  \\\n",
       "3            2 1993-03-31      credit              Others      14    25050   \n",
       "6            2 1993-04-30      credit              Others     110    34395   \n",
       "9            2 1993-05-31      credit              Others     145    37176   \n",
       "12           2 1993-06-30      credit              Others     160    45290   \n",
       "17           2 1993-07-31  withdrawal  withdrawal_in_cash      15    39069   \n",
       "\n",
       "                 k_symbol  amount_in  amount_out  loan_date  day_after_loan  \n",
       "3       interest_credited       14.0         0.0 1994-01-05             280  \n",
       "6       interest_credited      110.0         0.0 1994-01-05             250  \n",
       "9       interest_credited      145.0         0.0 1994-01-05             219  \n",
       "12      interest_credited      160.0         0.0 1994-01-05             189  \n",
       "17  payment_for_statement        0.0        15.0 1994-01-05             158  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_in = trans_df_out[trans_df_out['k_symbol']!='Others']\n",
    "print(trans_df_in.shape)\n",
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_df_ = trans_df_in.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>out_household</th>\n",
       "      <th>out_insurrance</th>\n",
       "      <th>in_interest_credited</th>\n",
       "      <th>out_payment_for_statement</th>\n",
       "      <th>out_sanction_interest_neg_bal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>110</td>\n",
       "      <td>34395</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-05-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>145</td>\n",
       "      <td>37176</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>160</td>\n",
       "      <td>45290</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-07-31</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>15</td>\n",
       "      <td>39069</td>\n",
       "      <td>payment_for_statement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id       date        type           operation  amount  balance  \\\n",
       "3            2 1993-03-31      credit              Others      14    25050   \n",
       "6            2 1993-04-30      credit              Others     110    34395   \n",
       "9            2 1993-05-31      credit              Others     145    37176   \n",
       "12           2 1993-06-30      credit              Others     160    45290   \n",
       "17           2 1993-07-31  withdrawal  withdrawal_in_cash      15    39069   \n",
       "\n",
       "                 k_symbol  amount_in  amount_out  loan_date  day_after_loan  \\\n",
       "3       interest_credited       14.0         0.0 1994-01-05             280   \n",
       "6       interest_credited      110.0         0.0 1994-01-05             250   \n",
       "9       interest_credited      145.0         0.0 1994-01-05             219   \n",
       "12      interest_credited      160.0         0.0 1994-01-05             189   \n",
       "17  payment_for_statement        0.0        15.0 1994-01-05             158   \n",
       "\n",
       "    out_household  out_insurrance  in_interest_credited  \\\n",
       "3               0               0                     1   \n",
       "6               0               0                     1   \n",
       "9               0               0                     1   \n",
       "12              0               0                     1   \n",
       "17              0               0                     0   \n",
       "\n",
       "    out_payment_for_statement  out_sanction_interest_neg_bal  \n",
       "3                           0                              0  \n",
       "6                           0                              0  \n",
       "9                           0                              0  \n",
       "12                          0                              0  \n",
       "17                          1                              0  "
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# onehot\n",
    "trans_df_ = onehot(trans_df_, col_name='k_symbol', prefix='out',drop=False)\n",
    "trans_df_ = trans_df_.rename(columns={'out_interest_credited':'in_interest_credited'})\n",
    "trans_df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>out_household</th>\n",
       "      <th>out_insurrance</th>\n",
       "      <th>in_interest_credited</th>\n",
       "      <th>out_payment_for_statement</th>\n",
       "      <th>out_sanction_interest_neg_bal</th>\n",
       "      <th>count_out_sanction_interest_neg_bal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>110</td>\n",
       "      <td>34395</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-05-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>145</td>\n",
       "      <td>37176</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-30</td>\n",
       "      <td>credit</td>\n",
       "      <td>Others</td>\n",
       "      <td>160</td>\n",
       "      <td>45290</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-07-31</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>15</td>\n",
       "      <td>39069</td>\n",
       "      <td>payment_for_statement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id       date        type           operation  amount  balance  \\\n",
       "3            2 1993-03-31      credit              Others      14    25050   \n",
       "6            2 1993-04-30      credit              Others     110    34395   \n",
       "9            2 1993-05-31      credit              Others     145    37176   \n",
       "12           2 1993-06-30      credit              Others     160    45290   \n",
       "17           2 1993-07-31  withdrawal  withdrawal_in_cash      15    39069   \n",
       "\n",
       "                 k_symbol  amount_in  amount_out  loan_date  day_after_loan  \\\n",
       "3       interest_credited       14.0         0.0 1994-01-05             280   \n",
       "6       interest_credited      110.0         0.0 1994-01-05             250   \n",
       "9       interest_credited      145.0         0.0 1994-01-05             219   \n",
       "12      interest_credited      160.0         0.0 1994-01-05             189   \n",
       "17  payment_for_statement        0.0        15.0 1994-01-05             158   \n",
       "\n",
       "    out_household  out_insurrance  in_interest_credited  \\\n",
       "3             0.0             0.0                  14.0   \n",
       "6             0.0             0.0                 110.0   \n",
       "9             0.0             0.0                 145.0   \n",
       "12            0.0             0.0                 160.0   \n",
       "17            0.0             0.0                   0.0   \n",
       "\n",
       "    out_payment_for_statement  out_sanction_interest_neg_bal  \\\n",
       "3                         0.0                            0.0   \n",
       "6                         0.0                            0.0   \n",
       "9                         0.0                            0.0   \n",
       "12                        0.0                            0.0   \n",
       "17                       15.0                            0.0   \n",
       "\n",
       "    count_out_sanction_interest_neg_bal  \n",
       "3                                     0  \n",
       "6                                     0  \n",
       "9                                     0  \n",
       "12                                    0  \n",
       "17                                    0  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_trans = ['out_household', 'out_insurrance', 'out_payment_for_statement', 'out_sanction_interest_neg_bal']\n",
    "# count sanction if neg balance. \n",
    "trans_df_['count_out_sanction_interest_neg_bal'] = trans_df_['out_sanction_interest_neg_bal']\n",
    "\n",
    "trans_df_.loc[:, out_trans] = trans_df_[out_trans].multiply(trans_df_[\"amount_out\"], axis=0)\n",
    "\n",
    "\n",
    "trans_df_.loc[:, 'in_interest_credited'] = trans_df_['in_interest_credited'].multiply(trans_df_[\"amount_in\"], axis=0)\n",
    "trans_df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>sum_monthly_payment_out_household</th>\n",
       "      <th>sum_monthly_payment_out_insurrance</th>\n",
       "      <th>sum_monthly_payment_in_interest_credited</th>\n",
       "      <th>sum_monthly_payment_out_payment_for_statement</th>\n",
       "      <th>sum_monthly_payment_out_sanction_interest_neg_bal</th>\n",
       "      <th>count_out_sanction_interest_neg_bal</th>\n",
       "      <th>count_monthly_payment_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>37711</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>36420.0</td>\n",
       "      <td>36330.0</td>\n",
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       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>130355</td>\n",
       "      <td>2835.0</td>\n",
       "      <td>127520.0</td>\n",
       "      <td>127400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2835.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>102031</td>\n",
       "      <td>4087.0</td>\n",
       "      <td>97944.0</td>\n",
       "      <td>95438.0</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>4087.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>31436</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>29133.0</td>\n",
       "      <td>27804.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>29750</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>28636.0</td>\n",
       "      <td>27604.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>11327</td>\n",
       "      <td>24415</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>22248.0</td>\n",
       "      <td>22158.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>11328</td>\n",
       "      <td>3636</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>11349</td>\n",
       "      <td>11643</td>\n",
       "      <td>757.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11359</td>\n",
       "      <td>80673</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>77076.0</td>\n",
       "      <td>76806.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>11362</td>\n",
       "      <td>52691</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>51235.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>679 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account_id  sum_monthly_payment_amount  sum_monthly_payment_amount_in  \\\n",
       "0             2                       37711                         1291.0   \n",
       "1            19                      130355                         2835.0   \n",
       "2            25                      102031                         4087.0   \n",
       "3            37                       31436                         2303.0   \n",
       "4            38                       29750                         1114.0   \n",
       "..          ...                         ...                            ...   \n",
       "674       11327                       24415                         2167.0   \n",
       "675       11328                        3636                         3411.0   \n",
       "676       11349                       11643                          757.0   \n",
       "677       11359                       80673                         3597.0   \n",
       "678       11362                       52691                         1456.0   \n",
       "\n",
       "     sum_monthly_payment_amount_out  sum_monthly_payment_out_household  \\\n",
       "0                           36420.0                            36330.0   \n",
       "1                          127520.0                           127400.0   \n",
       "2                           97944.0                            95438.0   \n",
       "3                           29133.0                            27804.0   \n",
       "4                           28636.0                            27604.0   \n",
       "..                              ...                                ...   \n",
       "674                         22248.0                            22158.0   \n",
       "675                           225.0                                0.0   \n",
       "676                         10886.0                            10886.0   \n",
       "677                         77076.0                            76806.0   \n",
       "678                         51235.0                            47800.0   \n",
       "\n",
       "     sum_monthly_payment_out_insurrance  \\\n",
       "0                                   0.0   \n",
       "1                                   0.0   \n",
       "2                                2296.0   \n",
       "3                                1164.0   \n",
       "4                                 912.0   \n",
       "..                                  ...   \n",
       "674                                 0.0   \n",
       "675                                 0.0   \n",
       "676                                 0.0   \n",
       "677                                 0.0   \n",
       "678                              3300.0   \n",
       "\n",
       "     sum_monthly_payment_in_interest_credited  \\\n",
       "0                                      1291.0   \n",
       "1                                      2835.0   \n",
       "2                                      4087.0   \n",
       "3                                      2303.0   \n",
       "4                                      1114.0   \n",
       "..                                        ...   \n",
       "674                                    2167.0   \n",
       "675                                    3411.0   \n",
       "676                                     757.0   \n",
       "677                                    3597.0   \n",
       "678                                    1456.0   \n",
       "\n",
       "     sum_monthly_payment_out_payment_for_statement  \\\n",
       "0                                             90.0   \n",
       "1                                            120.0   \n",
       "2                                            210.0   \n",
       "3                                            165.0   \n",
       "4                                            120.0   \n",
       "..                                             ...   \n",
       "674                                           90.0   \n",
       "675                                          225.0   \n",
       "676                                            0.0   \n",
       "677                                          270.0   \n",
       "678                                          135.0   \n",
       "\n",
       "     sum_monthly_payment_out_sanction_interest_neg_bal  \\\n",
       "0                                                  0.0   \n",
       "1                                                  0.0   \n",
       "2                                                  0.0   \n",
       "3                                                  0.0   \n",
       "4                                                  0.0   \n",
       "..                                                 ...   \n",
       "674                                                0.0   \n",
       "675                                                0.0   \n",
       "676                                                0.0   \n",
       "677                                                0.0   \n",
       "678                                                0.0   \n",
       "\n",
       "     count_out_sanction_interest_neg_bal  count_monthly_payment_trans  \n",
       "0                                      0                           21  \n",
       "1                                      0                           40  \n",
       "2                                      0                           57  \n",
       "3                                      1                           50  \n",
       "4                                      0                           20  \n",
       "..                                   ...                          ...  \n",
       "674                                    0                           22  \n",
       "675                                    0                           34  \n",
       "676                                    0                            5  \n",
       "677                                    0                           57  \n",
       "678                                    0                           42  \n",
       "\n",
       "[679 rows x 11 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_monthly_payment = trans_df_.loc[:, ['account_id', 'amount', 'amount_in', 'amount_out', 'out_household', 'out_insurrance', 'in_interest_credited', 'out_payment_for_statement', 'out_sanction_interest_neg_bal', 'count_out_sanction_interest_neg_bal']].copy()\n",
    "trans_monthly_payment = trans_monthly_payment.groupby(by='account_id').sum().reset_index()\n",
    "\n",
    "trans_monthly_payment['count_monthly_trans'] = trans_df_.groupby(by='account_id', as_index=False).count()['amount']\n",
    "\n",
    "trans_monthly_payment = add_prefix_to_colnames(trans_monthly_payment, prefix='sum_monthly_payment_')\n",
    "trans_monthly_payment = trans_monthly_payment.rename(columns={'sum_monthly_payment_count_monthly_trans':'count_monthly_payment_trans', 'sum_monthly_payment_count_out_sanction_interest_neg_bal':'count_out_sanction_interest_neg_bal'})\n",
    "trans_monthly_payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_df_['last_month'] = trans_df_['loan_date'] - pd.Timedelta(days=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>current_monthly_payment_amount_out</th>\n",
       "      <th>current_monthly_payment_out_household</th>\n",
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       "      <td>138.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
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       "      <td>6817.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>2317.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>94.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>7159.0</td>\n",
       "      <td>6901.0</td>\n",
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       "      <td>15.0</td>\n",
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       "      <th>675</th>\n",
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       "      <td>15.0</td>\n",
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       "      <th>676</th>\n",
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       "      <td>10886.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
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       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11359</td>\n",
       "      <td>4533.0</td>\n",
       "      <td>4518.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>110.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>94.0</td>\n",
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      "text/plain": [
       "     account_id  current_monthly_payment_amount_out  \\\n",
       "0             2                              7281.0   \n",
       "1            19                             18215.0   \n",
       "2            25                              6996.0   \n",
       "3            37                              2429.0   \n",
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       "..          ...                                 ...   \n",
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       "675       11328                                15.0   \n",
       "676       11349                             10886.0   \n",
       "677       11359                              4533.0   \n",
       "678       11362                              5125.0   \n",
       "\n",
       "     current_monthly_payment_out_household  \\\n",
       "0                                   7266.0   \n",
       "1                                  18200.0   \n",
       "2                                   6817.0   \n",
       "3                                   2317.0   \n",
       "4                                   6901.0   \n",
       "..                                     ...   \n",
       "674                                 3693.0   \n",
       "675                                    0.0   \n",
       "676                                10886.0   \n",
       "677                                 4518.0   \n",
       "678                                 4780.0   \n",
       "\n",
       "     current_monthly_payment_out_insurrance  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                     164.0   \n",
       "3                                      97.0   \n",
       "4                                     228.0   \n",
       "..                                      ...   \n",
       "674                                     0.0   \n",
       "675                                     0.0   \n",
       "676                                     0.0   \n",
       "677                                     0.0   \n",
       "678                                   330.0   \n",
       "\n",
       "     current_monthly_payment_out_payment_for_statement  \\\n",
       "0                                                 15.0   \n",
       "1                                                 15.0   \n",
       "2                                                 15.0   \n",
       "3                                                 15.0   \n",
       "4                                                 30.0   \n",
       "..                                                 ...   \n",
       "674                                               15.0   \n",
       "675                                               15.0   \n",
       "676                                                0.0   \n",
       "677                                               15.0   \n",
       "678                                               15.0   \n",
       "\n",
       "     current_monthly_payment_in_interest_credited  \n",
       "0                                           138.0  \n",
       "1                                           168.0  \n",
       "2                                           372.0  \n",
       "3                                            94.0  \n",
       "4                                           117.0  \n",
       "..                                            ...  \n",
       "674                                         272.0  \n",
       "675                                         189.0  \n",
       "676                                         263.0  \n",
       "677                                         110.0  \n",
       "678                                          94.0  \n",
       "\n",
       "[679 rows x 6 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_monthly_payment = trans_df_[day_to_int(trans_df_['date']-trans_df_['last_month']) >= 0][['date', 'account_id', 'amount_out', 'out_household', 'out_insurrance', 'out_payment_for_statement', 'in_interest_credited']]\n",
    "current_monthly_payment = current_monthly_payment.groupby(by='account_id', as_index=False).sum()\n",
    "current_monthly_payment = add_prefix_to_colnames(current_monthly_payment, prefix='current_monthly_payment_')\n",
    "current_monthly_payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>372.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>31436</td>\n",
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       "      <td>165.0</td>\n",
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       "      <td>94.0</td>\n",
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       "      <td>2167.0</td>\n",
       "      <td>22248.0</td>\n",
       "      <td>22158.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>3708.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>11328</td>\n",
       "      <td>3636</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>11349</td>\n",
       "      <td>11643</td>\n",
       "      <td>757.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11359</td>\n",
       "      <td>80673</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>77076.0</td>\n",
       "      <td>76806.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>4533.0</td>\n",
       "      <td>4518.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>11362</td>\n",
       "      <td>52691</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>51235.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>5125.0</td>\n",
       "      <td>4780.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>679 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account_id  sum_monthly_payment_amount  sum_monthly_payment_amount_in  \\\n",
       "0             2                       37711                         1291.0   \n",
       "1            19                      130355                         2835.0   \n",
       "2            25                      102031                         4087.0   \n",
       "3            37                       31436                         2303.0   \n",
       "4            38                       29750                         1114.0   \n",
       "..          ...                         ...                            ...   \n",
       "674       11327                       24415                         2167.0   \n",
       "675       11328                        3636                         3411.0   \n",
       "676       11349                       11643                          757.0   \n",
       "677       11359                       80673                         3597.0   \n",
       "678       11362                       52691                         1456.0   \n",
       "\n",
       "     sum_monthly_payment_amount_out  sum_monthly_payment_out_household  \\\n",
       "0                           36420.0                            36330.0   \n",
       "1                          127520.0                           127400.0   \n",
       "2                           97944.0                            95438.0   \n",
       "3                           29133.0                            27804.0   \n",
       "4                           28636.0                            27604.0   \n",
       "..                              ...                                ...   \n",
       "674                         22248.0                            22158.0   \n",
       "675                           225.0                                0.0   \n",
       "676                         10886.0                            10886.0   \n",
       "677                         77076.0                            76806.0   \n",
       "678                         51235.0                            47800.0   \n",
       "\n",
       "     sum_monthly_payment_out_insurrance  \\\n",
       "0                                   0.0   \n",
       "1                                   0.0   \n",
       "2                                2296.0   \n",
       "3                                1164.0   \n",
       "4                                 912.0   \n",
       "..                                  ...   \n",
       "674                                 0.0   \n",
       "675                                 0.0   \n",
       "676                                 0.0   \n",
       "677                                 0.0   \n",
       "678                              3300.0   \n",
       "\n",
       "     sum_monthly_payment_in_interest_credited  \\\n",
       "0                                      1291.0   \n",
       "1                                      2835.0   \n",
       "2                                      4087.0   \n",
       "3                                      2303.0   \n",
       "4                                      1114.0   \n",
       "..                                        ...   \n",
       "674                                    2167.0   \n",
       "675                                    3411.0   \n",
       "676                                     757.0   \n",
       "677                                    3597.0   \n",
       "678                                    1456.0   \n",
       "\n",
       "     sum_monthly_payment_out_payment_for_statement  \\\n",
       "0                                             90.0   \n",
       "1                                            120.0   \n",
       "2                                            210.0   \n",
       "3                                            165.0   \n",
       "4                                            120.0   \n",
       "..                                             ...   \n",
       "674                                           90.0   \n",
       "675                                          225.0   \n",
       "676                                            0.0   \n",
       "677                                          270.0   \n",
       "678                                          135.0   \n",
       "\n",
       "     sum_monthly_payment_out_sanction_interest_neg_bal  \\\n",
       "0                                                  0.0   \n",
       "1                                                  0.0   \n",
       "2                                                  0.0   \n",
       "3                                                  0.0   \n",
       "4                                                  0.0   \n",
       "..                                                 ...   \n",
       "674                                                0.0   \n",
       "675                                                0.0   \n",
       "676                                                0.0   \n",
       "677                                                0.0   \n",
       "678                                                0.0   \n",
       "\n",
       "     count_out_sanction_interest_neg_bal  count_monthly_payment_trans  \\\n",
       "0                                      0                           21   \n",
       "1                                      0                           40   \n",
       "2                                      0                           57   \n",
       "3                                      1                           50   \n",
       "4                                      0                           20   \n",
       "..                                   ...                          ...   \n",
       "674                                    0                           22   \n",
       "675                                    0                           34   \n",
       "676                                    0                            5   \n",
       "677                                    0                           57   \n",
       "678                                    0                           42   \n",
       "\n",
       "     current_monthly_payment_amount_out  \\\n",
       "0                                7281.0   \n",
       "1                               18215.0   \n",
       "2                                6996.0   \n",
       "3                                2429.0   \n",
       "4                                7159.0   \n",
       "..                                  ...   \n",
       "674                              3708.0   \n",
       "675                                15.0   \n",
       "676                             10886.0   \n",
       "677                              4533.0   \n",
       "678                              5125.0   \n",
       "\n",
       "     current_monthly_payment_out_household  \\\n",
       "0                                   7266.0   \n",
       "1                                  18200.0   \n",
       "2                                   6817.0   \n",
       "3                                   2317.0   \n",
       "4                                   6901.0   \n",
       "..                                     ...   \n",
       "674                                 3693.0   \n",
       "675                                    0.0   \n",
       "676                                10886.0   \n",
       "677                                 4518.0   \n",
       "678                                 4780.0   \n",
       "\n",
       "     current_monthly_payment_out_insurrance  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                     164.0   \n",
       "3                                      97.0   \n",
       "4                                     228.0   \n",
       "..                                      ...   \n",
       "674                                     0.0   \n",
       "675                                     0.0   \n",
       "676                                     0.0   \n",
       "677                                     0.0   \n",
       "678                                   330.0   \n",
       "\n",
       "     current_monthly_payment_out_payment_for_statement  \\\n",
       "0                                                 15.0   \n",
       "1                                                 15.0   \n",
       "2                                                 15.0   \n",
       "3                                                 15.0   \n",
       "4                                                 30.0   \n",
       "..                                                 ...   \n",
       "674                                               15.0   \n",
       "675                                               15.0   \n",
       "676                                                0.0   \n",
       "677                                               15.0   \n",
       "678                                               15.0   \n",
       "\n",
       "     current_monthly_payment_in_interest_credited  \n",
       "0                                           138.0  \n",
       "1                                           168.0  \n",
       "2                                           372.0  \n",
       "3                                            94.0  \n",
       "4                                           117.0  \n",
       "..                                            ...  \n",
       "674                                         272.0  \n",
       "675                                         189.0  \n",
       "676                                         263.0  \n",
       "677                                         110.0  \n",
       "678                                          94.0  \n",
       "\n",
       "[679 rows x 16 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_monthly_payment_summary = pd.merge(trans_monthly_payment, current_monthly_payment, how='left', on=['account_id'])\n",
    "trans_monthly_payment_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace NaN with zero.\n",
    "df = pd.merge(df, trans_monthly_payment_summary, how='left', on=['account_id'])\n",
    "df = df.replace(np.nan, 0)\n",
    "df.isna().any().any() # check that there is nan or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('incloud_monthly_trans.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "divide transaction amount sum, count with day before loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t_list = ['sum_monthly_payment_amount', 'sum_monthly_payment_amount_in', 'sum_monthly_payment_amount_out', 'sum_monthly_payment_out_household','sum_monthly_payment_out_insurrance','sum_monthly_payment_in_interest_credited','sum_monthly_payment_out_payment_for_statement','sum_monthly_payment_out_sanction_interest_neg_bal','count_out_sanction_interest_neg_bal', 'count_monthly_payment_trans','current_monthly_payment_amount_out','current_monthly_payment_out_household','current_monthly_payment_out_insurrance','current_monthly_payment_out_payment_for_statement','current_monthly_payment_in_interest_credited']\n",
    "# t.loc[:, t_list] = t[t_list].div(t['day_before_loan'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t.to_csv('incloud_monthly_trans_normbyday.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create features \n",
    "* sum other amount\n",
    "* sum_other_amount_in, sum_other_amount_out\n",
    "* count other trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "การใช้จ่ายอื่น ๆ (ที่ไม่ใช่จ่ายรายเดือนใน k_symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34422, 11)\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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>Others</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>Others</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>Others</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>Others</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-27</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>11000</td>\n",
       "      <td>34286</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id       date        type                    operation  amount  \\\n",
       "0           2 1993-02-26      credit               credit_in_cash    1100   \n",
       "1           2 1993-03-12      credit  collection_from_anotherbank   20236   \n",
       "2           2 1993-03-28      credit               credit_in_cash    3700   \n",
       "4           2 1993-04-12      credit  collection_from_anotherbank   20236   \n",
       "5           2 1993-04-27  withdrawal           withdrawal_in_cash   11000   \n",
       "\n",
       "   balance k_symbol  amount_in  amount_out  loan_date  day_after_loan  \n",
       "0     1100   Others     1100.0         0.0 1994-01-05             313  \n",
       "1    21336   Others    20236.0         0.0 1994-01-05             299  \n",
       "2    25036   Others     3700.0         0.0 1994-01-05             283  \n",
       "4    45286   Others    20236.0         0.0 1994-01-05             268  \n",
       "5    34286   Others        0.0     11000.0 1994-01-05             253  "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_in = trans_df_out[trans_df_out['k_symbol']=='Others']\n",
    "print(trans_df_in.shape)\n",
    "trans_df_in.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>sum_other_amount</th>\n",
       "      <th>sum_other_amount_in</th>\n",
       "      <th>sum_other_amount_out</th>\n",
       "      <th>count_other_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>391823</td>\n",
       "      <td>227396.0</td>\n",
       "      <td>164427.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>338153</td>\n",
       "      <td>239345.0</td>\n",
       "      <td>98808.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1884662</td>\n",
       "      <td>1005069.0</td>\n",
       "      <td>879593.0</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>846920</td>\n",
       "      <td>445057.0</td>\n",
       "      <td>401863.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>229641</td>\n",
       "      <td>147290.0</td>\n",
       "      <td>82351.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11327</td>\n",
       "      <td>406396</td>\n",
       "      <td>244296.0</td>\n",
       "      <td>162100.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>11328</td>\n",
       "      <td>859072</td>\n",
       "      <td>443592.0</td>\n",
       "      <td>415480.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>11349</td>\n",
       "      <td>428034</td>\n",
       "      <td>231434.0</td>\n",
       "      <td>196600.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>11359</td>\n",
       "      <td>1199517</td>\n",
       "      <td>649336.0</td>\n",
       "      <td>550181.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>11362</td>\n",
       "      <td>434197</td>\n",
       "      <td>254087.0</td>\n",
       "      <td>180110.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account_id  sum_other_amount  sum_other_amount_in  sum_other_amount_out  \\\n",
       "0             2            391823             227396.0              164427.0   \n",
       "1            19            338153             239345.0               98808.0   \n",
       "2            25           1884662            1005069.0              879593.0   \n",
       "3            37            846920             445057.0              401863.0   \n",
       "4            38            229641             147290.0               82351.0   \n",
       "..          ...               ...                  ...                   ...   \n",
       "677       11327            406396             244296.0              162100.0   \n",
       "678       11328            859072             443592.0              415480.0   \n",
       "679       11349            428034             231434.0              196600.0   \n",
       "680       11359           1199517             649336.0              550181.0   \n",
       "681       11362            434197             254087.0              180110.0   \n",
       "\n",
       "     count_other_trans  \n",
       "0                   33  \n",
       "1                   40  \n",
       "2                  107  \n",
       "3                   66  \n",
       "4                   35  \n",
       "..                 ...  \n",
       "677                 32  \n",
       "678                 72  \n",
       "679                 13  \n",
       "680                 90  \n",
       "681                 65  \n",
       "\n",
       "[682 rows x 5 columns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_orthers = trans_df_in.loc[:, ['account_id', 'amount', 'amount_in', 'amount_out']].groupby(by='account_id').sum().reset_index()\n",
    "trans_orthers.loc[:, ['count_other_trans']] = trans_df_in.groupby(by='account_id', as_index=False).count()['amount']\n",
    "\n",
    "trans_orthers = add_prefix_to_colnames(trans_orthers, prefix='sum_other_')\n",
    "trans_orthers = trans_orthers.rename(columns={'sum_other_count_other_trans':'count_other_trans'})\n",
    "trans_orthers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_district_id</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>...</th>\n",
       "      <th>count_monthly_payment_trans</th>\n",
       "      <th>current_monthly_payment_amount_out</th>\n",
       "      <th>current_monthly_payment_out_household</th>\n",
       "      <th>current_monthly_payment_out_insurrance</th>\n",
       "      <th>current_monthly_payment_out_payment_for_statement</th>\n",
       "      <th>current_monthly_payment_in_interest_credited</th>\n",
       "      <th>sum_other_amount</th>\n",
       "      <th>sum_other_amount_in</th>\n",
       "      <th>sum_other_amount_out</th>\n",
       "      <th>count_other_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7281.0</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>391823</td>\n",
       "      <td>227396.0</td>\n",
       "      <td>164427.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>18215.0</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>338153</td>\n",
       "      <td>239345.0</td>\n",
       "      <td>98808.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4962</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>68</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6996.0</td>\n",
       "      <td>6817.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>1884662</td>\n",
       "      <td>1005069.0</td>\n",
       "      <td>879593.0</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>20</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>2317.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>846920</td>\n",
       "      <td>445057.0</td>\n",
       "      <td>401863.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7159.0</td>\n",
       "      <td>6901.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>229641</td>\n",
       "      <td>147290.0</td>\n",
       "      <td>82351.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0          4959           2 1994-01-05        80952             24   \n",
       "1          4961          19 1996-04-29        30276             12   \n",
       "2          4962          25 1997-12-08        30276             12   \n",
       "3          4967          37 1998-10-14       318480             60   \n",
       "4          4968          38 1998-04-19       110736             48   \n",
       "\n",
       "   loan_payments loan_status  acc_district_id acc_frequency  day_before_loan  \\\n",
       "0           3373           A                1       monthly              313   \n",
       "1           2523           B               21       monthly              388   \n",
       "2           2523           A               68       monthly              498   \n",
       "3           5308           D               20       monthly              422   \n",
       "4           2307           C               19        weekly              254   \n",
       "\n",
       "   ...  count_monthly_payment_trans  current_monthly_payment_amount_out  \\\n",
       "0  ...                         21.0                              7281.0   \n",
       "1  ...                         40.0                             18215.0   \n",
       "2  ...                         57.0                              6996.0   \n",
       "3  ...                         50.0                              2429.0   \n",
       "4  ...                         20.0                              7159.0   \n",
       "\n",
       "   current_monthly_payment_out_household  \\\n",
       "0                                 7266.0   \n",
       "1                                18200.0   \n",
       "2                                 6817.0   \n",
       "3                                 2317.0   \n",
       "4                                 6901.0   \n",
       "\n",
       "   current_monthly_payment_out_insurrance  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                   164.0   \n",
       "3                                    97.0   \n",
       "4                                   228.0   \n",
       "\n",
       "   current_monthly_payment_out_payment_for_statement  \\\n",
       "0                                               15.0   \n",
       "1                                               15.0   \n",
       "2                                               15.0   \n",
       "3                                               15.0   \n",
       "4                                               30.0   \n",
       "\n",
       "   current_monthly_payment_in_interest_credited  sum_other_amount  \\\n",
       "0                                         138.0            391823   \n",
       "1                                         168.0            338153   \n",
       "2                                         372.0           1884662   \n",
       "3                                          94.0            846920   \n",
       "4                                         117.0            229641   \n",
       "\n",
       "   sum_other_amount_in  sum_other_amount_out  count_other_trans  \n",
       "0             227396.0              164427.0                 33  \n",
       "1             239345.0               98808.0                 40  \n",
       "2            1005069.0              879593.0                107  \n",
       "3             445057.0              401863.0                 66  \n",
       "4             147290.0               82351.0                 35  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, trans_orthers, how='left', on=['account_id'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### balance latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id       date    type                    operation  amount  \\\n",
       "0           2 1993-02-26  credit               credit_in_cash    1100   \n",
       "1           2 1993-03-12  credit  collection_from_anotherbank   20236   \n",
       "2           2 1993-03-28  credit               credit_in_cash    3700   \n",
       "3           2 1993-03-31  credit                          NaN      14   \n",
       "4           2 1993-04-12  credit  collection_from_anotherbank   20236   \n",
       "\n",
       "   balance           k_symbol  amount_in  amount_out  loan_date  \\\n",
       "0     1100                NaN     1100.0         0.0 1994-01-05   \n",
       "1    21336                NaN    20236.0         0.0 1994-01-05   \n",
       "2    25036                NaN     3700.0         0.0 1994-01-05   \n",
       "3    25050  interest_credited       14.0         0.0 1994-01-05   \n",
       "4    45286                NaN    20236.0         0.0 1994-01-05   \n",
       "\n",
       "   day_after_loan  \n",
       "0             313  \n",
       "1             299  \n",
       "2             283  \n",
       "3             280  \n",
       "4             268  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_loan_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_district_id</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>...</th>\n",
       "      <th>current_monthly_payment_amount_out</th>\n",
       "      <th>current_monthly_payment_out_household</th>\n",
       "      <th>current_monthly_payment_out_insurrance</th>\n",
       "      <th>current_monthly_payment_out_payment_for_statement</th>\n",
       "      <th>current_monthly_payment_in_interest_credited</th>\n",
       "      <th>sum_other_amount</th>\n",
       "      <th>sum_other_amount_in</th>\n",
       "      <th>sum_other_amount_out</th>\n",
       "      <th>count_other_trans</th>\n",
       "      <th>balance_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4959</td>\n",
       "      <td>2</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>7281.0</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>391823</td>\n",
       "      <td>227396.0</td>\n",
       "      <td>164427.0</td>\n",
       "      <td>33</td>\n",
       "      <td>27855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4961</td>\n",
       "      <td>19</td>\n",
       "      <td>1996-04-29</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>...</td>\n",
       "      <td>18215.0</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>338153</td>\n",
       "      <td>239345.0</td>\n",
       "      <td>98808.0</td>\n",
       "      <td>40</td>\n",
       "      <td>15854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4962</td>\n",
       "      <td>25</td>\n",
       "      <td>1997-12-08</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>68</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>...</td>\n",
       "      <td>6996.0</td>\n",
       "      <td>6817.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>1884662</td>\n",
       "      <td>1005069.0</td>\n",
       "      <td>879593.0</td>\n",
       "      <td>107</td>\n",
       "      <td>31622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4967</td>\n",
       "      <td>37</td>\n",
       "      <td>1998-10-14</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>20</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>...</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>2317.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>846920</td>\n",
       "      <td>445057.0</td>\n",
       "      <td>401863.0</td>\n",
       "      <td>66</td>\n",
       "      <td>16368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4968</td>\n",
       "      <td>38</td>\n",
       "      <td>1998-04-19</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
       "      <td>7159.0</td>\n",
       "      <td>6901.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>229641</td>\n",
       "      <td>147290.0</td>\n",
       "      <td>82351.0</td>\n",
       "      <td>35</td>\n",
       "      <td>37417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0          4959           2 1994-01-05        80952             24   \n",
       "1          4961          19 1996-04-29        30276             12   \n",
       "2          4962          25 1997-12-08        30276             12   \n",
       "3          4967          37 1998-10-14       318480             60   \n",
       "4          4968          38 1998-04-19       110736             48   \n",
       "\n",
       "   loan_payments loan_status  acc_district_id acc_frequency  day_before_loan  \\\n",
       "0           3373           A                1       monthly              313   \n",
       "1           2523           B               21       monthly              388   \n",
       "2           2523           A               68       monthly              498   \n",
       "3           5308           D               20       monthly              422   \n",
       "4           2307           C               19        weekly              254   \n",
       "\n",
       "   ...  current_monthly_payment_amount_out  \\\n",
       "0  ...                              7281.0   \n",
       "1  ...                             18215.0   \n",
       "2  ...                              6996.0   \n",
       "3  ...                              2429.0   \n",
       "4  ...                              7159.0   \n",
       "\n",
       "   current_monthly_payment_out_household  \\\n",
       "0                                 7266.0   \n",
       "1                                18200.0   \n",
       "2                                 6817.0   \n",
       "3                                 2317.0   \n",
       "4                                 6901.0   \n",
       "\n",
       "   current_monthly_payment_out_insurrance  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                   164.0   \n",
       "3                                    97.0   \n",
       "4                                   228.0   \n",
       "\n",
       "   current_monthly_payment_out_payment_for_statement  \\\n",
       "0                                               15.0   \n",
       "1                                               15.0   \n",
       "2                                               15.0   \n",
       "3                                               15.0   \n",
       "4                                               30.0   \n",
       "\n",
       "   current_monthly_payment_in_interest_credited  sum_other_amount  \\\n",
       "0                                         138.0            391823   \n",
       "1                                         168.0            338153   \n",
       "2                                         372.0           1884662   \n",
       "3                                          94.0            846920   \n",
       "4                                         117.0            229641   \n",
       "\n",
       "   sum_other_amount_in  sum_other_amount_out  count_other_trans  \\\n",
       "0             227396.0              164427.0                 33   \n",
       "1             239345.0               98808.0                 40   \n",
       "2            1005069.0              879593.0                107   \n",
       "3             445057.0              401863.0                 66   \n",
       "4             147290.0               82351.0                 35   \n",
       "\n",
       "   balance_latest  \n",
       "0           27855  \n",
       "1           15854  \n",
       "2           31622  \n",
       "3           16368  \n",
       "4           37417  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_df_before_loan = trans_df_before_loan.sort_values(by=['account_id', 'date'])\n",
    "df['balance_latest'] = trans_df_before_loan.groupby('account_id').tail(1)['balance'].reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disp & Client & Card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(5369, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>OWNER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>OWNER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>DISPONENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>OWNER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>DISPONENT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   disp_id  client_id  account_id       type\n",
       "0        1          1           1      OWNER\n",
       "1        2          2           2      OWNER\n",
       "2        3          3           2  DISPONENT\n",
       "3        4          4           3      OWNER\n",
       "4        5          5           3  DISPONENT"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "disp_id       0\n",
       "client_id     0\n",
       "account_id    0\n",
       "type          0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(5369, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>client_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>district_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1970-12-13</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>1945-02-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>1940-10-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>1956-12-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "      <td>1960-07-03</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   client_id gender  birth_date  district_id\n",
       "0          1      F  1970-12-13           18\n",
       "1          2      M  1945-02-04            1\n",
       "2          3      F  1940-10-09            1\n",
       "3          4      M  1956-12-01            5\n",
       "4          5      F  1960-07-03            5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "client_id      0\n",
       "gender         0\n",
       "birth_date     0\n",
       "district_id    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(892, 4)\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>card_id</th>\n",
       "      <th>disp_id</th>\n",
       "      <th>type</th>\n",
       "      <th>issued</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>gold</td>\n",
       "      <td>1998-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>classic</td>\n",
       "      <td>1998-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>gold</td>\n",
       "      <td>1995-09-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>classic</td>\n",
       "      <td>1998-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>junior</td>\n",
       "      <td>1995-04-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   card_id  disp_id     type      issued\n",
       "0        1        9     gold  1998-10-16\n",
       "1        2       19  classic  1998-03-13\n",
       "2        3       41     gold  1995-09-03\n",
       "3        4       42  classic  1998-11-26\n",
       "4        5       51   junior  1995-04-24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "card_id    0\n",
       "disp_id    0\n",
       "type       0\n",
       "issued     0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_df(disp_df)\n",
    "summary_df(client_df)\n",
    "summary_df(card_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "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>disp_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>client_type</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>district_id</th>\n",
       "      <th>card_id</th>\n",
       "      <th>card_type</th>\n",
       "      <th>card_issued</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>OWNER</td>\n",
       "      <td>F</td>\n",
       "      <td>1970-12-13</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>OWNER</td>\n",
       "      <td>M</td>\n",
       "      <td>1945-02-04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>DISPONENT</td>\n",
       "      <td>F</td>\n",
       "      <td>1940-10-09</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>OWNER</td>\n",
       "      <td>M</td>\n",
       "      <td>1956-12-01</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>DISPONENT</td>\n",
       "      <td>F</td>\n",
       "      <td>1960-07-03</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   disp_id  client_id  account_id client_type gender  birth_date  district_id  \\\n",
       "0        1          1           1       OWNER      F  1970-12-13           18   \n",
       "1        2          2           2       OWNER      M  1945-02-04            1   \n",
       "2        3          3           2   DISPONENT      F  1940-10-09            1   \n",
       "3        4          4           3       OWNER      M  1956-12-01            5   \n",
       "4        5          5           3   DISPONENT      F  1960-07-03            5   \n",
       "\n",
       "   card_id card_type card_issued  \n",
       "0      NaN       NaN         NaN  \n",
       "1      NaN       NaN         NaN  \n",
       "2      NaN       NaN         NaN  \n",
       "3      NaN       NaN         NaN  \n",
       "4      NaN       NaN         NaN  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df = pd.merge(disp_df, client_df, how='left', on='client_id')\n",
    "dcc_df = pd.merge(dcc_df, card_df, how='left', on='disp_id')\n",
    "\n",
    "dcc_df = dcc_df.rename(columns={'type_x': 'client_type', 'type_y':'card_type', 'issued':'card_issued'})\n",
    "dcc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "birth_date -> age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>client_type</th>\n",
       "      <th>gender</th>\n",
       "      <th>card_type</th>\n",
       "      <th>card_issued</th>\n",
       "      <th>age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
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       "      <th>5364</th>\n",
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       "      <td>OWNER</td>\n",
       "      <td>M</td>\n",
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       "      <th>5367</th>\n",
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       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5369 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      client_id  account_id client_type gender card_type card_issued  age\n",
       "0             1           1       OWNER      F       NaN         NaN   29\n",
       "1             2           2       OWNER      M       NaN         NaN   54\n",
       "2             3           2   DISPONENT      F       NaN         NaN   59\n",
       "3             4           3       OWNER      M       NaN         NaN   43\n",
       "4             5           3   DISPONENT      F       NaN         NaN   39\n",
       "...         ...         ...         ...    ...       ...         ...  ...\n",
       "5364      13955       11349       OWNER      F       NaN         NaN   54\n",
       "5365      13956       11349   DISPONENT      M       NaN         NaN   56\n",
       "5366      13968       11359       OWNER      M   classic  1995-06-13   31\n",
       "5367      13971       11362       OWNER      F       NaN         NaN   37\n",
       "5368      13998       11382       OWNER      F       NaN         NaN   46\n",
       "\n",
       "[5369 rows x 7 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df['birth_date'] = pd.to_datetime(dcc_df['birth_date'], format='%Y-%m-%d')\n",
    "today = pd.to_datetime(\"1999-01-01\", format='%Y-%m-%d')\n",
    "dcc_df['age'] = [today.year - d.year for d in dcc_df['birth_date']]\n",
    "dcc_df = dcc_df.drop(['disp_id', 'birth_date', 'district_id', 'card_id'], axis=1)\n",
    "dcc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OWNER        4500\n",
       "DISPONENT     869\n",
       "Name: client_type, dtype: int64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df['client_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    2724\n",
       "F    2645\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "client_id is unique. So, there is no more client in 1 account.\n",
    "\n",
    "only have 1 account more client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [client_id, account_id, client_type, gender, card_type, card_issued, age]\n",
       "Index: []"
      ]
     },
     "execution_count": 316,
     "metadata": {},
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   ],
   "source": [
    "dcc_df[dcc_df.duplicated(['client_id'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "only owner can issue permanent orders and ask for a loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
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       "<p>4500 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      client_id  account_id client_type gender card_type card_issued  age  \\\n",
       "0             1           1       OWNER      F       NaN         NaN   29   \n",
       "1             2           2       OWNER      M       NaN         NaN   54   \n",
       "2             4           3       OWNER      M       NaN         NaN   43   \n",
       "3             6           4       OWNER      M       NaN         NaN   80   \n",
       "4             7           5       OWNER      M       NaN         NaN   70   \n",
       "...         ...         ...         ...    ...       ...         ...  ...   \n",
       "4495      13931       11333       OWNER      M       NaN         NaN   57   \n",
       "4496      13955       11349       OWNER      F       NaN         NaN   54   \n",
       "4497      13968       11359       OWNER      M   classic  1995-06-13   31   \n",
       "4498      13971       11362       OWNER      F       NaN         NaN   37   \n",
       "4499      13998       11382       OWNER      F       NaN         NaN   46   \n",
       "\n",
       "      all_client_mean_age  all_client_count  \n",
       "0                    29.0                 1  \n",
       "1                    56.5                 2  \n",
       "2                    41.0                 2  \n",
       "3                    80.0                 1  \n",
       "4                    70.0                 1  \n",
       "...                   ...               ...  \n",
       "4495                 57.0                 1  \n",
       "4496                 55.0                 2  \n",
       "4497                 31.0                 1  \n",
       "4498                 37.0                 1  \n",
       "4499                 46.0                 1  \n",
       "\n",
       "[4500 rows x 9 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df_ = dcc_df[dcc_df['client_type']=='OWNER'].reset_index(drop=True)\n",
    "\n",
    "dcc_df_['all_client_mean_age'] = dcc_df.groupby(by='account_id')['age'].mean().reset_index(drop=True)\n",
    "dcc_df_['all_client_count'] = dcc_df.groupby(by='account_id')['client_id'].count().reset_index(drop=True)\n",
    "dcc_df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
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       "      <td>46</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      account_id client_gender card_type  client_age  all_client_mean_age  \\\n",
       "0              1             F       NaN          29                 29.0   \n",
       "1              2             M       NaN          54                 56.5   \n",
       "2              3             M       NaN          43                 41.0   \n",
       "3              4             M       NaN          80                 80.0   \n",
       "4              5             M       NaN          70                 70.0   \n",
       "...          ...           ...       ...         ...                  ...   \n",
       "4495       11333             M       NaN          57                 57.0   \n",
       "4496       11349             F       NaN          54                 55.0   \n",
       "4497       11359             M   classic          31                 31.0   \n",
       "4498       11362             F       NaN          37                 37.0   \n",
       "4499       11382             F       NaN          46                 46.0   \n",
       "\n",
       "      all_client_count  \n",
       "0                    1  \n",
       "1                    2  \n",
       "2                    2  \n",
       "3                    1  \n",
       "4                    1  \n",
       "...                ...  \n",
       "4495                 1  \n",
       "4496                 2  \n",
       "4497                 1  \n",
       "4498                 1  \n",
       "4499                 1  \n",
       "\n",
       "[4500 rows x 6 columns]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df_ = dcc_df_.drop(['client_id', 'client_type', 'card_issued'], axis=1)\n",
    "dcc_df_ = dcc_df_.rename(columns={'gender':'client_gender', 'age':'client_age'})\n",
    "dcc_df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      account_id client_gender  client_age  all_client_mean_age  \\\n",
       "0              1             F          29                 29.0   \n",
       "1              2             M          54                 56.5   \n",
       "2              3             M          43                 41.0   \n",
       "3              4             M          80                 80.0   \n",
       "4              5             M          70                 70.0   \n",
       "...          ...           ...         ...                  ...   \n",
       "4495       11333             M          57                 57.0   \n",
       "4496       11349             F          54                 55.0   \n",
       "4497       11359             M          31                 31.0   \n",
       "4498       11362             F          37                 37.0   \n",
       "4499       11382             F          46                 46.0   \n",
       "\n",
       "      all_client_count  card_type_classic  card_type_gold  card_type_junior  \n",
       "0                    1                  0               0                 0  \n",
       "1                    2                  0               0                 0  \n",
       "2                    2                  0               0                 0  \n",
       "3                    1                  0               0                 0  \n",
       "4                    1                  0               0                 0  \n",
       "...                ...                ...             ...               ...  \n",
       "4495                 1                  0               0                 0  \n",
       "4496                 2                  0               0                 0  \n",
       "4497                 1                  1               0                 0  \n",
       "4498                 1                  0               0                 0  \n",
       "4499                 1                  0               0                 0  \n",
       "\n",
       "[4500 rows x 8 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcc_df_final = onehot(dcc_df_, col_name='card_type', prefix='card_type')\n",
    "# dcc_df_final = onehot(dcc_df_, col_name='client_gender', prefix='client_gender')\n",
    "\n",
    "dcc_df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, dcc_df_final, how='left', on='account_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
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       "      <td>675</td>\n",
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       "      <td>550181.0</td>\n",
       "      <td>90</td>\n",
       "      <td>25697</td>\n",
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       "      <td>31</td>\n",
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       "    <tr>\n",
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       "      <td>1996-12-27</td>\n",
       "      <td>129408</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     loan_loan_id  account_id  loan_date  loan_amount  loan_duration  \\\n",
       "0            4959           2 1994-01-05        80952             24   \n",
       "1            4961          19 1996-04-29        30276             12   \n",
       "2            4962          25 1997-12-08        30276             12   \n",
       "3            4967          37 1998-10-14       318480             60   \n",
       "4            4968          38 1998-04-19       110736             48   \n",
       "..            ...         ...        ...          ...            ...   \n",
       "677          7294       11327 1998-09-27        39168             24   \n",
       "678          7295       11328 1998-07-18       280440             60   \n",
       "679          7304       11349 1995-10-29       419880             60   \n",
       "680          7305       11359 1996-08-06        54024             12   \n",
       "681          7308       11362 1996-12-27       129408             24   \n",
       "\n",
       "     loan_payments loan_status  acc_district_id acc_frequency  \\\n",
       "0             3373           A                1       monthly   \n",
       "1             2523           B               21       monthly   \n",
       "2             2523           A               68       monthly   \n",
       "3             5308           D               20       monthly   \n",
       "4             2307           C               19        weekly   \n",
       "..             ...         ...              ...           ...   \n",
       "677           1632           C                7       monthly   \n",
       "678           4674           C               54       monthly   \n",
       "679           6998           C                1        weekly   \n",
       "680           4502           A               61       monthly   \n",
       "681           5392           A               67       monthly   \n",
       "\n",
       "     day_before_loan  ...  sum_other_amount_out  count_other_trans  \\\n",
       "0                313  ...              164427.0                 33   \n",
       "1                388  ...               98808.0                 40   \n",
       "2                498  ...              879593.0                107   \n",
       "3                422  ...              401863.0                 66   \n",
       "4                254  ...               82351.0                 35   \n",
       "..               ...  ...                   ...                ...   \n",
       "677              347  ...              162100.0                 32   \n",
       "678              620  ...              415480.0                 72   \n",
       "679              156  ...              196600.0                 13   \n",
       "680              675  ...              550181.0                 90   \n",
       "681              440  ...              180110.0                 65   \n",
       "\n",
       "     balance_latest  client_gender  client_age  all_client_mean_age  \\\n",
       "0             27855              M          54                 56.5   \n",
       "1             15854              F          60                 60.0   \n",
       "2             31622              M          37                 37.0   \n",
       "3             16368              M          47                 47.0   \n",
       "4             37417              F          59                 59.0   \n",
       "..              ...            ...         ...                  ...   \n",
       "677           62119              F          20                 20.0   \n",
       "678           31303              F          47                 47.0   \n",
       "679           24704              F          54                 55.0   \n",
       "680           25697              M          31                 31.0   \n",
       "681           24200              F          37                 37.0   \n",
       "\n",
       "     all_client_count  card_type_classic  card_type_gold  card_type_junior  \n",
       "0                   2                  0               0                 0  \n",
       "1                   1                  0               0                 0  \n",
       "2                   1                  0               0                 0  \n",
       "3                   1                  0               0                 0  \n",
       "4                   1                  0               0                 0  \n",
       "..                ...                ...             ...               ...  \n",
       "677                 1                  0               0                 0  \n",
       "678                 1                  0               0                 0  \n",
       "679                 2                  0               0                 0  \n",
       "680                 1                  1               0                 0  \n",
       "681                 1                  0               0                 0  \n",
       "\n",
       "[682 rows x 37 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>district_id</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hl.m. Praha</td>\n",
       "      <td>Prague</td>\n",
       "      <td>1204953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12541</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.43</td>\n",
       "      <td>167</td>\n",
       "      <td>85677.0</td>\n",
       "      <td>99107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Benesov</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>88884</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>8507</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.85</td>\n",
       "      <td>132</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>2674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Beroun</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>75232</td>\n",
       "      <td>55</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41.7</td>\n",
       "      <td>8980</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.21</td>\n",
       "      <td>111</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>2813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kladno</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>149893</td>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>9753</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>109</td>\n",
       "      <td>5244.0</td>\n",
       "      <td>5892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kolin</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>95616</td>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>51.4</td>\n",
       "      <td>9307</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.43</td>\n",
       "      <td>118</td>\n",
       "      <td>2616.0</td>\n",
       "      <td>3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Opava</td>\n",
       "      <td>north Moravia</td>\n",
       "      <td>182027</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>56.4</td>\n",
       "      <td>8746</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.74</td>\n",
       "      <td>90</td>\n",
       "      <td>4355.0</td>\n",
       "      <td>4433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Ostrava - mesto</td>\n",
       "      <td>north Moravia</td>\n",
       "      <td>323870</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10673</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.44</td>\n",
       "      <td>100</td>\n",
       "      <td>18782.0</td>\n",
       "      <td>18347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>Prerov</td>\n",
       "      <td>north Moravia</td>\n",
       "      <td>138032</td>\n",
       "      <td>67</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>64.6</td>\n",
       "      <td>8819</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.66</td>\n",
       "      <td>99</td>\n",
       "      <td>4063.0</td>\n",
       "      <td>4505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>Sumperk</td>\n",
       "      <td>north Moravia</td>\n",
       "      <td>127369</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>51.2</td>\n",
       "      <td>8369</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.88</td>\n",
       "      <td>107</td>\n",
       "      <td>3736.0</td>\n",
       "      <td>2807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>Vsetin</td>\n",
       "      <td>north Moravia</td>\n",
       "      <td>148545</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53.5</td>\n",
       "      <td>8909</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.56</td>\n",
       "      <td>113</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>3590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    district_id               A2               A3       A4  A5  A6  A7  A8  \\\n",
       "0             1      Hl.m. Praha           Prague  1204953   0   0   0   1   \n",
       "1             2          Benesov  central Bohemia    88884  80  26   6   2   \n",
       "2             3           Beroun  central Bohemia    75232  55  26   4   1   \n",
       "3             4           Kladno  central Bohemia   149893  63  29   6   2   \n",
       "4             5            Kolin  central Bohemia    95616  65  30   4   1   \n",
       "..          ...              ...              ...      ...  ..  ..  ..  ..   \n",
       "72           73            Opava    north Moravia   182027  17  49  12   2   \n",
       "73           74  Ostrava - mesto    north Moravia   323870   0   0   0   1   \n",
       "74           75           Prerov    north Moravia   138032  67  30   4   2   \n",
       "75           76          Sumperk    north Moravia   127369  31  32  13   2   \n",
       "76           77           Vsetin    north Moravia   148545   8  35  12   3   \n",
       "\n",
       "    A9    A10    A11  A12   A13  A14      A15    A16  \n",
       "0    1  100.0  12541  0.2  0.43  167  85677.0  99107  \n",
       "1    5   46.7   8507  1.6  1.85  132   2159.0   2674  \n",
       "2    5   41.7   8980  1.9  2.21  111   2824.0   2813  \n",
       "3    6   67.4   9753  4.6  5.05  109   5244.0   5892  \n",
       "4    6   51.4   9307  3.8  4.43  118   2616.0   3040  \n",
       "..  ..    ...    ...  ...   ...  ...      ...    ...  \n",
       "72   7   56.4   8746  3.3  3.74   90   4355.0   4433  \n",
       "73   1  100.0  10673  4.7  5.44  100  18782.0  18347  \n",
       "74   5   64.6   8819  5.3  5.66   99   4063.0   4505  \n",
       "75   7   51.2   8369  4.7  5.88  107   3736.0   2807  \n",
       "76   4   53.5   8909  4.0  5.56  113   3460.0   3590  \n",
       "\n",
       "[77 rows x 16 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>num_inhabitants</th>\n",
       "      <th>urban_rate</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>num_enterpreneurs_per1000inhabitants</th>\n",
       "      <th>unemploy_rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1204953</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12541</td>\n",
       "      <td>167</td>\n",
       "      <td>0.315</td>\n",
       "      <td>92392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>88884</td>\n",
       "      <td>0.467</td>\n",
       "      <td>8507</td>\n",
       "      <td>132</td>\n",
       "      <td>1.725</td>\n",
       "      <td>2416.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>75232</td>\n",
       "      <td>0.417</td>\n",
       "      <td>8980</td>\n",
       "      <td>111</td>\n",
       "      <td>2.055</td>\n",
       "      <td>2818.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>149893</td>\n",
       "      <td>0.674</td>\n",
       "      <td>9753</td>\n",
       "      <td>109</td>\n",
       "      <td>4.825</td>\n",
       "      <td>5568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>95616</td>\n",
       "      <td>0.514</td>\n",
       "      <td>9307</td>\n",
       "      <td>118</td>\n",
       "      <td>4.115</td>\n",
       "      <td>2828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>182027</td>\n",
       "      <td>0.564</td>\n",
       "      <td>8746</td>\n",
       "      <td>90</td>\n",
       "      <td>3.520</td>\n",
       "      <td>4394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>323870</td>\n",
       "      <td>1.000</td>\n",
       "      <td>10673</td>\n",
       "      <td>100</td>\n",
       "      <td>5.070</td>\n",
       "      <td>18564.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>138032</td>\n",
       "      <td>0.646</td>\n",
       "      <td>8819</td>\n",
       "      <td>99</td>\n",
       "      <td>5.480</td>\n",
       "      <td>4284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>127369</td>\n",
       "      <td>0.512</td>\n",
       "      <td>8369</td>\n",
       "      <td>107</td>\n",
       "      <td>5.290</td>\n",
       "      <td>3271.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>148545</td>\n",
       "      <td>0.535</td>\n",
       "      <td>8909</td>\n",
       "      <td>113</td>\n",
       "      <td>4.780</td>\n",
       "      <td>3525.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>77 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    district_id  num_inhabitants  urban_rate  avg_salary  \\\n",
       "0             1          1204953       1.000       12541   \n",
       "1             2            88884       0.467        8507   \n",
       "2             3            75232       0.417        8980   \n",
       "3             4           149893       0.674        9753   \n",
       "4             5            95616       0.514        9307   \n",
       "..          ...              ...         ...         ...   \n",
       "72           73           182027       0.564        8746   \n",
       "73           74           323870       1.000       10673   \n",
       "74           75           138032       0.646        8819   \n",
       "75           76           127369       0.512        8369   \n",
       "76           77           148545       0.535        8909   \n",
       "\n",
       "    num_enterpreneurs_per1000inhabitants  unemploy_rate  number_crimes  \n",
       "0                                    167          0.315        92392.0  \n",
       "1                                    132          1.725         2416.5  \n",
       "2                                    111          2.055         2818.5  \n",
       "3                                    109          4.825         5568.0  \n",
       "4                                    118          4.115         2828.0  \n",
       "..                                   ...            ...            ...  \n",
       "72                                    90          3.520         4394.0  \n",
       "73                                   100          5.070        18564.5  \n",
       "74                                    99          5.480         4284.0  \n",
       "75                                   107          5.290         3271.5  \n",
       "76                                   113          4.780         3525.0  \n",
       "\n",
       "[77 rows x 7 columns]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_district_feature = ['district_id', 'A4', 'A10', 'A11', 'A14']\n",
    "district_df_ = district_df[selected_district_feature]\n",
    "district_df_.loc[district_df_.index, ['unemploy_rate']] = district_df[['A12', 'A13']].mean(axis=1)\n",
    "district_df_.loc[district_df_.index, ['number_crimes']] = district_df[['A15', 'A16']].mean(axis=1)\n",
    "district_df_.loc[district_df_.index, ['A10']] = district_df_['A10'] / 100\n",
    "\n",
    "district_df_ = district_df_.rename(columns={'A4':'num_inhabitants', 'A10':'urban_rate', 'A11':'avg_salary', 'A14':'num_enterpreneurs_per1000inhabitants'})\n",
    "district_df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'acc_district_id':'district_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>all_client_count</th>\n",
       "      <th>card_type_classic</th>\n",
       "      <th>card_type_gold</th>\n",
       "      <th>card_type_junior</th>\n",
       "      <th>num_inhabitants</th>\n",
       "      <th>urban_rate</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>num_enterpreneurs_per1000inhabitants</th>\n",
       "      <th>unemploy_rate</th>\n",
       "      <th>number_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>37711.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>36420.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1204953</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12541</td>\n",
       "      <td>167</td>\n",
       "      <td>0.315</td>\n",
       "      <td>92392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>130355.0</td>\n",
       "      <td>2835.0</td>\n",
       "      <td>127520.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103347</td>\n",
       "      <td>0.670</td>\n",
       "      <td>9104</td>\n",
       "      <td>123</td>\n",
       "      <td>1.785</td>\n",
       "      <td>2326.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>102031.0</td>\n",
       "      <td>4087.0</td>\n",
       "      <td>97944.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>228848</td>\n",
       "      <td>0.572</td>\n",
       "      <td>9893</td>\n",
       "      <td>96</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>31436.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>29133.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70646</td>\n",
       "      <td>0.584</td>\n",
       "      <td>8547</td>\n",
       "      <td>120</td>\n",
       "      <td>3.120</td>\n",
       "      <td>1552.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>29750.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>28636.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51428</td>\n",
       "      <td>0.527</td>\n",
       "      <td>8402</td>\n",
       "      <td>120</td>\n",
       "      <td>3.540</td>\n",
       "      <td>1049.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0           2        80952             24           3373           A   \n",
       "1          19        30276             12           2523           B   \n",
       "2          25        30276             12           2523           A   \n",
       "3          37       318480             60           5308           D   \n",
       "4          38       110736             48           2307           C   \n",
       "\n",
       "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0       monthly              313                     37711.0   \n",
       "1       monthly              388                    130355.0   \n",
       "2       monthly              498                    102031.0   \n",
       "3       monthly              422                     31436.0   \n",
       "4        weekly              254                     29750.0   \n",
       "\n",
       "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                         1291.0                         36420.0  ...   \n",
       "1                         2835.0                        127520.0  ...   \n",
       "2                         4087.0                         97944.0  ...   \n",
       "3                         2303.0                         29133.0  ...   \n",
       "4                         1114.0                         28636.0  ...   \n",
       "\n",
       "   all_client_count  card_type_classic  card_type_gold  card_type_junior  \\\n",
       "0                 2                  0               0                 0   \n",
       "1                 1                  0               0                 0   \n",
       "2                 1                  0               0                 0   \n",
       "3                 1                  0               0                 0   \n",
       "4                 1                  0               0                 0   \n",
       "\n",
       "   num_inhabitants  urban_rate  avg_salary  \\\n",
       "0          1204953       1.000       12541   \n",
       "1           103347       0.670        9104   \n",
       "2           228848       0.572        9893   \n",
       "3            70646       0.584        8547   \n",
       "4            51428       0.527        8402   \n",
       "\n",
       "   num_enterpreneurs_per1000inhabitants  unemploy_rate  number_crimes  \n",
       "0                                   167          0.315        92392.0  \n",
       "1                                   123          1.785         2326.5  \n",
       "2                                    96          4.360         5755.0  \n",
       "3                                   120          3.120         1552.5  \n",
       "4                                   120          3.540         1049.0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(df, district_df_, how='left', on='district_id')\n",
    "final_df.drop(columns=['loan_loan_id', 'loan_date', 'district_id'], inplace=True)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize SUM, COUNT by day_before_loan\n",
    "Because we believe that if you use your account for a long time, the sum count should be large as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_id', 'loan_amount', 'loan_duration', 'loan_payments',\n",
       "       'loan_status', 'acc_frequency', 'day_before_loan',\n",
       "       'sum_monthly_payment_amount', 'sum_monthly_payment_amount_in',\n",
       "       'sum_monthly_payment_amount_out', 'sum_monthly_payment_out_household',\n",
       "       'sum_monthly_payment_out_insurrance',\n",
       "       'sum_monthly_payment_in_interest_credited',\n",
       "       'sum_monthly_payment_out_payment_for_statement',\n",
       "       'sum_monthly_payment_out_sanction_interest_neg_bal',\n",
       "       'count_out_sanction_interest_neg_bal', 'count_monthly_payment_trans',\n",
       "       'current_monthly_payment_amount_out',\n",
       "       'current_monthly_payment_out_household',\n",
       "       'current_monthly_payment_out_insurrance',\n",
       "       'current_monthly_payment_out_payment_for_statement',\n",
       "       'current_monthly_payment_in_interest_credited', 'sum_other_amount',\n",
       "       'sum_other_amount_in', 'sum_other_amount_out', 'count_other_trans',\n",
       "       'balance_latest', 'client_gender', 'client_age', 'all_client_mean_age',\n",
       "       'all_client_count', 'card_type_classic', 'card_type_gold',\n",
       "       'card_type_junior', 'num_inhabitants', 'urban_rate', 'avg_salary',\n",
       "       'num_enterpreneurs_per1000inhabitants', 'unemploy_rate',\n",
       "       'number_crimes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
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       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>all_client_count</th>\n",
       "      <th>card_type_classic</th>\n",
       "      <th>card_type_gold</th>\n",
       "      <th>card_type_junior</th>\n",
       "      <th>num_inhabitants</th>\n",
       "      <th>urban_rate</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>num_enterpreneurs_per1000inhabitants</th>\n",
       "      <th>unemploy_rate</th>\n",
       "      <th>number_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>80952</td>\n",
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       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>120.482428</td>\n",
       "      <td>4.124601</td>\n",
       "      <td>116.357827</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
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       "      <td>167</td>\n",
       "      <td>0.315</td>\n",
       "      <td>92392.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>335.966495</td>\n",
       "      <td>7.306701</td>\n",
       "      <td>328.659794</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103347</td>\n",
       "      <td>0.670</td>\n",
       "      <td>9104</td>\n",
       "      <td>123</td>\n",
       "      <td>1.785</td>\n",
       "      <td>2326.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>204.881526</td>\n",
       "      <td>8.206827</td>\n",
       "      <td>196.674699</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>228848</td>\n",
       "      <td>0.572</td>\n",
       "      <td>9893</td>\n",
       "      <td>96</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>74.492891</td>\n",
       "      <td>5.457346</td>\n",
       "      <td>69.035545</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.584</td>\n",
       "      <td>8547</td>\n",
       "      <td>120</td>\n",
       "      <td>3.120</td>\n",
       "      <td>1552.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>117.125984</td>\n",
       "      <td>4.385827</td>\n",
       "      <td>112.740157</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51428</td>\n",
       "      <td>0.527</td>\n",
       "      <td>8402</td>\n",
       "      <td>120</td>\n",
       "      <td>3.540</td>\n",
       "      <td>1049.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0           2        80952             24           3373           A   \n",
       "1          19        30276             12           2523           B   \n",
       "2          25        30276             12           2523           A   \n",
       "3          37       318480             60           5308           D   \n",
       "4          38       110736             48           2307           C   \n",
       "\n",
       "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0       monthly              313                  120.482428   \n",
       "1       monthly              388                  335.966495   \n",
       "2       monthly              498                  204.881526   \n",
       "3       monthly              422                   74.492891   \n",
       "4        weekly              254                  117.125984   \n",
       "\n",
       "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                       4.124601                      116.357827  ...   \n",
       "1                       7.306701                      328.659794  ...   \n",
       "2                       8.206827                      196.674699  ...   \n",
       "3                       5.457346                       69.035545  ...   \n",
       "4                       4.385827                      112.740157  ...   \n",
       "\n",
       "   all_client_count  card_type_classic  card_type_gold  card_type_junior  \\\n",
       "0                 2                  0               0                 0   \n",
       "1                 1                  0               0                 0   \n",
       "2                 1                  0               0                 0   \n",
       "3                 1                  0               0                 0   \n",
       "4                 1                  0               0                 0   \n",
       "\n",
       "   num_inhabitants  urban_rate  avg_salary  \\\n",
       "0          1204953       1.000       12541   \n",
       "1           103347       0.670        9104   \n",
       "2           228848       0.572        9893   \n",
       "3            70646       0.584        8547   \n",
       "4            51428       0.527        8402   \n",
       "\n",
       "   num_enterpreneurs_per1000inhabitants  unemploy_rate  number_crimes  \n",
       "0                                   167          0.315        92392.0  \n",
       "1                                   123          1.785         2326.5  \n",
       "2                                    96          4.360         5755.0  \n",
       "3                                   120          3.120         1552.5  \n",
       "4                                   120          3.540         1049.0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_features = ['sum_monthly_payment_amount', 'sum_monthly_payment_amount_in', 'sum_monthly_payment_amount_out', 'sum_monthly_payment_out_household',\n",
    "                 'sum_monthly_payment_out_insurrance','sum_monthly_payment_in_interest_credited','sum_monthly_payment_out_payment_for_statement',\n",
    "                 'sum_monthly_payment_out_sanction_interest_neg_bal','count_out_sanction_interest_neg_bal', 'count_monthly_payment_trans',\n",
    "                 'sum_other_amount','sum_other_amount_in', 'sum_other_amount_out', 'count_other_trans']\n",
    "\n",
    "final_df.loc[:, norm_features] = final_df.loc[:, norm_features].div(final_df['day_before_loan'], axis=0)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682, 40)"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_df.to_csv('transformed_data/prepared_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add more feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* All time balance (avg balance, min, max)\n",
    "* last month, last 3 months balance min. mean, max, median, var\n",
    "* average balance from first month, last month ,and last 3 months. \n",
    "* avg balance / loan payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('transformed_data/prepared_data_beforeloan_normbyday.csv')\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = final_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id        date    type                    operation  amount  \\\n",
       "0           2  1993-02-26  credit               credit_in_cash    1100   \n",
       "1           2  1993-03-12  credit  collection_from_anotherbank   20236   \n",
       "2           2  1993-03-28  credit               credit_in_cash    3700   \n",
       "3           2  1993-03-31  credit                          NaN      14   \n",
       "4           2  1993-04-12  credit  collection_from_anotherbank   20236   \n",
       "\n",
       "   balance           k_symbol  amount_in  amount_out   loan_date  \\\n",
       "0     1100                NaN     1100.0         0.0  1994-01-05   \n",
       "1    21336                NaN    20236.0         0.0  1994-01-05   \n",
       "2    25036                NaN     3700.0         0.0  1994-01-05   \n",
       "3    25050  interest_credited       14.0         0.0  1994-01-05   \n",
       "4    45286                NaN    20236.0         0.0  1994-01-05   \n",
       "\n",
       "   day_after_loan  \n",
       "0           313.0  \n",
       "1           299.0  \n",
       "2           283.0  \n",
       "3           280.0  \n",
       "4           268.0  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_before_loan = pd.read_csv('data/trans_before_loan.csv')\n",
    "trans_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_before_loan.groupby('account_id')['balance'].min().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_before_loan.groupby(['account_id'])['balance'].head(1).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All time balance\n",
    "average balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans_before_loan.groupby(['account_id'])['balance'].mean().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "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>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>card_type_junior</th>\n",
       "      <th>num_inhabitants</th>\n",
       "      <th>urban_rate</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>num_enterpreneurs_per1000inhabitants</th>\n",
       "      <th>unemploy_rate</th>\n",
       "      <th>number_crimes</th>\n",
       "      <th>balance_mean</th>\n",
       "      <th>balance_min</th>\n",
       "      <th>balance_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>120.482428</td>\n",
       "      <td>4.124601</td>\n",
       "      <td>116.357827</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1204953</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12541</td>\n",
       "      <td>167</td>\n",
       "      <td>0.315</td>\n",
       "      <td>92392.0</td>\n",
       "      <td>32590.759259</td>\n",
       "      <td>1100</td>\n",
       "      <td>67530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>335.966495</td>\n",
       "      <td>7.306701</td>\n",
       "      <td>328.659794</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>103347</td>\n",
       "      <td>0.670</td>\n",
       "      <td>9104</td>\n",
       "      <td>123</td>\n",
       "      <td>1.785</td>\n",
       "      <td>2326.5</td>\n",
       "      <td>25197.137500</td>\n",
       "      <td>715</td>\n",
       "      <td>58158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>204.881526</td>\n",
       "      <td>8.206827</td>\n",
       "      <td>196.674699</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>228848</td>\n",
       "      <td>0.572</td>\n",
       "      <td>9893</td>\n",
       "      <td>96</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>62991.408537</td>\n",
       "      <td>900</td>\n",
       "      <td>119653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>74.492891</td>\n",
       "      <td>5.457346</td>\n",
       "      <td>69.035545</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70646</td>\n",
       "      <td>0.584</td>\n",
       "      <td>8547</td>\n",
       "      <td>120</td>\n",
       "      <td>3.120</td>\n",
       "      <td>1552.5</td>\n",
       "      <td>39954.034483</td>\n",
       "      <td>-1011</td>\n",
       "      <td>104762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>117.125984</td>\n",
       "      <td>4.385827</td>\n",
       "      <td>112.740157</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>51428</td>\n",
       "      <td>0.527</td>\n",
       "      <td>8402</td>\n",
       "      <td>120</td>\n",
       "      <td>3.540</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>31383.581818</td>\n",
       "      <td>13841</td>\n",
       "      <td>55991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0           2        80952             24           3373           A   \n",
       "1          19        30276             12           2523           B   \n",
       "2          25        30276             12           2523           A   \n",
       "3          37       318480             60           5308           D   \n",
       "4          38       110736             48           2307           C   \n",
       "\n",
       "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0       monthly              313                  120.482428   \n",
       "1       monthly              388                  335.966495   \n",
       "2       monthly              498                  204.881526   \n",
       "3       monthly              422                   74.492891   \n",
       "4        weekly              254                  117.125984   \n",
       "\n",
       "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                       4.124601                      116.357827  ...   \n",
       "1                       7.306701                      328.659794  ...   \n",
       "2                       8.206827                      196.674699  ...   \n",
       "3                       5.457346                       69.035545  ...   \n",
       "4                       4.385827                      112.740157  ...   \n",
       "\n",
       "   card_type_junior  num_inhabitants  urban_rate  avg_salary  \\\n",
       "0                 0          1204953       1.000       12541   \n",
       "1                 0           103347       0.670        9104   \n",
       "2                 0           228848       0.572        9893   \n",
       "3                 0            70646       0.584        8547   \n",
       "4                 0            51428       0.527        8402   \n",
       "\n",
       "   num_enterpreneurs_per1000inhabitants  unemploy_rate  number_crimes  \\\n",
       "0                                   167          0.315        92392.0   \n",
       "1                                   123          1.785         2326.5   \n",
       "2                                    96          4.360         5755.0   \n",
       "3                                   120          3.120         1552.5   \n",
       "4                                   120          3.540         1049.0   \n",
       "\n",
       "   balance_mean  balance_min  balance_max  \n",
       "0  32590.759259         1100        67530  \n",
       "1  25197.137500          715        58158  \n",
       "2  62991.408537          900       119653  \n",
       "3  39954.034483        -1011       104762  \n",
       "4  31383.581818        13841        55991  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['balance_mean'] = trans_before_loan.groupby(['account_id'])['balance'].mean().reset_index(drop=True)\n",
    "df['balance_min'] = trans_before_loan.groupby(['account_id'])['balance'].min().reset_index(drop=True)\n",
    "df['balance_max'] = trans_before_loan.groupby(['account_id'])['balance'].max().reset_index(drop=True)\n",
    "# df['balance_median'] = trans_before_loan.groupby(['account_id'])['balance'].median().reset_index(drop=True)\n",
    "# df['balance_var'] = trans_before_loan.groupby(['account_id'])['balance'].var().reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "last month, last 3 months balance min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>last_month_before_loan</th>\n",
       "      <th>last_3_months_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-02-26</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>21336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>299.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-28</td>\n",
       "      <td>credit</td>\n",
       "      <td>credit_in_cash</td>\n",
       "      <td>3700</td>\n",
       "      <td>25036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>283.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-03-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>25050</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-04-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>45286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id        date    type                    operation  amount  \\\n",
       "0           2  1993-02-26  credit               credit_in_cash    1100   \n",
       "1           2  1993-03-12  credit  collection_from_anotherbank   20236   \n",
       "2           2  1993-03-28  credit               credit_in_cash    3700   \n",
       "3           2  1993-03-31  credit                          NaN      14   \n",
       "4           2  1993-04-12  credit  collection_from_anotherbank   20236   \n",
       "\n",
       "   balance           k_symbol  amount_in  amount_out  loan_date  \\\n",
       "0     1100                NaN     1100.0         0.0 1994-01-05   \n",
       "1    21336                NaN    20236.0         0.0 1994-01-05   \n",
       "2    25036                NaN     3700.0         0.0 1994-01-05   \n",
       "3    25050  interest_credited       14.0         0.0 1994-01-05   \n",
       "4    45286                NaN    20236.0         0.0 1994-01-05   \n",
       "\n",
       "   day_after_loan last_month_before_loan last_3_months_before_loan  \n",
       "0           313.0             1993-12-05                1993-10-04  \n",
       "1           299.0             1993-12-05                1993-10-04  \n",
       "2           283.0             1993-12-05                1993-10-04  \n",
       "3           280.0             1993-12-05                1993-10-04  \n",
       "4           268.0             1993-12-05                1993-10-04  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_before_loan['loan_date'] = pd.to_datetime(trans_before_loan['loan_date'])\n",
    "trans_before_loan['last_month_before_loan'] = trans_before_loan['loan_date'] - pd.Timedelta(days=31)\n",
    "trans_before_loan['last_3_months_before_loan'] = trans_before_loan['loan_date'] - pd.Timedelta(days=31*3)\n",
    "trans_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>last_month_before_loan</th>\n",
       "      <th>last_3_months_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>7266</td>\n",
       "      <td>24559</td>\n",
       "      <td>household</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-12-08</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>3500</td>\n",
       "      <td>21059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-12-10</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>4200</td>\n",
       "      <td>16859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-12-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>30354</td>\n",
       "      <td>47213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30354.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-12-19</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>3000</td>\n",
       "      <td>44213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id        date        type                    operation  amount  \\\n",
       "45           2  1993-12-05  withdrawal    remittance_to_anotherbank    7266   \n",
       "46           2  1993-12-08  withdrawal           withdrawal_in_cash    3500   \n",
       "47           2  1993-12-10  withdrawal           withdrawal_in_cash    4200   \n",
       "48           2  1993-12-12      credit  collection_from_anotherbank   30354   \n",
       "49           2  1993-12-19  withdrawal           withdrawal_in_cash    3000   \n",
       "\n",
       "    balance   k_symbol  amount_in  amount_out  loan_date  day_after_loan  \\\n",
       "45    24559  household        0.0      7266.0 1994-01-05            31.0   \n",
       "46    21059        NaN        0.0      3500.0 1994-01-05            28.0   \n",
       "47    16859        NaN        0.0      4200.0 1994-01-05            26.0   \n",
       "48    47213        NaN    30354.0         0.0 1994-01-05            24.0   \n",
       "49    44213        NaN        0.0      3000.0 1994-01-05            17.0   \n",
       "\n",
       "   last_month_before_loan last_3_months_before_loan  \n",
       "45             1993-12-05                1993-10-04  \n",
       "46             1993-12-05                1993-10-04  \n",
       "47             1993-12-05                1993-10-04  \n",
       "48             1993-12-05                1993-10-04  \n",
       "49             1993-12-05                1993-10-04  "
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_month_before_loan = trans_before_loan[trans_before_loan['date'] >= trans_before_loan['last_month_before_loan']]\n",
    "last_month_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>num_enterpreneurs_per1000inhabitants</th>\n",
       "      <th>unemploy_rate</th>\n",
       "      <th>number_crimes</th>\n",
       "      <th>balance_mean</th>\n",
       "      <th>balance_min</th>\n",
       "      <th>balance_max</th>\n",
       "      <th>balance_min_last_month</th>\n",
       "      <th>balance_max_last_month</th>\n",
       "      <th>balance_mean_last_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>120.482428</td>\n",
       "      <td>4.124601</td>\n",
       "      <td>116.357827</td>\n",
       "      <td>...</td>\n",
       "      <td>12541</td>\n",
       "      <td>167</td>\n",
       "      <td>0.315</td>\n",
       "      <td>92392.0</td>\n",
       "      <td>32590.759259</td>\n",
       "      <td>1100</td>\n",
       "      <td>67530</td>\n",
       "      <td>16859</td>\n",
       "      <td>47213</td>\n",
       "      <td>29792.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>335.966495</td>\n",
       "      <td>7.306701</td>\n",
       "      <td>328.659794</td>\n",
       "      <td>...</td>\n",
       "      <td>9104</td>\n",
       "      <td>123</td>\n",
       "      <td>1.785</td>\n",
       "      <td>2326.5</td>\n",
       "      <td>25197.137500</td>\n",
       "      <td>715</td>\n",
       "      <td>58158</td>\n",
       "      <td>715</td>\n",
       "      <td>18915</td>\n",
       "      <td>14062.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>204.881526</td>\n",
       "      <td>8.206827</td>\n",
       "      <td>196.674699</td>\n",
       "      <td>...</td>\n",
       "      <td>9893</td>\n",
       "      <td>96</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>62991.408537</td>\n",
       "      <td>900</td>\n",
       "      <td>119653</td>\n",
       "      <td>31622</td>\n",
       "      <td>119653</td>\n",
       "      <td>64026.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>74.492891</td>\n",
       "      <td>5.457346</td>\n",
       "      <td>69.035545</td>\n",
       "      <td>...</td>\n",
       "      <td>8547</td>\n",
       "      <td>120</td>\n",
       "      <td>3.120</td>\n",
       "      <td>1552.5</td>\n",
       "      <td>39954.034483</td>\n",
       "      <td>-1011</td>\n",
       "      <td>104762</td>\n",
       "      <td>16368</td>\n",
       "      <td>49186</td>\n",
       "      <td>33760.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>117.125984</td>\n",
       "      <td>4.385827</td>\n",
       "      <td>112.740157</td>\n",
       "      <td>...</td>\n",
       "      <td>8402</td>\n",
       "      <td>120</td>\n",
       "      <td>3.540</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>31383.581818</td>\n",
       "      <td>13841</td>\n",
       "      <td>55991</td>\n",
       "      <td>23807</td>\n",
       "      <td>37648</td>\n",
       "      <td>31890.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0           2        80952             24           3373           A   \n",
       "1          19        30276             12           2523           B   \n",
       "2          25        30276             12           2523           A   \n",
       "3          37       318480             60           5308           D   \n",
       "4          38       110736             48           2307           C   \n",
       "\n",
       "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0       monthly              313                  120.482428   \n",
       "1       monthly              388                  335.966495   \n",
       "2       monthly              498                  204.881526   \n",
       "3       monthly              422                   74.492891   \n",
       "4        weekly              254                  117.125984   \n",
       "\n",
       "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                       4.124601                      116.357827  ...   \n",
       "1                       7.306701                      328.659794  ...   \n",
       "2                       8.206827                      196.674699  ...   \n",
       "3                       5.457346                       69.035545  ...   \n",
       "4                       4.385827                      112.740157  ...   \n",
       "\n",
       "   avg_salary  num_enterpreneurs_per1000inhabitants  unemploy_rate  \\\n",
       "0       12541                                   167          0.315   \n",
       "1        9104                                   123          1.785   \n",
       "2        9893                                    96          4.360   \n",
       "3        8547                                   120          3.120   \n",
       "4        8402                                   120          3.540   \n",
       "\n",
       "   number_crimes  balance_mean  balance_min  balance_max  \\\n",
       "0        92392.0  32590.759259         1100        67530   \n",
       "1         2326.5  25197.137500          715        58158   \n",
       "2         5755.0  62991.408537          900       119653   \n",
       "3         1552.5  39954.034483        -1011       104762   \n",
       "4         1049.0  31383.581818        13841        55991   \n",
       "\n",
       "   balance_min_last_month  balance_max_last_month  balance_mean_last_month  \n",
       "0                   16859                   47213             29792.555556  \n",
       "1                     715                   18915             14062.500000  \n",
       "2                   31622                  119653             64026.555556  \n",
       "3                   16368                   49186             33760.700000  \n",
       "4                   23807                   37648             31890.750000  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['balance_min_last_month'] = last_month_before_loan.groupby(by='account_id')['balance'].min().reset_index(drop=True)\n",
    "df['balance_max_last_month'] = last_month_before_loan.groupby(by='account_id')['balance'].max().reset_index(drop=True)\n",
    "df['balance_mean_last_month'] = last_month_before_loan.groupby(by='account_id')['balance'].mean().reset_index(drop=True)\n",
    "# df['balance_median_last_month'] = last_month_before_loan.groupby(by='account_id')['balance'].median().reset_index(drop=True)\n",
    "# df['balance_var_last_month'] = last_month_before_loan.groupby(by='account_id')['balance'].var().reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>last_month_before_loan</th>\n",
       "      <th>last_3_months_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-05</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>7266</td>\n",
       "      <td>10508</td>\n",
       "      <td>household</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>30744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-24</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>3000</td>\n",
       "      <td>27744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-31</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>15</td>\n",
       "      <td>27830</td>\n",
       "      <td>payment_for_statement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "      <td>27845</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id        date        type                    operation  amount  \\\n",
       "34           2  1993-10-05  withdrawal    remittance_to_anotherbank    7266   \n",
       "35           2  1993-10-12      credit  collection_from_anotherbank   20236   \n",
       "36           2  1993-10-24  withdrawal           withdrawal_in_cash    3000   \n",
       "37           2  1993-10-31  withdrawal           withdrawal_in_cash      15   \n",
       "38           2  1993-10-31      credit                          NaN     101   \n",
       "\n",
       "    balance               k_symbol  amount_in  amount_out  loan_date  \\\n",
       "34    10508              household        0.0      7266.0 1994-01-05   \n",
       "35    30744                    NaN    20236.0         0.0 1994-01-05   \n",
       "36    27744                    NaN        0.0      3000.0 1994-01-05   \n",
       "37    27830  payment_for_statement        0.0        15.0 1994-01-05   \n",
       "38    27845      interest_credited      101.0         0.0 1994-01-05   \n",
       "\n",
       "    day_after_loan last_month_before_loan last_3_months_before_loan  \n",
       "34            92.0             1993-12-05                1993-10-04  \n",
       "35            85.0             1993-12-05                1993-10-04  \n",
       "36            73.0             1993-12-05                1993-10-04  \n",
       "37            66.0             1993-12-05                1993-10-04  \n",
       "38            66.0             1993-12-05                1993-10-04  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_3months_before_loan = trans_before_loan[trans_before_loan['date'] >= trans_before_loan['last_3_months_before_loan']]\n",
    "last_3months_before_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "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>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>number_crimes</th>\n",
       "      <th>balance_mean</th>\n",
       "      <th>balance_min</th>\n",
       "      <th>balance_max</th>\n",
       "      <th>balance_min_last_month</th>\n",
       "      <th>balance_max_last_month</th>\n",
       "      <th>balance_mean_last_month</th>\n",
       "      <th>balance_min_last_3months</th>\n",
       "      <th>balance_max_last_3months</th>\n",
       "      <th>balance_mean_last_3months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>120.482428</td>\n",
       "      <td>4.124601</td>\n",
       "      <td>116.357827</td>\n",
       "      <td>...</td>\n",
       "      <td>92392.0</td>\n",
       "      <td>32590.759259</td>\n",
       "      <td>1100</td>\n",
       "      <td>67530</td>\n",
       "      <td>16859</td>\n",
       "      <td>47213</td>\n",
       "      <td>29792.555556</td>\n",
       "      <td>10508</td>\n",
       "      <td>47213</td>\n",
       "      <td>29326.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>335.966495</td>\n",
       "      <td>7.306701</td>\n",
       "      <td>328.659794</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.5</td>\n",
       "      <td>25197.137500</td>\n",
       "      <td>715</td>\n",
       "      <td>58158</td>\n",
       "      <td>715</td>\n",
       "      <td>18915</td>\n",
       "      <td>14062.500000</td>\n",
       "      <td>715</td>\n",
       "      <td>35162</td>\n",
       "      <td>15123.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>204.881526</td>\n",
       "      <td>8.206827</td>\n",
       "      <td>196.674699</td>\n",
       "      <td>...</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>62991.408537</td>\n",
       "      <td>900</td>\n",
       "      <td>119653</td>\n",
       "      <td>31622</td>\n",
       "      <td>119653</td>\n",
       "      <td>64026.555556</td>\n",
       "      <td>31622</td>\n",
       "      <td>119653</td>\n",
       "      <td>67839.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>74.492891</td>\n",
       "      <td>5.457346</td>\n",
       "      <td>69.035545</td>\n",
       "      <td>...</td>\n",
       "      <td>1552.5</td>\n",
       "      <td>39954.034483</td>\n",
       "      <td>-1011</td>\n",
       "      <td>104762</td>\n",
       "      <td>16368</td>\n",
       "      <td>49186</td>\n",
       "      <td>33760.700000</td>\n",
       "      <td>-1011</td>\n",
       "      <td>49186</td>\n",
       "      <td>20563.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>117.125984</td>\n",
       "      <td>4.385827</td>\n",
       "      <td>112.740157</td>\n",
       "      <td>...</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>31383.581818</td>\n",
       "      <td>13841</td>\n",
       "      <td>55991</td>\n",
       "      <td>23807</td>\n",
       "      <td>37648</td>\n",
       "      <td>31890.750000</td>\n",
       "      <td>15671</td>\n",
       "      <td>37648</td>\n",
       "      <td>26212.692308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0           2        80952             24           3373           A   \n",
       "1          19        30276             12           2523           B   \n",
       "2          25        30276             12           2523           A   \n",
       "3          37       318480             60           5308           D   \n",
       "4          38       110736             48           2307           C   \n",
       "\n",
       "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0       monthly              313                  120.482428   \n",
       "1       monthly              388                  335.966495   \n",
       "2       monthly              498                  204.881526   \n",
       "3       monthly              422                   74.492891   \n",
       "4        weekly              254                  117.125984   \n",
       "\n",
       "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                       4.124601                      116.357827  ...   \n",
       "1                       7.306701                      328.659794  ...   \n",
       "2                       8.206827                      196.674699  ...   \n",
       "3                       5.457346                       69.035545  ...   \n",
       "4                       4.385827                      112.740157  ...   \n",
       "\n",
       "   number_crimes  balance_mean  balance_min  balance_max  \\\n",
       "0        92392.0  32590.759259         1100        67530   \n",
       "1         2326.5  25197.137500          715        58158   \n",
       "2         5755.0  62991.408537          900       119653   \n",
       "3         1552.5  39954.034483        -1011       104762   \n",
       "4         1049.0  31383.581818        13841        55991   \n",
       "\n",
       "   balance_min_last_month  balance_max_last_month  balance_mean_last_month  \\\n",
       "0                   16859                   47213             29792.555556   \n",
       "1                     715                   18915             14062.500000   \n",
       "2                   31622                  119653             64026.555556   \n",
       "3                   16368                   49186             33760.700000   \n",
       "4                   23807                   37648             31890.750000   \n",
       "\n",
       "   balance_min_last_3months  balance_max_last_3months  \\\n",
       "0                     10508                     47213   \n",
       "1                       715                     35162   \n",
       "2                     31622                    119653   \n",
       "3                     -1011                     49186   \n",
       "4                     15671                     37648   \n",
       "\n",
       "   balance_mean_last_3months  \n",
       "0               29326.650000  \n",
       "1               15123.190476  \n",
       "2               67839.300000  \n",
       "3               20563.071429  \n",
       "4               26212.692308  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['balance_min_last_3months'] = last_3months_before_loan.groupby(by='account_id')['balance'].min().reset_index(drop=True)\n",
    "df['balance_max_last_3months'] = last_3months_before_loan.groupby(by='account_id')['balance'].max().reset_index(drop=True)\n",
    "df['balance_mean_last_3months'] = last_3months_before_loan.groupby(by='account_id')['balance'].mean().reset_index(drop=True)\n",
    "# df['balance_median_last_3months'] = last_3months_before_loan.groupby(by='account_id')['balance'].median().reset_index(drop=True)\n",
    "# df['balance_var_last_3months'] = last_3months_before_loan.groupby(by='account_id')['balance'].var().reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "avg balance / loan payment\n",
    "avg balance / loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['balance_mean_per_loan_payment'] = df['balance_mean'].div(df['loan_payments'], axis=0)\n",
    "df['balance_mean_last_month_per_loan_payment'] = df['balance_mean_last_month'].div(df['loan_payments'], axis=0)\n",
    "# df['balance_mean_last_3months_per_loan_payment'] = df['balance_mean_last_3months'].div(df['loan_payments'], axis=0)\n",
    "\n",
    "df['balance_mean_per_loan_amount'] = df['balance_mean'].div(df['loan_amount'], axis=0)\n",
    "df['balance_mean_last_month_per_loan_amount'] = df['balance_mean_last_month'].div(df['loan_amount'], axis=0)\n",
    "# df['balance_mean_last_3months_per_loan_amount'] = df['balance_mean_last_3months'].div(df['loan_amount'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[['balance_mean', 'balance_mean_last_month', 'balance_mean_last_3months']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average balance, average amount growth from first month, last month ,and last 3 months. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>k_symbol</th>\n",
       "      <th>amount_in</th>\n",
       "      <th>amount_out</th>\n",
       "      <th>loan_date</th>\n",
       "      <th>day_after_loan</th>\n",
       "      <th>last_month_before_loan</th>\n",
       "      <th>last_3_months_before_loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-05</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>remittance_to_anotherbank</td>\n",
       "      <td>7266</td>\n",
       "      <td>10508</td>\n",
       "      <td>household</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7266.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-12</td>\n",
       "      <td>credit</td>\n",
       "      <td>collection_from_anotherbank</td>\n",
       "      <td>20236</td>\n",
       "      <td>30744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-24</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>3000</td>\n",
       "      <td>27744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-31</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>withdrawal_in_cash</td>\n",
       "      <td>15</td>\n",
       "      <td>27830</td>\n",
       "      <td>payment_for_statement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-10-31</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "      <td>27845</td>\n",
       "      <td>interest_credited</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994-01-05</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1993-12-05</td>\n",
       "      <td>1993-10-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account_id        date        type                    operation  amount  \\\n",
       "34           2  1993-10-05  withdrawal    remittance_to_anotherbank    7266   \n",
       "35           2  1993-10-12      credit  collection_from_anotherbank   20236   \n",
       "36           2  1993-10-24  withdrawal           withdrawal_in_cash    3000   \n",
       "37           2  1993-10-31  withdrawal           withdrawal_in_cash      15   \n",
       "38           2  1993-10-31      credit                          NaN     101   \n",
       "\n",
       "    balance               k_symbol  amount_in  amount_out  loan_date  \\\n",
       "34    10508              household        0.0      7266.0 1994-01-05   \n",
       "35    30744                    NaN    20236.0         0.0 1994-01-05   \n",
       "36    27744                    NaN        0.0      3000.0 1994-01-05   \n",
       "37    27830  payment_for_statement        0.0        15.0 1994-01-05   \n",
       "38    27845      interest_credited      101.0         0.0 1994-01-05   \n",
       "\n",
       "    day_after_loan last_month_before_loan last_3_months_before_loan  \n",
       "34            92.0             1993-12-05                1993-10-04  \n",
       "35            85.0             1993-12-05                1993-10-04  \n",
       "36            73.0             1993-12-05                1993-10-04  \n",
       "37            66.0             1993-12-05                1993-10-04  \n",
       "38            66.0             1993-12-05                1993-10-04  "
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_3months_ago = trans_before_loan[(trans_before_loan['date'] > trans_before_loan['last_3_months_before_loan']) & (trans_before_loan['date'] <= trans_before_loan['last_3_months_before_loan'] + pd.Timedelta(days=31))]\n",
    "last_3months_ago.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['growth_balance'] = (df['balance_mean_last_month'] - last_3months_ago.groupby('account_id')['balance'].mean().reset_index(drop=True)) / (30*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['growth_balance'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_id', 'loan_amount', 'loan_duration', 'loan_payments',\n",
       "       'loan_status', 'acc_frequency', 'day_before_loan',\n",
       "       'sum_monthly_payment_amount', 'sum_monthly_payment_amount_in',\n",
       "       'sum_monthly_payment_amount_out', 'sum_monthly_payment_out_household',\n",
       "       'sum_monthly_payment_out_insurrance',\n",
       "       'sum_monthly_payment_in_interest_credited',\n",
       "       'sum_monthly_payment_out_payment_for_statement',\n",
       "       'sum_monthly_payment_out_sanction_interest_neg_bal',\n",
       "       'count_out_sanction_interest_neg_bal', 'count_monthly_payment_trans',\n",
       "       'current_monthly_payment_amount_out',\n",
       "       'current_monthly_payment_out_household',\n",
       "       'current_monthly_payment_out_insurrance',\n",
       "       'current_monthly_payment_out_payment_for_statement',\n",
       "       'current_monthly_payment_in_interest_credited', 'sum_other_amount',\n",
       "       'sum_other_amount_in', 'sum_other_amount_out', 'count_other_trans',\n",
       "       'balance_latest', 'client_gender', 'client_age', 'all_client_mean_age',\n",
       "       'all_client_count', 'card_type_classic', 'card_type_gold',\n",
       "       'card_type_junior', 'num_inhabitants', 'urban_rate', 'avg_salary',\n",
       "       'num_enterpreneurs_per1000inhabitants', 'unemploy_rate',\n",
       "       'number_crimes', 'balance_mean', 'balance_min', 'balance_max',\n",
       "       'balance_min_last_month', 'balance_max_last_month',\n",
       "       'balance_mean_last_month', 'balance_min_last_3months',\n",
       "       'balance_max_last_3months', 'balance_mean_last_3months',\n",
       "       'balance_mean_per_loan_payment',\n",
       "       'balance_mean_last_month_per_loan_payment',\n",
       "       'balance_mean_per_loan_amount',\n",
       "       'balance_mean_last_month_per_loan_amount', 'growth_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* amount growth\n",
    "* sum amount out per month\n",
    "* sum amount out per month / loan payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_month_before_loan = trans_before_loan[trans_before_loan['date'] >= trans_before_loan['last_month_before_loan']]\n",
    "\n",
    "last_3months_before_loan = trans_before_loan[trans_before_loan['date'] >= trans_before_loan['last_3_months_before_loan']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "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>account_id</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_duration</th>\n",
       "      <th>loan_payments</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>acc_frequency</th>\n",
       "      <th>day_before_loan</th>\n",
       "      <th>sum_monthly_payment_amount</th>\n",
       "      <th>sum_monthly_payment_amount_in</th>\n",
       "      <th>sum_monthly_payment_amount_out</th>\n",
       "      <th>...</th>\n",
       "      <th>amount_last_mount_out_per_loan_amount</th>\n",
       "      <th>amount_last_3months_avg</th>\n",
       "      <th>amount_last_3months_in_avg</th>\n",
       "      <th>amount_last_3months_out_avg</th>\n",
       "      <th>amount_last_3months_per_loan_payments</th>\n",
       "      <th>amount_last_3months_in_per_loan_payments</th>\n",
       "      <th>amount_last_3months_out_per_loan_payments</th>\n",
       "      <th>amount_last_3months_per_loan_amount</th>\n",
       "      <th>amount_last_3months_in_per_loan_amount</th>\n",
       "      <th>amount_last_3months_out_per_loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>80952</td>\n",
       "      <td>24</td>\n",
       "      <td>3373</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>313</td>\n",
       "      <td>120.482428</td>\n",
       "      <td>4.124601</td>\n",
       "      <td>116.357827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.425894</td>\n",
       "      <td>44114.333333</td>\n",
       "      <td>23734.666667</td>\n",
       "      <td>20379.666667</td>\n",
       "      <td>13.078664</td>\n",
       "      <td>7.036664</td>\n",
       "      <td>6.042000</td>\n",
       "      <td>0.544944</td>\n",
       "      <td>0.293194</td>\n",
       "      <td>0.251750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>B</td>\n",
       "      <td>monthly</td>\n",
       "      <td>388</td>\n",
       "      <td>335.966495</td>\n",
       "      <td>7.306701</td>\n",
       "      <td>328.659794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.601632</td>\n",
       "      <td>36373.333333</td>\n",
       "      <td>17858.333333</td>\n",
       "      <td>18515.000000</td>\n",
       "      <td>14.416700</td>\n",
       "      <td>7.078214</td>\n",
       "      <td>7.338486</td>\n",
       "      <td>1.201392</td>\n",
       "      <td>0.589851</td>\n",
       "      <td>0.611540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>30276</td>\n",
       "      <td>12</td>\n",
       "      <td>2523</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>498</td>\n",
       "      <td>204.881526</td>\n",
       "      <td>8.206827</td>\n",
       "      <td>196.674699</td>\n",
       "      <td>...</td>\n",
       "      <td>3.150482</td>\n",
       "      <td>115321.666667</td>\n",
       "      <td>53753.000000</td>\n",
       "      <td>61568.666667</td>\n",
       "      <td>45.708152</td>\n",
       "      <td>21.305192</td>\n",
       "      <td>24.402959</td>\n",
       "      <td>3.809013</td>\n",
       "      <td>1.775433</td>\n",
       "      <td>2.033580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>318480</td>\n",
       "      <td>60</td>\n",
       "      <td>5308</td>\n",
       "      <td>D</td>\n",
       "      <td>monthly</td>\n",
       "      <td>422</td>\n",
       "      <td>74.492891</td>\n",
       "      <td>5.457346</td>\n",
       "      <td>69.035545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103344</td>\n",
       "      <td>60867.000000</td>\n",
       "      <td>30319.333333</td>\n",
       "      <td>30547.666667</td>\n",
       "      <td>11.467031</td>\n",
       "      <td>5.712007</td>\n",
       "      <td>5.755024</td>\n",
       "      <td>0.191117</td>\n",
       "      <td>0.095200</td>\n",
       "      <td>0.095917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>110736</td>\n",
       "      <td>48</td>\n",
       "      <td>2307</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>254</td>\n",
       "      <td>117.125984</td>\n",
       "      <td>4.385827</td>\n",
       "      <td>112.740157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089312</td>\n",
       "      <td>29212.333333</td>\n",
       "      <td>16590.333333</td>\n",
       "      <td>12622.000000</td>\n",
       "      <td>12.662477</td>\n",
       "      <td>7.191302</td>\n",
       "      <td>5.471175</td>\n",
       "      <td>0.263802</td>\n",
       "      <td>0.149819</td>\n",
       "      <td>0.113983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11327</td>\n",
       "      <td>39168</td>\n",
       "      <td>24</td>\n",
       "      <td>1632</td>\n",
       "      <td>C</td>\n",
       "      <td>monthly</td>\n",
       "      <td>347</td>\n",
       "      <td>70.360231</td>\n",
       "      <td>6.244957</td>\n",
       "      <td>64.115274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656352</td>\n",
       "      <td>38777.333333</td>\n",
       "      <td>20569.333333</td>\n",
       "      <td>18208.000000</td>\n",
       "      <td>23.760621</td>\n",
       "      <td>12.603758</td>\n",
       "      <td>11.156863</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.525157</td>\n",
       "      <td>0.464869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>11328</td>\n",
       "      <td>280440</td>\n",
       "      <td>60</td>\n",
       "      <td>4674</td>\n",
       "      <td>C</td>\n",
       "      <td>monthly</td>\n",
       "      <td>620</td>\n",
       "      <td>5.864516</td>\n",
       "      <td>5.501613</td>\n",
       "      <td>0.362903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076412</td>\n",
       "      <td>42196.000000</td>\n",
       "      <td>20453.333333</td>\n",
       "      <td>21742.666667</td>\n",
       "      <td>9.027813</td>\n",
       "      <td>4.375981</td>\n",
       "      <td>4.651833</td>\n",
       "      <td>0.150464</td>\n",
       "      <td>0.072933</td>\n",
       "      <td>0.077531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>11349</td>\n",
       "      <td>419880</td>\n",
       "      <td>60</td>\n",
       "      <td>6998</td>\n",
       "      <td>C</td>\n",
       "      <td>weekly</td>\n",
       "      <td>156</td>\n",
       "      <td>74.634615</td>\n",
       "      <td>4.852564</td>\n",
       "      <td>69.782051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200500</td>\n",
       "      <td>96056.000000</td>\n",
       "      <td>42327.333333</td>\n",
       "      <td>53728.666667</td>\n",
       "      <td>13.726207</td>\n",
       "      <td>6.048490</td>\n",
       "      <td>7.677717</td>\n",
       "      <td>0.228770</td>\n",
       "      <td>0.100808</td>\n",
       "      <td>0.127962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>11359</td>\n",
       "      <td>54024</td>\n",
       "      <td>12</td>\n",
       "      <td>4502</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>675</td>\n",
       "      <td>119.515556</td>\n",
       "      <td>5.328889</td>\n",
       "      <td>114.186667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390327</td>\n",
       "      <td>65997.333333</td>\n",
       "      <td>31656.000000</td>\n",
       "      <td>34341.333333</td>\n",
       "      <td>14.659559</td>\n",
       "      <td>7.031542</td>\n",
       "      <td>7.628017</td>\n",
       "      <td>1.221630</td>\n",
       "      <td>0.585962</td>\n",
       "      <td>0.635668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>11362</td>\n",
       "      <td>129408</td>\n",
       "      <td>24</td>\n",
       "      <td>5392</td>\n",
       "      <td>A</td>\n",
       "      <td>monthly</td>\n",
       "      <td>440</td>\n",
       "      <td>119.752273</td>\n",
       "      <td>3.309091</td>\n",
       "      <td>116.443182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170546</td>\n",
       "      <td>35072.666667</td>\n",
       "      <td>18916.000000</td>\n",
       "      <td>16156.666667</td>\n",
       "      <td>6.504575</td>\n",
       "      <td>3.508160</td>\n",
       "      <td>2.996414</td>\n",
       "      <td>0.271024</td>\n",
       "      <td>0.146173</td>\n",
       "      <td>0.124851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
       "0             2        80952             24           3373           A   \n",
       "1            19        30276             12           2523           B   \n",
       "2            25        30276             12           2523           A   \n",
       "3            37       318480             60           5308           D   \n",
       "4            38       110736             48           2307           C   \n",
       "..          ...          ...            ...            ...         ...   \n",
       "677       11327        39168             24           1632           C   \n",
       "678       11328       280440             60           4674           C   \n",
       "679       11349       419880             60           6998           C   \n",
       "680       11359        54024             12           4502           A   \n",
       "681       11362       129408             24           5392           A   \n",
       "\n",
       "    acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
       "0         monthly              313                  120.482428   \n",
       "1         monthly              388                  335.966495   \n",
       "2         monthly              498                  204.881526   \n",
       "3         monthly              422                   74.492891   \n",
       "4          weekly              254                  117.125984   \n",
       "..            ...              ...                         ...   \n",
       "677       monthly              347                   70.360231   \n",
       "678       monthly              620                    5.864516   \n",
       "679        weekly              156                   74.634615   \n",
       "680       monthly              675                  119.515556   \n",
       "681       monthly              440                  119.752273   \n",
       "\n",
       "     sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
       "0                         4.124601                      116.357827  ...   \n",
       "1                         7.306701                      328.659794  ...   \n",
       "2                         8.206827                      196.674699  ...   \n",
       "3                         5.457346                       69.035545  ...   \n",
       "4                         4.385827                      112.740157  ...   \n",
       "..                             ...                             ...  ...   \n",
       "677                       6.244957                       64.115274  ...   \n",
       "678                       5.501613                        0.362903  ...   \n",
       "679                       4.852564                       69.782051  ...   \n",
       "680                       5.328889                      114.186667  ...   \n",
       "681                       3.309091                      116.443182  ...   \n",
       "\n",
       "     amount_last_mount_out_per_loan_amount  amount_last_3months_avg  \\\n",
       "0                                 0.425894             44114.333333   \n",
       "1                                 0.601632             36373.333333   \n",
       "2                                 3.150482            115321.666667   \n",
       "3                                 0.103344             60867.000000   \n",
       "4                                 0.089312             29212.333333   \n",
       "..                                     ...                      ...   \n",
       "677                               0.656352             38777.333333   \n",
       "678                               0.076412             42196.000000   \n",
       "679                               0.200500             96056.000000   \n",
       "680                               0.390327             65997.333333   \n",
       "681                               0.170546             35072.666667   \n",
       "\n",
       "     amount_last_3months_in_avg  amount_last_3months_out_avg  \\\n",
       "0                  23734.666667                 20379.666667   \n",
       "1                  17858.333333                 18515.000000   \n",
       "2                  53753.000000                 61568.666667   \n",
       "3                  30319.333333                 30547.666667   \n",
       "4                  16590.333333                 12622.000000   \n",
       "..                          ...                          ...   \n",
       "677                20569.333333                 18208.000000   \n",
       "678                20453.333333                 21742.666667   \n",
       "679                42327.333333                 53728.666667   \n",
       "680                31656.000000                 34341.333333   \n",
       "681                18916.000000                 16156.666667   \n",
       "\n",
       "     amount_last_3months_per_loan_payments  \\\n",
       "0                                13.078664   \n",
       "1                                14.416700   \n",
       "2                                45.708152   \n",
       "3                                11.467031   \n",
       "4                                12.662477   \n",
       "..                                     ...   \n",
       "677                              23.760621   \n",
       "678                               9.027813   \n",
       "679                              13.726207   \n",
       "680                              14.659559   \n",
       "681                               6.504575   \n",
       "\n",
       "     amount_last_3months_in_per_loan_payments  \\\n",
       "0                                    7.036664   \n",
       "1                                    7.078214   \n",
       "2                                   21.305192   \n",
       "3                                    5.712007   \n",
       "4                                    7.191302   \n",
       "..                                        ...   \n",
       "677                                 12.603758   \n",
       "678                                  4.375981   \n",
       "679                                  6.048490   \n",
       "680                                  7.031542   \n",
       "681                                  3.508160   \n",
       "\n",
       "     amount_last_3months_out_per_loan_payments  \\\n",
       "0                                     6.042000   \n",
       "1                                     7.338486   \n",
       "2                                    24.402959   \n",
       "3                                     5.755024   \n",
       "4                                     5.471175   \n",
       "..                                         ...   \n",
       "677                                  11.156863   \n",
       "678                                   4.651833   \n",
       "679                                   7.677717   \n",
       "680                                   7.628017   \n",
       "681                                   2.996414   \n",
       "\n",
       "     amount_last_3months_per_loan_amount  \\\n",
       "0                               0.544944   \n",
       "1                               1.201392   \n",
       "2                               3.809013   \n",
       "3                               0.191117   \n",
       "4                               0.263802   \n",
       "..                                   ...   \n",
       "677                             0.990026   \n",
       "678                             0.150464   \n",
       "679                             0.228770   \n",
       "680                             1.221630   \n",
       "681                             0.271024   \n",
       "\n",
       "     amount_last_3months_in_per_loan_amount  \\\n",
       "0                                  0.293194   \n",
       "1                                  0.589851   \n",
       "2                                  1.775433   \n",
       "3                                  0.095200   \n",
       "4                                  0.149819   \n",
       "..                                      ...   \n",
       "677                                0.525157   \n",
       "678                                0.072933   \n",
       "679                                0.100808   \n",
       "680                                0.585962   \n",
       "681                                0.146173   \n",
       "\n",
       "     amount_last_3months_out_per_loan_amount  \n",
       "0                                   0.251750  \n",
       "1                                   0.611540  \n",
       "2                                   2.033580  \n",
       "3                                   0.095917  \n",
       "4                                   0.113983  \n",
       "..                                       ...  \n",
       "677                                 0.464869  \n",
       "678                                 0.077531  \n",
       "679                                 0.127962  \n",
       "680                                 0.635668  \n",
       "681                                 0.124851  \n",
       "\n",
       "[682 rows x 72 columns]"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['amount_last_month', 'amount_last_month_in', 'amount_last_month_out']] = last_month_before_loan.groupby('account_id')[['amount', 'amount_in', 'amount_out']].sum().reset_index(drop=True)\n",
    "df[['amount_last_month_per_loan_payments', 'amount_last_mount_in_per_loan_payments', 'amount_last_mount_out_per_loan_payments']] = df[['amount_last_month', 'amount_last_month_in', 'amount_last_month_out']].div(df['loan_payments'], axis=0)\n",
    "df[['amount_last_month_per_loan_amount', 'amount_last_mount_in_per_loan_amount', 'amount_last_mount_out_per_loan_amount']] = df[['amount_last_month', 'amount_last_month_in', 'amount_last_month_out']].div(df['loan_amount'], axis=0)\n",
    "\n",
    "df[['amount_last_3months_avg', 'amount_last_3months_in_avg', 'amount_last_3months_out_avg']] = last_3months_before_loan.groupby('account_id')[['amount', 'amount_in', 'amount_out']].sum().reset_index(drop=True) / 3\n",
    "df[['amount_last_3months_per_loan_payments', 'amount_last_3months_in_per_loan_payments', 'amount_last_3months_out_per_loan_payments']] = df[['amount_last_3months_avg', 'amount_last_3months_in_avg', 'amount_last_3months_out_avg']].div(df['loan_payments'], axis=0)\n",
    "df[['amount_last_3months_per_loan_amount', 'amount_last_3months_in_per_loan_amount', 'amount_last_3months_out_per_loan_amount']] = df[['amount_last_3months_avg', 'amount_last_3months_in_avg', 'amount_last_3months_out_avg']].div(df['loan_amount'], axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# last_3months_ago = trans_before_loan[(trans_before_loan['date'] > trans_before_loan['last_3_months_before_loan']) & (trans_before_loan['date'] <= trans_before_loan['last_3_months_before_loan'] + pd.Timedelta(days=31))]\n",
    "# last_3months_ago.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['growth_amount_in'] = (df['amount_last_month_in'] - last_3months_ago.groupby('account_id')['amount_in'].sum().reset_index(drop=True)) / (30*3)\n",
    "# df['growth_amount_out'] = (df['amount_last_month_out'] - last_3months_ago.groupby('account_id')['amount_out'].sum().reset_index(drop=True)) / (30*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['growth_amount_in'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['growth_amount_out'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682, 72)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "account_id                                   False\n",
       "loan_amount                                  False\n",
       "loan_duration                                False\n",
       "loan_payments                                False\n",
       "loan_status                                  False\n",
       "                                             ...  \n",
       "amount_last_3months_in_per_loan_payments     False\n",
       "amount_last_3months_out_per_loan_payments    False\n",
       "amount_last_3months_per_loan_amount          False\n",
       "amount_last_3months_in_per_loan_amount       False\n",
       "amount_last_3months_out_per_loan_amount      False\n",
       "Length: 72, dtype: bool"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('transformed_data/final_transformed_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = df.copy()\n",
    "dff.drop(['sum_monthly_payment_amount', 'sum_monthly_payment_amount_in', 'sum_monthly_payment_amount_out',\n",
    "         'day_before_loan', 'current_monthly_payment_out_payment_for_statement', 'sum_other_amount',\n",
    "         'sum_other_amount_in', 'sum_other_amount_out', 'client_age', 'all_client_mean_age', \n",
    "         'num_inhabitants', 'urban_rate', 'num_enterpreneurs_per1000inhabitants', 'unemploy_rate', 'balance_max',\n",
    "         'balance_mean_per_loan_payment', 'balance_mean_per_loan_amount', 'amount_last_month', 'amount_last_month_in',\n",
    "         'amount_last_month_per_loan_payments', 'amount_last_month_per_loan_amount', 'amount_last_3months_avg',\n",
    "         'amount_last_3months_in_avg', 'amount_last_3months_out_avg', 'amount_last_3months_per_loan_payments', \n",
    "         'amount_last_3months_per_loan_amount', 'balance_min_last_3months', 'balance_max_last_3months', 'balance_mean_last_3months',\n",
    "         'amount_last_3months_per_loan_payments'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682, 43)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff.to_csv('transformed_data/final_dropped_transformed_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.isna().any().any()"
   ]
  }
 ],
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