Credit Risk Modelling - Loan Default Prediction - on Bank dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql as pms\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import sklearn.linear_model as slm\n",
    "import sklearn.metrics as sm\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "import sklearn.svm as svm\n",
    "import sklearn.ensemble as sen\n",
    "import sklearn.preprocessing as sp\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=pms.Connect(host='localhost', user='***', passwd='*****', db='***')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=pd.read_sql('select * from ***', con=conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>npl_group</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>account_ssuda</th>\n",
       "      <th>teyinat</th>\n",
       "      <th>kr_kx</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>amount</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
       "      <th>res_pricipal</th>\n",
       "      <th>over_days_prin</th>\n",
       "      <th>arr_prin</th>\n",
       "      <th>over_days_int</th>\n",
       "      <th>arr_int</th>\n",
       "      <th>max_over_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>182607</td>\n",
       "      <td>1-182607-3</td>\n",
       "      <td>Mikro</td>\n",
       "      <td>KR</td>\n",
       "      <td>2003-05-15</td>\n",
       "      <td>2008-05-15</td>\n",
       "      <td>60</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>42037.57</td>\n",
       "      <td>4189</td>\n",
       "      <td>42037.57</td>\n",
       "      <td>4189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>29467045</td>\n",
       "      <td>1-29467045-3</td>\n",
       "      <td>Mikro</td>\n",
       "      <td>KR</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>2012-02-09</td>\n",
       "      <td>24</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36397.82</td>\n",
       "      <td>2629</td>\n",
       "      <td>36397.82</td>\n",
       "      <td>2629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32566184</td>\n",
       "      <td>1-32566184-3</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KX</td>\n",
       "      <td>2010-05-31</td>\n",
       "      <td>2011-05-31</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.8644</td>\n",
       "      <td>73.98</td>\n",
       "      <td>2040</td>\n",
       "      <td>39.68</td>\n",
       "      <td>2486</td>\n",
       "      <td>81.33</td>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42807960</td>\n",
       "      <td>1-42807960-3</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2012-04-24</td>\n",
       "      <td>2013-10-24</td>\n",
       "      <td>18</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70000546</td>\n",
       "      <td>1-70000546-6</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KX</td>\n",
       "      <td>2013-08-13</td>\n",
       "      <td>2015-08-13</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>8.13</td>\n",
       "      <td>505</td>\n",
       "      <td>4.59</td>\n",
       "      <td>770</td>\n",
       "      <td>3.93</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  npl_group  branch_code   cust_id account_ssuda   teyinat kr_kx date_start  \\\n",
       "0       PKD            1    182607    1-182607-3     Mikro    KR 2003-05-15   \n",
       "1       PKD            1  29467045  1-29467045-3     Mikro    KR 2010-02-09   \n",
       "2         0            1  32566184  1-32566184-3  Istehlak    KX 2010-05-31   \n",
       "3         0            1  42807960  1-42807960-3  Istehlak    KR 2012-04-24   \n",
       "4         0            1  70000546  1-70000546-6  Istehlak    KX 2013-08-13   \n",
       "\n",
       "    date_end  maturity   amount  intrate  annuity currency  exrate  \\\n",
       "0 2008-05-15        60  98000.0      7.0      0.0      AZN  1.0000   \n",
       "1 2012-02-09        24  40000.0     27.0      0.0      AZN  1.0000   \n",
       "2 2011-05-31        12      0.0     25.0      0.0      EUR  1.8644   \n",
       "3 2013-10-24        18   1364.0     25.0      0.0      USD  1.7707   \n",
       "4 2015-08-13        24      0.0     27.0      0.0      USD  1.7707   \n",
       "\n",
       "   res_pricipal  over_days_prin  arr_prin  over_days_int   arr_int  \\\n",
       "0          0.00               0  42037.57           4189  42037.57   \n",
       "1          0.00               0  36397.82           2629  36397.82   \n",
       "2         73.98            2040     39.68           2486     81.33   \n",
       "3          0.00               0      0.00              0      0.00   \n",
       "4          8.13             505      4.59            770      3.93   \n",
       "\n",
       "   max_over_days  \n",
       "0           4189  \n",
       "1           2629  \n",
       "2           2486  \n",
       "3              0  \n",
       "4            770  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=dt[(dt['teyinat']=='Istehlak')&(dt['kr_kx']=='KR')&(dt['date_start']>='2013-01-01')&((dt['amount']*dt['exrate'])<40000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npl_group</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>account_ssuda</th>\n",
       "      <th>teyinat</th>\n",
       "      <th>kr_kx</th>\n",
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       "      <th>date_end</th>\n",
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       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
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       "      <th>over_days_prin</th>\n",
       "      <th>arr_prin</th>\n",
       "      <th>over_days_int</th>\n",
       "      <th>arr_int</th>\n",
       "      <th>max_over_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70000612</td>\n",
       "      <td>1-70000612-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1145</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1530</td>\n",
       "      <td>2169.45</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70001320</td>\n",
       "      <td>1-70001320-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1650.97</td>\n",
       "      <td>927</td>\n",
       "      <td>932.38</td>\n",
       "      <td>625</td>\n",
       "      <td>793.26</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70004187</td>\n",
       "      <td>1-70004187-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1260.10</td>\n",
       "      <td>878</td>\n",
       "      <td>711.64</td>\n",
       "      <td>1107</td>\n",
       "      <td>919.73</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70005562</td>\n",
       "      <td>1-70005562-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>3077.21</td>\n",
       "      <td>1173</td>\n",
       "      <td>1737.85</td>\n",
       "      <td>1303</td>\n",
       "      <td>2628.99</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70006140</td>\n",
       "      <td>1-70006140-6</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>627</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>608</td>\n",
       "      <td>789.67</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  npl_group  branch_code   cust_id account_ssuda   teyinat kr_kx date_start  \\\n",
       "5       PKD            1  70000612  1-70000612-5  Istehlak    KR 2013-07-11   \n",
       "6       PKD            1  70001320  1-70001320-4  Istehlak    KR 2013-07-17   \n",
       "7       PKD            1  70004187  1-70004187-4  Istehlak    KR 2013-08-05   \n",
       "8       PKD            1  70005562  1-70005562-5  Istehlak    KR 2013-08-14   \n",
       "9       PKD            1  70006140  1-70006140-6  Istehlak    KR 2014-03-12   \n",
       "\n",
       "    date_end  maturity  amount  intrate  annuity currency  exrate  \\\n",
       "5 2015-01-11        18  1500.0     33.0      0.0      AZN  1.0000   \n",
       "6 2015-01-17        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "7 2015-02-05        18  1660.0     33.0      0.0      USD  1.7707   \n",
       "8 2015-02-14        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "9 2016-03-12        24  2000.0     32.0      0.0      AZN  1.0000   \n",
       "\n",
       "   res_pricipal  over_days_prin  arr_prin  over_days_int  arr_int  \\\n",
       "5       1287.91            1145   1287.91           1530  2169.45   \n",
       "6       1650.97             927    932.38            625   793.26   \n",
       "7       1260.10             878    711.64           1107   919.73   \n",
       "8       3077.21            1173   1737.85           1303  2628.99   \n",
       "9       1100.18             627   1100.18            608   789.67   \n",
       "\n",
       "   max_over_days  \n",
       "5           1530  \n",
       "6            927  \n",
       "7           1107  \n",
       "8           1303  \n",
       "9            627  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25250, 20), (83859, 20))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, dt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npl_group</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>account_ssuda</th>\n",
       "      <th>teyinat</th>\n",
       "      <th>kr_kx</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>amount</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
       "      <th>res_pricipal</th>\n",
       "      <th>over_days_prin</th>\n",
       "      <th>arr_prin</th>\n",
       "      <th>over_days_int</th>\n",
       "      <th>arr_int</th>\n",
       "      <th>max_over_days</th>\n",
       "      <th>dft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70000612</td>\n",
       "      <td>1-70000612-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1145</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1530</td>\n",
       "      <td>2169.45</td>\n",
       "      <td>1530</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70001320</td>\n",
       "      <td>1-70001320-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1650.97</td>\n",
       "      <td>927</td>\n",
       "      <td>932.38</td>\n",
       "      <td>625</td>\n",
       "      <td>793.26</td>\n",
       "      <td>927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70004187</td>\n",
       "      <td>1-70004187-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1260.10</td>\n",
       "      <td>878</td>\n",
       "      <td>711.64</td>\n",
       "      <td>1107</td>\n",
       "      <td>919.73</td>\n",
       "      <td>1107</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70005562</td>\n",
       "      <td>1-70005562-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>3077.21</td>\n",
       "      <td>1173</td>\n",
       "      <td>1737.85</td>\n",
       "      <td>1303</td>\n",
       "      <td>2628.99</td>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70006140</td>\n",
       "      <td>1-70006140-6</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>627</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>608</td>\n",
       "      <td>789.67</td>\n",
       "      <td>627</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  npl_group  branch_code   cust_id account_ssuda   teyinat kr_kx date_start  \\\n",
       "5       PKD            1  70000612  1-70000612-5  Istehlak    KR 2013-07-11   \n",
       "6       PKD            1  70001320  1-70001320-4  Istehlak    KR 2013-07-17   \n",
       "7       PKD            1  70004187  1-70004187-4  Istehlak    KR 2013-08-05   \n",
       "8       PKD            1  70005562  1-70005562-5  Istehlak    KR 2013-08-14   \n",
       "9       PKD            1  70006140  1-70006140-6  Istehlak    KR 2014-03-12   \n",
       "\n",
       "    date_end  maturity  amount  intrate  annuity currency  exrate  \\\n",
       "5 2015-01-11        18  1500.0     33.0      0.0      AZN  1.0000   \n",
       "6 2015-01-17        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "7 2015-02-05        18  1660.0     33.0      0.0      USD  1.7707   \n",
       "8 2015-02-14        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "9 2016-03-12        24  2000.0     32.0      0.0      AZN  1.0000   \n",
       "\n",
       "   res_pricipal  over_days_prin  arr_prin  over_days_int  arr_int  \\\n",
       "5       1287.91            1145   1287.91           1530  2169.45   \n",
       "6       1650.97             927    932.38            625   793.26   \n",
       "7       1260.10             878    711.64           1107   919.73   \n",
       "8       3077.21            1173   1737.85           1303  2628.99   \n",
       "9       1100.18             627   1100.18            608   789.67   \n",
       "\n",
       "   max_over_days  dft  \n",
       "5           1530    1  \n",
       "6            927    1  \n",
       "7           1107    1  \n",
       "8           1303    1  \n",
       "9            627    1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.df['dft']=df.max_over_days.apply(lambda x: int((x>360)))\n",
    "\n",
    "df['dft']=(df.max_over_days>360).astype(np.int8)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    13499\n",
       "0    11751\n",
       "Name: dft, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dft.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13499"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dft.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>npl_group</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>account_ssuda</th>\n",
       "      <th>teyinat</th>\n",
       "      <th>kr_kx</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>amount</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
       "      <th>res_pricipal</th>\n",
       "      <th>over_days_prin</th>\n",
       "      <th>arr_prin</th>\n",
       "      <th>over_days_int</th>\n",
       "      <th>arr_int</th>\n",
       "      <th>max_over_days</th>\n",
       "      <th>dft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70000612</td>\n",
       "      <td>1-70000612-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1145</td>\n",
       "      <td>1287.91</td>\n",
       "      <td>1530</td>\n",
       "      <td>2169.45</td>\n",
       "      <td>1530</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70001320</td>\n",
       "      <td>1-70001320-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1650.97</td>\n",
       "      <td>927</td>\n",
       "      <td>932.38</td>\n",
       "      <td>625</td>\n",
       "      <td>793.26</td>\n",
       "      <td>927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70004187</td>\n",
       "      <td>1-70004187-4</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1260.10</td>\n",
       "      <td>878</td>\n",
       "      <td>711.64</td>\n",
       "      <td>1107</td>\n",
       "      <td>919.73</td>\n",
       "      <td>1107</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70005562</td>\n",
       "      <td>1-70005562-5</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>3077.21</td>\n",
       "      <td>1173</td>\n",
       "      <td>1737.85</td>\n",
       "      <td>1303</td>\n",
       "      <td>2628.99</td>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PKD</td>\n",
       "      <td>1</td>\n",
       "      <td>70006140</td>\n",
       "      <td>1-70006140-6</td>\n",
       "      <td>Istehlak</td>\n",
       "      <td>KR</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>627</td>\n",
       "      <td>1100.18</td>\n",
       "      <td>608</td>\n",
       "      <td>789.67</td>\n",
       "      <td>627</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  npl_group  branch_code   cust_id account_ssuda   teyinat kr_kx date_start  \\\n",
       "5       PKD            1  70000612  1-70000612-5  Istehlak    KR 2013-07-11   \n",
       "6       PKD            1  70001320  1-70001320-4  Istehlak    KR 2013-07-17   \n",
       "7       PKD            1  70004187  1-70004187-4  Istehlak    KR 2013-08-05   \n",
       "8       PKD            1  70005562  1-70005562-5  Istehlak    KR 2013-08-14   \n",
       "9       PKD            1  70006140  1-70006140-6  Istehlak    KR 2014-03-12   \n",
       "\n",
       "    date_end  maturity  amount  intrate  annuity currency  exrate  \\\n",
       "5 2015-01-11        18  1500.0     33.0      0.0      AZN  1.0000   \n",
       "6 2015-01-17        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "7 2015-02-05        18  1660.0     33.0      0.0      USD  1.7707   \n",
       "8 2015-02-14        18  1900.0     33.0      0.0      USD  1.7707   \n",
       "9 2016-03-12        24  2000.0     32.0      0.0      AZN  1.0000   \n",
       "\n",
       "   res_pricipal  over_days_prin  arr_prin  over_days_int  arr_int  \\\n",
       "5       1287.91            1145   1287.91           1530  2169.45   \n",
       "6       1650.97             927    932.38            625   793.26   \n",
       "7       1260.10             878    711.64           1107   919.73   \n",
       "8       3077.21            1173   1737.85           1303  2628.99   \n",
       "9       1100.18             627   1100.18            608   789.67   \n",
       "\n",
       "   max_over_days  dft  \n",
       "5           1530    1  \n",
       "6            927    1  \n",
       "7           1107    1  \n",
       "8           1303    1  \n",
       "9            627    1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[(df.max_over_days>360)|(df.max_over_days<=120)]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1    13499\n",
       " 0     7685\n",
       " Name: dft, dtype: int64, (21184, 21))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dft.value_counts(), df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['npl_group', 'branch_code', 'cust_id', 'account_ssuda', 'teyinat',\n",
       "       'kr_kx', 'date_start', 'date_end', 'maturity', 'amount', 'intrate',\n",
       "       'annuity', 'currency', 'exrate', 'res_pricipal', 'over_days_prin',\n",
       "       'arr_prin', 'over_days_int', 'arr_int', 'max_over_days', 'dft'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>branch_code</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>amount</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
       "      <th>dft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   branch_code date_start   date_end  maturity  amount  intrate  annuity  \\\n",
       "5            1 2013-07-11 2015-01-11        18  1500.0     33.0      0.0   \n",
       "6            1 2013-07-17 2015-01-17        18  1900.0     33.0      0.0   \n",
       "7            1 2013-08-05 2015-02-05        18  1660.0     33.0      0.0   \n",
       "8            1 2013-08-14 2015-02-14        18  1900.0     33.0      0.0   \n",
       "9            1 2014-03-12 2016-03-12        24  2000.0     32.0      0.0   \n",
       "\n",
       "  currency  exrate  dft  \n",
       "5      AZN  1.0000    1  \n",
       "6      USD  1.7707    1  \n",
       "7      USD  1.7707    1  \n",
       "8      USD  1.7707    1  \n",
       "9      AZN  1.0000    1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm=df.drop(columns=['npl_group','cust_id','account_ssuda','teyinat','kr_kx','res_pricipal', 'over_days_prin',\n",
    "'arr_prin', 'over_days_int', 'arr_int', 'max_over_days'])\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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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>branch_code</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>currency</th>\n",
       "      <th>exrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>2939.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   branch_code date_start   date_end  maturity  intrate  annuity currency  \\\n",
       "5            1 2013-07-11 2015-01-11        18     33.0      0.0      AZN   \n",
       "6            1 2013-07-17 2015-01-17        18     33.0      0.0      USD   \n",
       "7            1 2013-08-05 2015-02-05        18     33.0      0.0      USD   \n",
       "8            1 2013-08-14 2015-02-14        18     33.0      0.0      USD   \n",
       "9            1 2014-03-12 2016-03-12        24     32.0      0.0      AZN   \n",
       "\n",
       "   exrate  dft  clt_amount  \n",
       "5  1.0000    1    1500.000  \n",
       "6  1.7707    1    3364.330  \n",
       "7  1.7707    1    2939.362  \n",
       "8  1.7707    1    3364.330  \n",
       "9  1.0000    1    2000.000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculated amount\n",
    "dm['clt_amount']=dm['amount']*dm['exrate']\n",
    "dm.drop(columns=['amount'], inplace=True)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USD    12956\n",
       "AZN     8224\n",
       "EUR        4\n",
       "Name: currency, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.currency.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch_code</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_end</th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>annuity</th>\n",
       "      <th>exrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>cur_AZN</th>\n",
       "      <th>cur_EUR</th>\n",
       "      <th>cur_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>2015-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>2939.362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2015-02-14</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7707</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   branch_code date_start   date_end  maturity  intrate  annuity  exrate  dft  \\\n",
       "5            1 2013-07-11 2015-01-11        18     33.0      0.0  1.0000    1   \n",
       "6            1 2013-07-17 2015-01-17        18     33.0      0.0  1.7707    1   \n",
       "7            1 2013-08-05 2015-02-05        18     33.0      0.0  1.7707    1   \n",
       "8            1 2013-08-14 2015-02-14        18     33.0      0.0  1.7707    1   \n",
       "9            1 2014-03-12 2016-03-12        24     32.0      0.0  1.0000    1   \n",
       "\n",
       "   clt_amount  cur_AZN  cur_EUR  cur_USD  \n",
       "5    1500.000        1        0        0  \n",
       "6    3364.330        0        0        1  \n",
       "7    2939.362        0        0        1  \n",
       "8    3364.330        0        0        1  \n",
       "9    2000.000        1        0        0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm=pd.get_dummies(dm, columns=['currency'],prefix=['cur'])\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to take year and month\n",
    "def yd(d):\n",
    "    return str(d)[0:4]\n",
    "def mn(a):\n",
    "    return str(a)[5:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>date_start</th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>cur_AZN</th>\n",
       "      <th>cur_EUR</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2939.362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_start  maturity  intrate  dft  clt_amount  cur_AZN  cur_EUR  year month\n",
       "5 2013-07-11        18     33.0    1    1500.000        1        0  2013    07\n",
       "6 2013-07-17        18     33.0    1    3364.330        0        0  2013    07\n",
       "7 2013-08-05        18     33.0    1    2939.362        0        0  2013    08\n",
       "8 2013-08-14        18     33.0    1    3364.330        0        0  2013    08\n",
       "9 2014-03-12        24     32.0    1    2000.000        1        0  2014    03"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm['year']=dm.date_start.apply(yd)\n",
    "dm['month']=dm.date_start.apply(mn)\n",
    "dm.drop(columns=['branch_code','date_end', 'annuity', 'exrate', 'cur_USD'], inplace=True)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2014    9618\n",
       " 2013    5484\n",
       " 2015    4868\n",
       " 2016    1214\n",
       " Name: year, dtype: int64, 10    2448\n",
       " 12    2427\n",
       " 11    2237\n",
       " 09    2228\n",
       " 08    1841\n",
       " 01    1800\n",
       " 07    1661\n",
       " 02    1570\n",
       " 06    1338\n",
       " 05    1309\n",
       " 04    1224\n",
       " 03    1101\n",
       " Name: month, dtype: int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.year.value_counts(), dm.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_start</th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>cur_AZN</th>\n",
       "      <th>cur_EUR</th>\n",
       "      <th>yr_2013</th>\n",
       "      <th>yr_2014</th>\n",
       "      <th>yr_2015</th>\n",
       "      <th>yr_2016</th>\n",
       "      <th>mn_01</th>\n",
       "      <th>mn_02</th>\n",
       "      <th>mn_03</th>\n",
       "      <th>mn_04</th>\n",
       "      <th>mn_05</th>\n",
       "      <th>mn_06</th>\n",
       "      <th>mn_07</th>\n",
       "      <th>mn_08</th>\n",
       "      <th>mn_09</th>\n",
       "      <th>mn_10</th>\n",
       "      <th>mn_11</th>\n",
       "      <th>mn_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-07-11</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-07-17</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-08-05</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2939.362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-03-12</td>\n",
       "      <td>24</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_start  maturity  intrate  dft  clt_amount  cur_AZN  cur_EUR  yr_2013  \\\n",
       "5 2013-07-11        18     33.0    1    1500.000        1        0        1   \n",
       "6 2013-07-17        18     33.0    1    3364.330        0        0        1   \n",
       "7 2013-08-05        18     33.0    1    2939.362        0        0        1   \n",
       "8 2013-08-14        18     33.0    1    3364.330        0        0        1   \n",
       "9 2014-03-12        24     32.0    1    2000.000        1        0        0   \n",
       "\n",
       "   yr_2014  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  mn_05  mn_06  mn_07  \\\n",
       "5        0        0        0      0      0      0      0      0      0      1   \n",
       "6        0        0        0      0      0      0      0      0      0      1   \n",
       "7        0        0        0      0      0      0      0      0      0      0   \n",
       "8        0        0        0      0      0      0      0      0      0      0   \n",
       "9        1        0        0      0      0      1      0      0      0      0   \n",
       "\n",
       "   mn_08  mn_09  mn_10  mn_11  mn_12  \n",
       "5      0      0      0      0      0  \n",
       "6      0      0      0      0      0  \n",
       "7      1      0      0      0      0  \n",
       "8      1      0      0      0      0  \n",
       "9      0      0      0      0      0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dummy variables on year and month\n",
    "dm=pd.get_dummies(dm, prefix=['yr','mn'], columns=['year', 'month'])\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>cur_AZN</th>\n",
       "      <th>cur_EUR</th>\n",
       "      <th>yr_2013</th>\n",
       "      <th>yr_2015</th>\n",
       "      <th>yr_2016</th>\n",
       "      <th>mn_01</th>\n",
       "      <th>mn_02</th>\n",
       "      <th>mn_03</th>\n",
       "      <th>mn_04</th>\n",
       "      <th>mn_05</th>\n",
       "      <th>mn_06</th>\n",
       "      <th>mn_07</th>\n",
       "      <th>mn_08</th>\n",
       "      <th>mn_09</th>\n",
       "      <th>mn_11</th>\n",
       "      <th>mn_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maturity  intrate  dft  clt_amount  cur_AZN  cur_EUR  yr_2013  yr_2015  \\\n",
       "5        18     33.0    1    1500.000        1        0        1        0   \n",
       "6        18     33.0    1    3364.330        0        0        1        0   \n",
       "7        18     33.0    1    2939.362        0        0        1        0   \n",
       "8        18     33.0    1    3364.330        0        0        1        0   \n",
       "9        24     32.0    1    2000.000        1        0        0        0   \n",
       "\n",
       "   yr_2016  mn_01  mn_02  mn_03  mn_04  mn_05  mn_06  mn_07  mn_08  mn_09  \\\n",
       "5        0      0      0      0      0      0      0      1      0      0   \n",
       "6        0      0      0      0      0      0      0      1      0      0   \n",
       "7        0      0      0      0      0      0      0      0      1      0   \n",
       "8        0      0      0      0      0      0      0      0      1      0   \n",
       "9        0      0      0      1      0      0      0      0      0      0   \n",
       "\n",
       "   mn_11  mn_12  \n",
       "5      0      0  \n",
       "6      0      0  \n",
       "7      0      0  \n",
       "8      0      0  \n",
       "9      0      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping mostly existed dummy variables to avoid multicollinearity\n",
    "dm.drop(columns=['date_start','yr_2014','mn_10'], inplace=True)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pl=sp.PolynomialFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1500.000</td>\n",
       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>27000.000</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>49500.000</td>\n",
       "      <td>2.250000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>60557.940</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>111022.890</td>\n",
       "      <td>1.131872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2939.362</td>\n",
       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>52908.516</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>96998.946</td>\n",
       "      <td>8.639849e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>60557.940</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>111022.890</td>\n",
       "      <td>1.131872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>576.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>48000.000</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>64000.000</td>\n",
       "      <td>4.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1     2         3      4      5          6       7           8  \\\n",
       "0  1.0  18.0  33.0  1500.000  324.0  594.0  27000.000  1089.0   49500.000   \n",
       "1  1.0  18.0  33.0  3364.330  324.0  594.0  60557.940  1089.0  111022.890   \n",
       "2  1.0  18.0  33.0  2939.362  324.0  594.0  52908.516  1089.0   96998.946   \n",
       "3  1.0  18.0  33.0  3364.330  324.0  594.0  60557.940  1089.0  111022.890   \n",
       "4  1.0  24.0  32.0  2000.000  576.0  768.0  48000.000  1024.0   64000.000   \n",
       "\n",
       "              9  \n",
       "0  2.250000e+06  \n",
       "1  1.131872e+07  \n",
       "2  8.639849e+06  \n",
       "3  1.131872e+07  \n",
       "4  4.000000e+06  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly=pd.DataFrame(Pl.fit_transform(dm[['maturity','intrate','clt_amount']]))\n",
    "poly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " 'maturity',\n",
       " 'intrate',\n",
       " 'clt_amount',\n",
       " 'maturity^2',\n",
       " 'maturity intrate',\n",
       " 'maturity clt_amount',\n",
       " 'intrate^2',\n",
       " 'intrate clt_amount',\n",
       " 'clt_amount^2']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pl.get_feature_names(input_features=['maturity','intrate','clt_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>maturity</th>\n",
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       "      <th>maturity^2</th>\n",
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       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
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       "      <td>594.0</td>\n",
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       "      <td>111022.890</td>\n",
       "      <td>1.131872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>576.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>48000.000</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>64000.000</td>\n",
       "      <td>4.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maturity  intrate  clt_amount  maturity^2  maturity__intrate  \\\n",
       "0      18.0     33.0    1500.000       324.0              594.0   \n",
       "1      18.0     33.0    3364.330       324.0              594.0   \n",
       "2      18.0     33.0    2939.362       324.0              594.0   \n",
       "3      18.0     33.0    3364.330       324.0              594.0   \n",
       "4      24.0     32.0    2000.000       576.0              768.0   \n",
       "\n",
       "   maturity__clt_amount  intrate^2  intrate__clt_amount  clt_amount^2  \n",
       "0             27000.000     1089.0            49500.000  2.250000e+06  \n",
       "1             60557.940     1089.0           111022.890  1.131872e+07  \n",
       "2             52908.516     1089.0            96998.946  8.639849e+06  \n",
       "3             60557.940     1089.0           111022.890  1.131872e+07  \n",
       "4             48000.000     1024.0            64000.000  4.000000e+06  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.columns=['1',\n",
    " 'maturity',\n",
    " 'intrate',\n",
    " 'clt_amount',\n",
    " 'maturity^2',\n",
    " 'maturity__intrate',\n",
    " 'maturity__clt_amount',\n",
    " 'intrate^2',\n",
    " 'intrate__clt_amount',\n",
    " 'clt_amount^2']\n",
    "poly.drop(columns=['1'], inplace=True)\n",
    "poly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>dft</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>cur_AZN</th>\n",
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       "    </tr>\n",
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       "      <td>18</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3364.330</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maturity  intrate  dft  clt_amount  cur_AZN  cur_EUR  yr_2013  yr_2015  \\\n",
       "0        18     33.0    1    1500.000        1        0        1        0   \n",
       "1        18     33.0    1    3364.330        0        0        1        0   \n",
       "2        18     33.0    1    2939.362        0        0        1        0   \n",
       "3        18     33.0    1    3364.330        0        0        1        0   \n",
       "4        24     32.0    1    2000.000        1        0        0        0   \n",
       "\n",
       "   yr_2016  mn_01  mn_02  mn_03  mn_04  mn_05  mn_06  mn_07  mn_08  mn_09  \\\n",
       "0        0      0      0      0      0      0      0      1      0      0   \n",
       "1        0      0      0      0      0      0      0      1      0      0   \n",
       "2        0      0      0      0      0      0      0      0      1      0   \n",
       "3        0      0      0      0      0      0      0      0      1      0   \n",
       "4        0      0      0      1      0      0      0      0      0      0   \n",
       "\n",
       "   mn_11  mn_12  \n",
       "0      0      0  \n",
       "1      0      0  \n",
       "2      0      0  \n",
       "3      0      0  \n",
       "4      0      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.reset_index(drop=True, inplace=True)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>111022.890</td>\n",
       "      <td>1.131872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>52908.516</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>96998.946</td>\n",
       "      <td>8.639849e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3364.330</td>\n",
       "      <td>324.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>60557.940</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>111022.890</td>\n",
       "      <td>1.131872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>576.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>48000.000</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>64000.000</td>\n",
       "      <td>4.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dft  cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  \\\n",
       "0    1        1        0        1        0        0      0      0      0   \n",
       "1    1        0        0        1        0        0      0      0      0   \n",
       "2    1        0        0        1        0        0      0      0      0   \n",
       "3    1        0        0        1        0        0      0      0      0   \n",
       "4    1        1        0        0        0        0      0      0      1   \n",
       "\n",
       "   mn_04  mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  maturity  intrate  \\\n",
       "0      0      0      0      1      0      0      0      0      18.0     33.0   \n",
       "1      0      0      0      1      0      0      0      0      18.0     33.0   \n",
       "2      0      0      0      0      1      0      0      0      18.0     33.0   \n",
       "3      0      0      0      0      1      0      0      0      18.0     33.0   \n",
       "4      0      0      0      0      0      0      0      0      24.0     32.0   \n",
       "\n",
       "   clt_amount  maturity^2  maturity__intrate  maturity__clt_amount  intrate^2  \\\n",
       "0    1500.000       324.0              594.0             27000.000     1089.0   \n",
       "1    3364.330       324.0              594.0             60557.940     1089.0   \n",
       "2    2939.362       324.0              594.0             52908.516     1089.0   \n",
       "3    3364.330       324.0              594.0             60557.940     1089.0   \n",
       "4    2000.000       576.0              768.0             48000.000     1024.0   \n",
       "\n",
       "   intrate__clt_amount  clt_amount^2  \n",
       "0            49500.000  2.250000e+06  \n",
       "1           111022.890  1.131872e+07  \n",
       "2            96998.946  8.639849e+06  \n",
       "3           111022.890  1.131872e+07  \n",
       "4            64000.000  4.000000e+06  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm=dm.drop(columns=['maturity','intrate','clt_amount']).merge(poly, left_index=True, right_index=True)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(dm.drop(columns=['dft']),dm[['dft']], test_size=0.25, random_state=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train.reset_index(drop=True, inplace=True)\n",
    "y_train.reset_index(drop=True, inplace=True)\n",
    "x_test.reset_index(drop=True, inplace=True)\n",
    "y_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "nms=['maturity',\n",
    " 'intrate',\n",
    " 'clt_amount',\n",
    " 'maturity^2',\n",
    " 'maturity__intrate',\n",
    " 'maturity__clt_amount',\n",
    " 'intrate^2',\n",
    " 'intrate__clt_amount',\n",
    " 'clt_amount^2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb=sp.RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>maturity</th>\n",
       "      <th>intrate</th>\n",
       "      <th>clt_amount</th>\n",
       "      <th>maturity^2</th>\n",
       "      <th>maturity__intrate</th>\n",
       "      <th>maturity__clt_amount</th>\n",
       "      <th>intrate^2</th>\n",
       "      <th>intrate__clt_amount</th>\n",
       "      <th>clt_amount^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.1</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1.813864</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>1.150943</td>\n",
       "      <td>3.141489</td>\n",
       "      <td>-1.090909</td>\n",
       "      <td>1.338796</td>\n",
       "      <td>3.295488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.288458</td>\n",
       "      <td>-0.547826</td>\n",
       "      <td>-0.377358</td>\n",
       "      <td>-0.278492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.276395</td>\n",
       "      <td>-0.197556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.215460</td>\n",
       "      <td>-0.939130</td>\n",
       "      <td>-0.905660</td>\n",
       "      <td>-0.344258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.200152</td>\n",
       "      <td>-0.156031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.117172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.884486</td>\n",
       "      <td>0.872727</td>\n",
       "      <td>1.474821</td>\n",
       "      <td>1.610638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.380198</td>\n",
       "      <td>-0.939130</td>\n",
       "      <td>-0.905660</td>\n",
       "      <td>-0.406926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.372212</td>\n",
       "      <td>-0.241605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  maturity intrate clt_amount maturity^2 maturity__intrate  \\\n",
       "0      2.1    -1.2   1.813864   3.150000          1.150943   \n",
       "1     -0.6     0.0  -0.288458  -0.547826         -0.377358   \n",
       "2     -1.2     0.0  -0.215460  -0.939130         -0.905660   \n",
       "3      0.0     0.8   1.117172   0.000000          0.452830   \n",
       "4     -1.2     0.0  -0.380198  -0.939130         -0.905660   \n",
       "\n",
       "  maturity__clt_amount intrate^2 intrate__clt_amount clt_amount^2  \n",
       "0             3.141489 -1.090909            1.338796     3.295488  \n",
       "1            -0.278492  0.000000           -0.276395    -0.197556  \n",
       "2            -0.344258  0.000000           -0.200152    -0.156031  \n",
       "3             0.884486  0.872727            1.474821     1.610638  \n",
       "4            -0.406926  0.000000           -0.372212    -0.241605  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_x_train=pd.DataFrame(rb.fit_transform(x_train[nms]), \n",
    "columns=[nms])\n",
    "sc_x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
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      "text/plain": [
       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        0        0        0        0        0      0      0      0      0   \n",
       "1        0        0        0        0        0      0      0      0      0   \n",
       "2        0        0        1        0        0      0      0      1      0   \n",
       "3        0        0        0        1        0      1      0      0      0   \n",
       "4        0        0        0        0        0      0      1      0      0   \n",
       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  maturity  intrate  \\\n",
       "0      0      0      0      0      1      0      0      45.0     22.0   \n",
       "1      0      0      0      0      0      1      0      18.0     28.0   \n",
       "2      0      0      0      0      0      0      0      12.0     28.0   \n",
       "3      0      0      0      0      0      0      0      24.0     32.0   \n",
       "4      0      0      0      0      0      0      0      12.0     28.0   \n",
       "\n",
       "     clt_amount  maturity^2  maturity__intrate  maturity__clt_amount  \\\n",
       "0  10288.723178      2025.0              990.0         462992.543010   \n",
       "1   1950.408343       324.0              504.0          35107.350174   \n",
       "2   2239.935500       144.0              336.0          26879.226000   \n",
       "3   7525.475000       576.0              768.0         180611.400000   \n",
       "4   1586.547200       144.0              336.0          19038.566400   \n",
       "\n",
       "   intrate^2  intrate__clt_amount  clt_amount^2  \n",
       "0      484.0        226351.909916  1.058578e+08  \n",
       "1      784.0         54611.433604  3.804093e+06  \n",
       "2      784.0         62718.194000  5.017311e+06  \n",
       "3     1024.0        240815.200000  5.663277e+07  \n",
       "4      784.0         44423.321600  2.517132e+06  "
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     "execution_count": 35,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        0        0        0        0        0      0      0      0      0   \n",
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       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  \n",
       "0      0      0      0      0      1      0      0  \n",
       "1      0      0      0      0      0      1      0  \n",
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       "4      0      0      0      0      0      0      0  "
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.drop(columns=nms, inplace=True)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "      <th>mn_08</th>\n",
       "      <th>mn_09</th>\n",
       "      <th>mn_11</th>\n",
       "      <th>mn_12</th>\n",
       "      <th>(maturity,)</th>\n",
       "      <th>(intrate,)</th>\n",
       "      <th>(clt_amount,)</th>\n",
       "      <th>(maturity^2,)</th>\n",
       "      <th>(maturity__intrate,)</th>\n",
       "      <th>(maturity__clt_amount,)</th>\n",
       "      <th>(intrate^2,)</th>\n",
       "      <th>(intrate__clt_amount,)</th>\n",
       "      <th>(clt_amount^2,)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1.813864</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>1.150943</td>\n",
       "      <td>3.141489</td>\n",
       "      <td>-1.090909</td>\n",
       "      <td>1.338796</td>\n",
       "      <td>3.295488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.288458</td>\n",
       "      <td>-0.547826</td>\n",
       "      <td>-0.377358</td>\n",
       "      <td>-0.278492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.276395</td>\n",
       "      <td>-0.197556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.215460</td>\n",
       "      <td>-0.939130</td>\n",
       "      <td>-0.905660</td>\n",
       "      <td>-0.344258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.200152</td>\n",
       "      <td>-0.156031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>1.117172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.884486</td>\n",
       "      <td>0.872727</td>\n",
       "      <td>1.474821</td>\n",
       "      <td>1.610638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.380198</td>\n",
       "      <td>-0.939130</td>\n",
       "      <td>-0.905660</td>\n",
       "      <td>-0.406926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.372212</td>\n",
       "      <td>-0.241605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        0        0        0        0        0      0      0      0      0   \n",
       "1        0        0        0        0        0      0      0      0      0   \n",
       "2        0        0        1        0        0      0      0      1      0   \n",
       "3        0        0        0        1        0      1      0      0      0   \n",
       "4        0        0        0        0        0      0      1      0      0   \n",
       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  (maturity,)  (intrate,)  \\\n",
       "0      0      0      0      0      1      0      0          2.1        -1.2   \n",
       "1      0      0      0      0      0      1      0         -0.6         0.0   \n",
       "2      0      0      0      0      0      0      0         -1.2         0.0   \n",
       "3      0      0      0      0      0      0      0          0.0         0.8   \n",
       "4      0      0      0      0      0      0      0         -1.2         0.0   \n",
       "\n",
       "   (clt_amount,)  (maturity^2,)  (maturity__intrate,)  \\\n",
       "0       1.813864       3.150000              1.150943   \n",
       "1      -0.288458      -0.547826             -0.377358   \n",
       "2      -0.215460      -0.939130             -0.905660   \n",
       "3       1.117172       0.000000              0.452830   \n",
       "4      -0.380198      -0.939130             -0.905660   \n",
       "\n",
       "   (maturity__clt_amount,)  (intrate^2,)  (intrate__clt_amount,)  \\\n",
       "0                 3.141489     -1.090909                1.338796   \n",
       "1                -0.278492      0.000000               -0.276395   \n",
       "2                -0.344258      0.000000               -0.200152   \n",
       "3                 0.884486      0.872727                1.474821   \n",
       "4                -0.406926      0.000000               -0.372212   \n",
       "\n",
       "   (clt_amount^2,)  \n",
       "0         3.295488  \n",
       "1        -0.197556  \n",
       "2        -0.156031  \n",
       "3         1.610638  \n",
       "4        -0.241605  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train=x_train.merge(sc_x_train,left_index=True, right_index=True)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                'cur_AZN',                 'cur_EUR',\n",
       "                       'yr_2013',                 'yr_2015',\n",
       "                       'yr_2016',                   'mn_01',\n",
       "                         'mn_02',                   'mn_03',\n",
       "                         'mn_04',                   'mn_05',\n",
       "                         'mn_06',                   'mn_07',\n",
       "                         'mn_08',                   'mn_09',\n",
       "                         'mn_11',                   'mn_12',\n",
       "                   ('maturity',),              ('intrate',),\n",
       "                 ('clt_amount',),           ('maturity^2',),\n",
       "          ('maturity__intrate',), ('maturity__clt_amount',),\n",
       "                  ('intrate^2',),  ('intrate__clt_amount',),\n",
       "               ('clt_amount^2',)],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cur_AZN</th>\n",
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       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        0        0        0        0        0      0      0      0      0   \n",
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       "2        0        0        1        0        0      0      0      1      0   \n",
       "3        0        0        0        1        0      1      0      0      0   \n",
       "4        0        0        0        0        0      0      1      0      0   \n",
       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  maturity  intrate  \\\n",
       "0      0      0      0      0      1      0      0       2.1     -1.2   \n",
       "1      0      0      0      0      0      1      0      -0.6      0.0   \n",
       "2      0      0      0      0      0      0      0      -1.2      0.0   \n",
       "3      0      0      0      0      0      0      0       0.0      0.8   \n",
       "4      0      0      0      0      0      0      0      -1.2      0.0   \n",
       "\n",
       "   clt_amount  maturity^2  maturity__intrate  maturity__clt_amount  intrate^2  \\\n",
       "0    1.813864    3.150000           1.150943              3.141489  -1.090909   \n",
       "1   -0.288458   -0.547826          -0.377358             -0.278492   0.000000   \n",
       "2   -0.215460   -0.939130          -0.905660             -0.344258   0.000000   \n",
       "3    1.117172    0.000000           0.452830              0.884486   0.872727   \n",
       "4   -0.380198   -0.939130          -0.905660             -0.406926   0.000000   \n",
       "\n",
       "   intrate__clt_amount  clt_amount^2  \n",
       "0             1.338796      3.295488  \n",
       "1            -0.276395     -0.197556  \n",
       "2            -0.200152     -0.156031  \n",
       "3             1.474821      1.610638  \n",
       "4            -0.372212     -0.241605  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.columns=['cur_AZN','cur_EUR',\n",
    "'yr_2013',  'yr_2015',\n",
    "'yr_2016', 'mn_01','mn_02','mn_03','mn_04','mn_05',\n",
    "'mn_06','mn_07','mn_08','mn_09','mn_11','mn_12','maturity','intrate',\n",
    "'clt_amount', 'maturity^2','maturity__intrate', 'maturity__clt_amount',\n",
    "'intrate^2',  'intrate__clt_amount','clt_amount^2']\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.275955</td>\n",
       "      <td>-0.939130</td>\n",
       "      <td>-0.830189</td>\n",
       "      <td>-0.367271</td>\n",
       "      <td>0.421818</td>\n",
       "      <td>-0.225716</td>\n",
       "      <td>-0.190851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.360108</td>\n",
       "      <td>-0.989130</td>\n",
       "      <td>-0.993711</td>\n",
       "      <td>-0.412601</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.351229</td>\n",
       "      <td>-0.232734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.409663</td>\n",
       "      <td>-0.547826</td>\n",
       "      <td>-0.377358</td>\n",
       "      <td>-0.347654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.402987</td>\n",
       "      <td>-0.253831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.635078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150943</td>\n",
       "      <td>2.039352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.777096</td>\n",
       "      <td>5.952649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maturity  intrate  clt_amount  maturity^2  maturity__intrate  \\\n",
       "0       0.0      0.0   -0.402019    0.000000           0.150943   \n",
       "1      -1.2      0.4   -0.275955   -0.939130          -0.830189   \n",
       "2      -1.3      0.0   -0.360108   -0.989130          -0.993711   \n",
       "3      -0.6      0.0   -0.409663   -0.547826          -0.377358   \n",
       "4       0.0      0.0    2.635078    0.000000           0.150943   \n",
       "\n",
       "   maturity__clt_amount  intrate^2  intrate__clt_amount  clt_amount^2  \n",
       "0             -0.271358   0.000000            -0.395003     -0.250749  \n",
       "1             -0.367271   0.421818            -0.225716     -0.190851  \n",
       "2             -0.412601   0.000000            -0.351229     -0.232734  \n",
       "3             -0.347654   0.000000            -0.402987     -0.253831  \n",
       "4              2.039352   0.000000             2.777096      5.952649  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_x_test=pd.DataFrame(rb.transform(x_test[nms]), columns=nms)\n",
    "sc_x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cur_AZN</th>\n",
       "      <th>cur_EUR</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        1        0        0        1        0      0      0      0      0   \n",
       "1        1        0        1        0        0      0      0      0      0   \n",
       "2        0        0        0        0        0      0      0      0      0   \n",
       "3        0        0        1        0        0      0      0      0      0   \n",
       "4        0        0        0        0        0      0      0      0      0   \n",
       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  \n",
       "0      0      0      0      0      0      0      1  \n",
       "1      0      0      0      1      0      0      0  \n",
       "2      0      0      0      0      0      0      1  \n",
       "3      0      0      0      1      0      0      0  \n",
       "4      0      1      0      0      0      0      0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.drop(columns=nms, inplace=True)\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>5.952649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cur_AZN  cur_EUR  yr_2013  yr_2015  yr_2016  mn_01  mn_02  mn_03  mn_04  \\\n",
       "0        1        0        0        1        0      0      0      0      0   \n",
       "1        1        0        1        0        0      0      0      0      0   \n",
       "2        0        0        0        0        0      0      0      0      0   \n",
       "3        0        0        1        0        0      0      0      0      0   \n",
       "4        0        0        0        0        0      0      0      0      0   \n",
       "\n",
       "   mn_05  mn_06  mn_07  mn_08  mn_09  mn_11  mn_12  maturity  intrate  \\\n",
       "0      0      0      0      0      0      0      1       0.0      0.0   \n",
       "1      0      0      0      1      0      0      0      -1.2      0.4   \n",
       "2      0      0      0      0      0      0      1      -1.3      0.0   \n",
       "3      0      0      0      1      0      0      0      -0.6      0.0   \n",
       "4      0      1      0      0      0      0      0       0.0      0.0   \n",
       "\n",
       "   clt_amount  maturity^2  maturity__intrate  maturity__clt_amount  intrate^2  \\\n",
       "0   -0.402019    0.000000           0.150943             -0.271358   0.000000   \n",
       "1   -0.275955   -0.939130          -0.830189             -0.367271   0.421818   \n",
       "2   -0.360108   -0.989130          -0.993711             -0.412601   0.000000   \n",
       "3   -0.409663   -0.547826          -0.377358             -0.347654   0.000000   \n",
       "4    2.635078    0.000000           0.150943              2.039352   0.000000   \n",
       "\n",
       "   intrate__clt_amount  clt_amount^2  \n",
       "0            -0.395003     -0.250749  \n",
       "1            -0.225716     -0.190851  \n",
       "2            -0.351229     -0.232734  \n",
       "3            -0.402987     -0.253831  \n",
       "4             2.777096      5.952649  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test=x_test.merge(sc_x_test, left_index=True, right_index=True)\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [BAS, AS]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to merge all scores and metrics results altogether\n",
    "performance=pd.DataFrame(data=None, index=['BAS','AS'])\n",
    "performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating function to evaluate model at one run\n",
    "def evaluate(model,model_name):\n",
    "    performance[model_name]=[sm.balanced_accuracy_score(y_test, model.predict(x_test)),\n",
    "    sm.accuracy_score(y_test, model.predict(x_test)) ]\n",
    "    \n",
    "    print(sm.classification_report(y_test, model.predict(x_test)))\n",
    "    print(\"--------Balanced Accuracy Score--------\")\n",
    "    print(sm.balanced_accuracy_score(y_test, model.predict(x_test)))\n",
    "    print(\"-------Accuracy Score------------------\")\n",
    "    print(sm.accuracy_score(y_test, model.predict(x_test)))\n",
    "    \n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will do **Hyperparameter tuning** to find **otimal best** parameters in some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 10, 50, 100],\n",
       "                         'penalty': ('l1', 'l2', 'elastic', 'none')})"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'penalty':('l1','l2','elastic', 'none'),'C':[1, 10, 50, 100] }\n",
    "lr1 = slm.LogisticRegression()\n",
    "clf1 = GridSearchCV(lr1, parameters)\n",
    "clf1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.70      0.78      1892\n",
      "           1       0.85      0.94      0.89      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.86      0.82      0.83      5296\n",
      "weighted avg       0.86      0.86      0.85      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8202829403537188\n",
      "-------Accuracy Score------------------\n",
      "0.8557401812688822\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf1, 'Log_reg_0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1\n",
       "0 0.95 0.05\n",
       "1 0.08 0.92\n",
       "2 0.12 0.88\n",
       "3 0.04 0.96\n",
       "4 0.14 0.86"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probabilties of observations\n",
    "pd.DataFrame(clf1.predict_proba(x_test)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6353851963746223"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining cutoff point\n",
    "cutoff=y_train.dft.mean()\n",
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.set_option('display.precision', 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: 1, dtype: int8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prd=(pd.DataFrame(clf1.predict_proba(x_test))[1]>cutoff).astype(np.int8)\n",
    "prd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.75      0.77      1892\n",
      "           1       0.87      0.89      0.88      3404\n",
      "\n",
      "    accuracy                           0.84      5296\n",
      "   macro avg       0.83      0.82      0.82      5296\n",
      "weighted avg       0.84      0.84      0.84      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8201230116043059\n",
      "-------Accuracy Score------------------\n",
      "0.839690332326284\n"
     ]
    }
   ],
   "source": [
    "print(sm.classification_report(y_test, prd))\n",
    "print(\"--------Balanced Accuracy Score--------\")\n",
    "print(sm.balanced_accuracy_score(y_test, prd))\n",
    "print(\"-------Accuracy Score------------------\")\n",
    "print(sm.accuracy_score(y_test, prd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63\n",
       "BAS         0.82          0.82\n",
       "AS          0.86          0.84"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance['Log_reg_0.63']=[sm.balanced_accuracy_score(y_test, prd),\n",
    "    sm.accuracy_score(y_test, prd) ]\n",
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 50}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'C':[1, 10, 50, 100] }\n",
    "svc = svm.SVC()\n",
    "clf2 = GridSearchCV(svc, parameters)\n",
    "clf2.fit(x_train, y_train)\n",
    "clf2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.67      0.77      1892\n",
      "           1       0.84      0.96      0.90      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.87      0.82      0.84      5296\n",
      "weighted avg       0.86      0.86      0.85      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8175560775409108\n",
      "-------Accuracy Score------------------\n",
      "0.8585725075528701\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf2, 'SVC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC\n",
       "BAS         0.82          0.82 0.82\n",
       "AS          0.86          0.84 0.86"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RandomForest classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 190}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = { 'n_estimators':[i for i in range(150,200,10)] }\n",
    "rfc = sen.RandomForestClassifier(random_state=25)\n",
    "clf3 = GridSearchCV(rfc, parameters)\n",
    "clf3.fit(x_train, y_train)\n",
    "\n",
    "clf3.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.70      0.76      1892\n",
      "           1       0.85      0.92      0.88      3404\n",
      "\n",
      "    accuracy                           0.84      5296\n",
      "   macro avg       0.83      0.81      0.82      5296\n",
      "weighted avg       0.84      0.84      0.84      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8093236287118997\n",
      "-------Accuracy Score------------------\n",
      "0.839690332326284\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf3, 'R_Forest_0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "      <th>R_Forest_0.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC  R_Forest_0.5\n",
       "BAS         0.82          0.82 0.82          0.81\n",
       "AS          0.86          0.84 0.86          0.84"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: 1, dtype: int8"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prd=(pd.DataFrame(clf3.predict_proba(x_test))[1]>cutoff).astype(np.int8)\n",
    "prd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.77      0.75      1892\n",
      "           1       0.87      0.85      0.86      3404\n",
      "\n",
      "    accuracy                           0.82      5296\n",
      "   macro avg       0.80      0.81      0.81      5296\n",
      "weighted avg       0.82      0.82      0.82      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8081625149370346\n",
      "-------Accuracy Score------------------\n",
      "0.8194864048338368\n"
     ]
    }
   ],
   "source": [
    "print(sm.classification_report(y_test, prd))\n",
    "print(\"--------Balanced Accuracy Score--------\")\n",
    "print(sm.balanced_accuracy_score(y_test, prd))\n",
    "print(\"-------Accuracy Score------------------\")\n",
    "print(sm.accuracy_score(y_test, prd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "      <th>R_Forest_0.5</th>\n",
       "      <th>R_Forest_0.63</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC  R_Forest_0.5  R_Forest_0.63\n",
       "BAS         0.82          0.82 0.82          0.81           0.81\n",
       "AS          0.86          0.84 0.86          0.84           0.82"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance['R_Forest_0.63']=[sm.balanced_accuracy_score(y_test, prd),\n",
    "sm.accuracy_score(y_test, prd) ]\n",
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ExtraTree classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 130}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = { 'n_estimators':[i for i in range(80,200,10)] }\n",
    "etc = sen.ExtraTreesClassifier(random_state=25)\n",
    "clf4 = GridSearchCV(etc, parameters)\n",
    "clf4.fit(x_train, y_train)\n",
    "\n",
    "clf4.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.72      0.74      1892\n",
      "           1       0.85      0.88      0.86      3404\n",
      "\n",
      "    accuracy                           0.82      5296\n",
      "   macro avg       0.81      0.80      0.80      5296\n",
      "weighted avg       0.82      0.82      0.82      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.7988326132916628\n",
      "-------Accuracy Score------------------\n",
      "0.822129909365559\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf4, 'ExTree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GradientBoosting classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'n_estimators': 200}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = { 'learning_rate':[0.1,0.01,0.001], 'n_estimators':[50,100,150,200,250] }\n",
    "gbc = sen.GradientBoostingClassifier(random_state=25)\n",
    "clf5 = GridSearchCV(gbc, parameters)\n",
    "clf5.fit(x_train, y_train)\n",
    "\n",
    "clf5.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.69      0.78      1892\n",
      "           1       0.85      0.96      0.90      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.88      0.83      0.84      5296\n",
      "weighted avg       0.87      0.86      0.86      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8261301217570176\n",
      "-------Accuracy Score------------------\n",
      "0.8646148036253777\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf5, 'GradientBC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AdaBoost classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 1, 'n_estimators': 200}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = { 'learning_rate':[0.1, 1, 2], 'n_estimators':[50,100,150,200]}\n",
    "abc = sen.AdaBoostClassifier(random_state=25)\n",
    "clf6 = GridSearchCV(abc, parameters)\n",
    "clf6.fit(x_train, y_train)\n",
    "\n",
    "clf6.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.69      0.77      1892\n",
      "           1       0.85      0.95      0.89      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.86      0.82      0.83      5296\n",
      "weighted avg       0.86      0.86      0.85      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8187858209344558\n",
      "-------Accuracy Score------------------\n",
      "0.8551737160120846\n"
     ]
    }
   ],
   "source": [
    "evaluate(clf6, 'AdaBC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "      <th>R_Forest_0.5</th>\n",
       "      <th>R_Forest_0.63</th>\n",
       "      <th>ExTree</th>\n",
       "      <th>GradientBC</th>\n",
       "      <th>AdaBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC  R_Forest_0.5  R_Forest_0.63  ExTree  \\\n",
       "BAS         0.82          0.82 0.82          0.81           0.81    0.80   \n",
       "AS          0.86          0.84 0.86          0.84           0.82    0.82   \n",
       "\n",
       "     GradientBC  AdaBC  \n",
       "BAS        0.83   0.82  \n",
       "AS         0.86   0.86  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bagging classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=GradientBoostingClassifier(n_estimators=200),\n",
       "                  random_state=25)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc=sen.BaggingClassifier(base_estimator=sen.GradientBoostingClassifier(n_estimators=200, learning_rate=0.1), random_state=25)\n",
    "bc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.69      0.78      1892\n",
      "           1       0.85      0.96      0.90      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.87      0.82      0.84      5296\n",
      "weighted avg       0.87      0.86      0.86      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8238684497531819\n",
      "-------Accuracy Score------------------\n",
      "0.8621601208459214\n"
     ]
    }
   ],
   "source": [
    "evaluate(bc, 'BaggingC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "      <th>R_Forest_0.5</th>\n",
       "      <th>R_Forest_0.63</th>\n",
       "      <th>ExTree</th>\n",
       "      <th>GradientBC</th>\n",
       "      <th>AdaBC</th>\n",
       "      <th>BaggingC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC  R_Forest_0.5  R_Forest_0.63  ExTree  \\\n",
       "BAS         0.82          0.82 0.82          0.81           0.81    0.80   \n",
       "AS          0.86          0.84 0.86          0.84           0.82    0.82   \n",
       "\n",
       "     GradientBC  AdaBC  BaggingC  \n",
       "BAS        0.83   0.82      0.82  \n",
       "AS         0.86   0.86      0.86  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacking classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.66      0.77      1892\n",
      "           1       0.84      0.96      0.90      3404\n",
      "\n",
      "    accuracy                           0.86      5296\n",
      "   macro avg       0.87      0.81      0.83      5296\n",
      "weighted avg       0.86      0.86      0.85      5296\n",
      "\n",
      "--------Balanced Accuracy Score--------\n",
      "0.8125644373116567\n",
      "-------Accuracy Score------------------\n",
      "0.8551737160120846\n"
     ]
    }
   ],
   "source": [
    "# best parameters of estomators are preknown \n",
    "estimators = [ ('gc', sen.AdaBoostClassifier(learning_rate=1, n_estimators=200)) ]\n",
    "sc = sen.StackingClassifier(estimators=estimators, \n",
    "        final_estimator=sen.BaggingClassifier(base_estimator=sen.GradientBoostingClassifier(n_estimators=150), random_state=25))\n",
    "sc.fit(x_train, y_train)\n",
    "\n",
    "evaluate(sc, 'StackingC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Log_reg_0.5</th>\n",
       "      <th>Log_reg_0.63</th>\n",
       "      <th>SVC</th>\n",
       "      <th>R_Forest_0.5</th>\n",
       "      <th>R_Forest_0.63</th>\n",
       "      <th>ExTree</th>\n",
       "      <th>GradientBC</th>\n",
       "      <th>AdaBC</th>\n",
       "      <th>BaggingC</th>\n",
       "      <th>StackingC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log_reg_0.5  Log_reg_0.63  SVC  R_Forest_0.5  R_Forest_0.63  ExTree  \\\n",
       "BAS         0.82          0.82 0.82          0.81           0.81    0.80   \n",
       "AS          0.86          0.84 0.86          0.84           0.82    0.82   \n",
       "\n",
       "     GradientBC  AdaBC  BaggingC  StackingC  \n",
       "BAS        0.83   0.82      0.82       0.81  \n",
       "AS         0.86   0.86      0.86       0.86  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I merged evaluation metrics and scores of all models we trained into **\"performance\"** in order to make the comparison. So, that can be seen some models performed better like **GradientBoost, Bagging, AdaBoost, Logistic regression** models. Particularly, doing of **hyperparameter tuning** boosted performance on closer optimal values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the comparison of models performances, the **GradientBoost** is the optimal model to pick and to utilize as the major"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lorenz curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, threshold=sm.roc_curve(y_test, pd.DataFrame(clf5.predict_proba(x_test))[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,5))\n",
    "plt.plot(fpr, tpr, 'darkorange')\n",
    "plt.plot([0,1],[0,1], 'b--')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1])\n",
    "plt.xlabel('False positives')\n",
    "plt.ylabel('True positives')\n",
    "plt.legend(['lorenz curve'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9154391488188253"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More area under the **Lorenz curve** as well as higher **Gini Index(Coefficient)** indicates better performance at prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (auc-0.5)/0.5=2*auc(fpr,tpr)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8308782976376505"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gini index(coefficient)\n",
    "gini=2*sm.auc(fpr, tpr)-1\n",
    "gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}