credit-risk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Status</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>Home</th>\n",
       "      <th>Time</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital</th>\n",
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       "      <th>Income</th>\n",
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       "      <th>815</th>\n",
       "      <td>1</td>\n",
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       "      <td>48</td>\n",
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       "      <td>400</td>\n",
       "      <td>500</td>\n",
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       "      <th>1212</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>38</td>\n",
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      "text/plain": [
       "      Status  Seniority  Home  Time  Age  Marital  Records  Job  Expenses  \\\n",
       "815        1          3     2    48   48        2        1    3        45   \n",
       "95         1          3     2    36   41        2        1    3        75   \n",
       "3839       1          2     5    60   24        1        1    1        35   \n",
       "3157       1         13     3    60   40        2        1    1        60   \n",
       "2933       1         28     2    24   49        2        1    1        35   \n",
       "1212       2          4     5    60   38        4        1    1        35   \n",
       "3355       1          0     2    60   56        2        1    1        45   \n",
       "3864       1          8     2    12   39        2        1    1        75   \n",
       "895        1          5     1    36   37        2        1    1        74   \n",
       "2711       1          3     2    60   27        2        1    3        45   \n",
       "\n",
       "      Income  Assets  Debt  Amount  Price  \n",
       "815      100    4000     0    1600   2600  \n",
       "95       200    3000     0    1000   1000  \n",
       "3839     164       0     0     900   1516  \n",
       "3157     100    3500   540     970   1270  \n",
       "2933      73    2700     0     400    500  \n",
       "1212     100       0     0    1300   1500  \n",
       "3355     150   10000     0    1000   1798  \n",
       "3864     160    1500     0     360    865  \n",
       "895      138       0     0     900   1358  \n",
       "2711      21    4000     0    1050   1064  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CreditScoring.csv')\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['status', 'seniority', 'home', 'time', 'age', 'marital', 'records',\n",
       "       'job', 'expenses', 'income', 'assets', 'debt', 'amount', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.lower()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although most columns are numerical, some are categorical: status, home, marital, records and job. We need to translate them to their actual names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "status_values = {\n",
    "    1: 'ok',\n",
    "    2: 'default',\n",
    "    0: 'unk'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.status = df.status.map(status_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1316</td>\n",
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       "      <th>1063</th>\n",
       "      <td>ok</td>\n",
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       "      <td>24</td>\n",
       "      <td>46</td>\n",
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       "      <td>800</td>\n",
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       "      <td>35</td>\n",
       "      <td>349</td>\n",
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       "      <td>1300</td>\n",
       "      <td>1511</td>\n",
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       "      <td>ok</td>\n",
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       "      <td>60</td>\n",
       "      <td>40</td>\n",
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       "      <td>24</td>\n",
       "      <td>58</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>150</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>1607</td>\n",
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      ],
      "text/plain": [
       "       status  seniority  home  time  age  marital  records  job  expenses  \\\n",
       "3057       ok         29     2    48   60        2        1    1        35   \n",
       "3578  default         11     6    60   48        2        1    1        75   \n",
       "959   default          1     5    36   33        1        1    1        35   \n",
       "809   default          2     1    48   31        2        1    3       102   \n",
       "3072  default          0     5    60   22        1        1    2        35   \n",
       "1063       ok          3     2    24   46        2        1    3        75   \n",
       "903        ok         13     2    60   40        2        1    1        90   \n",
       "2685  default          1     2    60   40        2        1    3        35   \n",
       "3466       ok          2     2    60   40        2        1    3        75   \n",
       "1973  default          0     2    24   58        2        2    1        45   \n",
       "\n",
       "      income  assets  debt  amount  price  \n",
       "3057     152    3500     0    1050   1550  \n",
       "3578     225       0     0    1300   1550  \n",
       "959      125       0     0     600   1600  \n",
       "809      268       0     0    1400   1635  \n",
       "3072      49       0     0    1250   1316  \n",
       "1063     500   14000     0     500   1095  \n",
       "903      130    3000     0     800    860  \n",
       "2685     349    6000  4000    1300   1511  \n",
       "3466      67   25000     0    1800   2287  \n",
       "1973     150    4000     0     600   1607  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_values = {\n",
    "    1: 'rent',\n",
    "    2: 'owner',\n",
    "    3: 'private',\n",
    "    4: 'ignore',\n",
    "    5: 'parents',\n",
    "    6: 'other',\n",
    "    0: 'unk'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ok</td>\n",
       "      <td>4</td>\n",
       "      <td>parents</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>100</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>1048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>parents</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>owner</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>7500</td>\n",
       "      <td>2600</td>\n",
       "      <td>600</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>private</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>200</td>\n",
       "      <td>3000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>ok</td>\n",
       "      <td>10</td>\n",
       "      <td>other</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  seniority     home  time  age  marital  records  job  expenses  \\\n",
       "57         ok          4  parents    30   27        1        1    1        45   \n",
       "2144  default          1  parents    60   22        1        1    1        35   \n",
       "2807       ok          0    owner    18   26        1        1    4        35   \n",
       "397   default          1  private    48   34        2        2    3        45   \n",
       "2693       ok         10    other    42   26        1        1    1        35   \n",
       "\n",
       "      income  assets  debt  amount  price  \n",
       "57       100    5000     0     500   1048  \n",
       "2144      45       0     0    1350   1520  \n",
       "2807      85    7500  2600     600   1290  \n",
       "397      200    3000  1500    1000   1191  \n",
       "2693      67       0     0     800   1400  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.home = df.home.map(home_values)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "marital_values = {\n",
    "    1: 'single',\n",
    "    2: 'married',\n",
    "    3: 'widow',\n",
    "    4: 'separated',\n",
    "    5: 'divorced',\n",
    "    0: 'unk'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>status</th>\n",
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       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
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       "      <th>job</th>\n",
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       "      <th>debt</th>\n",
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       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>26</td>\n",
       "      <td>married</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3248</th>\n",
       "      <td>ok</td>\n",
       "      <td>1</td>\n",
       "      <td>parents</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>married</td>\n",
       "      <td>1</td>\n",
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       "      <td>140</td>\n",
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       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>parents</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>ok</td>\n",
       "      <td>1</td>\n",
       "      <td>private</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>174</td>\n",
       "      <td>7000</td>\n",
       "      <td>6500</td>\n",
       "      <td>950</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>ok</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1126</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  seniority     home  time  age  marital  records  job  expenses  \\\n",
       "473        ok          0    other    60   26  married        1    2        45   \n",
       "3248       ok          1  parents    60   25  married        1    1        35   \n",
       "833   default          1  parents    36   23   single        1    2        35   \n",
       "4401       ok          1  private    60   24   single        1    1        35   \n",
       "2991       ok          4    other    48   20   single        1    1        35   \n",
       "\n",
       "      income  assets  debt  amount  price  \n",
       "473       68       0     0    1200   1315  \n",
       "3248     140       0     0    2000   2100  \n",
       "833       75       0     0     350    425  \n",
       "4401     174    7000  6500     950   1468  \n",
       "2991     245       0     0    1126   1631  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.marital = df.marital.map(marital_values)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "records_values = {\n",
    "    1: 'no',\n",
    "    2: 'yes',\n",
    "    0: 'unk'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
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       "      <th>records</th>\n",
       "      <th>job</th>\n",
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       "      <th>income</th>\n",
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       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>ok</td>\n",
       "      <td>3</td>\n",
       "      <td>owner</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>125</td>\n",
       "      <td>4000</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>ok</td>\n",
       "      <td>14</td>\n",
       "      <td>owner</td>\n",
       "      <td>60</td>\n",
       "      <td>39</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
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       "      <td>105</td>\n",
       "      <td>129</td>\n",
       "      <td>3500</td>\n",
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       "      <td>750</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>ok</td>\n",
       "      <td>8</td>\n",
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       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>128</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>550</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>ok</td>\n",
       "      <td>15</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>134</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>ok</td>\n",
       "      <td>3</td>\n",
       "      <td>rent</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>99999999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     status  seniority     home  time  age  marital records  job  expenses  \\\n",
       "1934     ok          3    owner    48   25   single      no    1        35   \n",
       "1057     ok         14    owner    60   39  married      no    1       105   \n",
       "281      ok          8  private    36   39  married      no    1        75   \n",
       "894      ok         15     rent    60   37  married      no    1        58   \n",
       "1770     ok          3     rent    48   51  married      no    3        90   \n",
       "\n",
       "        income  assets  debt  amount  price  \n",
       "1934       125    4000     0    1000   1301  \n",
       "1057       129    3500  1200     750   1888  \n",
       "281        128    2500     0     550    550  \n",
       "894        134     500     0     900   1333  \n",
       "1770  99999999       0     0    1200   1240  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.records = df.records.map(records_values)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_values = {\n",
    "    1: 'fixed',\n",
    "    2: 'parttime',\n",
    "    3: 'freelance',\n",
    "    4: 'others',\n",
    "    0: 'unk'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>time</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>default</td>\n",
       "      <td>0</td>\n",
       "      <td>rent</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>89</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709</th>\n",
       "      <td>ok</td>\n",
       "      <td>4</td>\n",
       "      <td>parents</td>\n",
       "      <td>48</td>\n",
       "      <td>21</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>88</td>\n",
       "      <td>25000</td>\n",
       "      <td>0</td>\n",
       "      <td>1100</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1150</td>\n",
       "      <td>1555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>ok</td>\n",
       "      <td>28</td>\n",
       "      <td>owner</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>75</td>\n",
       "      <td>170</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>750</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4283</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>parents</td>\n",
       "      <td>48</td>\n",
       "      <td>23</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "      <td>1202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  seniority     home  time  age  marital records        job  \\\n",
       "1757  default          0     rent    36   32  married      no  freelance   \n",
       "1709       ok          4  parents    48   21   single      no      fixed   \n",
       "137   default          1     rent    60   37   single      no      fixed   \n",
       "3608       ok         28    owner    36   46  married      no      fixed   \n",
       "4283       ok          0  parents    48   23   single      no      fixed   \n",
       "\n",
       "      expenses  income  assets  debt  amount  price  \n",
       "1757        89     285       0     0    1000   1304  \n",
       "1709        35      88   25000     0    1100   1200  \n",
       "137         45     100       0     0    1150   1555  \n",
       "3608        75     170    6000     0     750    800  \n",
       "4283        35      56       0     0     325   1202  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job = df.job.map(job_values)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>seniority</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>763317.0</td>\n",
       "      <td>1060341.0</td>\n",
       "      <td>404382.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>1463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8703625.0</td>\n",
       "      <td>10217569.0</td>\n",
       "      <td>6344253.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seniority    time     age  expenses      income      assets  \\\n",
       "count     4455.0  4455.0  4455.0    4455.0      4455.0      4455.0   \n",
       "mean         8.0    46.0    37.0      56.0    763317.0   1060341.0   \n",
       "std          8.0    15.0    11.0      20.0   8703625.0  10217569.0   \n",
       "min          0.0     6.0    18.0      35.0         0.0         0.0   \n",
       "25%          2.0    36.0    28.0      35.0        80.0         0.0   \n",
       "50%          5.0    48.0    36.0      51.0       120.0      3500.0   \n",
       "75%         12.0    60.0    45.0      72.0       166.0      6000.0   \n",
       "max         48.0    72.0    68.0     180.0  99999999.0  99999999.0   \n",
       "\n",
       "             debt  amount    price  \n",
       "count      4455.0  4455.0   4455.0  \n",
       "mean     404382.0  1039.0   1463.0  \n",
       "std     6344253.0   475.0    628.0  \n",
       "min           0.0   100.0    105.0  \n",
       "25%           0.0   700.0   1118.0  \n",
       "50%           0.0  1000.0   1400.0  \n",
       "75%           0.0  1300.0   1692.0  \n",
       "max    99999999.0  5000.0  11140.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The max value in income assets, etc is 99999999. It seems like an artificial value. Lets replace this big number with NaN for these respective columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in ['income', 'assets', 'debt']:\n",
    "    df[c] = df[c].replace(to_replace=99999999, value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>seniority</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>5403.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>1463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>11573.0</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>959.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seniority    time     age  expenses  income    assets     debt  amount  \\\n",
       "count     4455.0  4455.0  4455.0    4455.0  4421.0    4408.0   4437.0  4455.0   \n",
       "mean         8.0    46.0    37.0      56.0   131.0    5403.0    343.0  1039.0   \n",
       "std          8.0    15.0    11.0      20.0    86.0   11573.0   1246.0   475.0   \n",
       "min          0.0     6.0    18.0      35.0     0.0       0.0      0.0   100.0   \n",
       "25%          2.0    36.0    28.0      35.0    80.0       0.0      0.0   700.0   \n",
       "50%          5.0    48.0    36.0      51.0   120.0    3000.0      0.0  1000.0   \n",
       "75%         12.0    60.0    45.0      72.0   165.0    6000.0      0.0  1300.0   \n",
       "max         48.0    72.0    68.0     180.0   959.0  300000.0  30000.0  5000.0   \n",
       "\n",
       "         price  \n",
       "count   4455.0  \n",
       "mean    1463.0  \n",
       "std      628.0  \n",
       "min      105.0  \n",
       "25%     1118.0  \n",
       "50%     1400.0  \n",
       "75%     1692.0  \n",
       "max    11140.0  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ok         3200\n",
       "default    1254\n",
       "unk           1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there is one row with \"unknown\" status: we don't know whether or not this client managed to pay back the loan. This row seems like it is not useful so let's remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.status != 'unk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ok         3200\n",
       "default    1254\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_full, df_test = train_test_split(df, test_size=0.2, random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\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>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3669</th>\n",
       "      <td>ok</td>\n",
       "      <td>22</td>\n",
       "      <td>owner</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>60</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2540</th>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>41</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>45</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>2101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>ok</td>\n",
       "      <td>2</td>\n",
       "      <td>parents</td>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>162.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>400</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>ok</td>\n",
       "      <td>1</td>\n",
       "      <td>owner</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>others</td>\n",
       "      <td>45</td>\n",
       "      <td>103.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>ok</td>\n",
       "      <td>13</td>\n",
       "      <td>owner</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>253.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1060</td>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>default</td>\n",
       "      <td>4</td>\n",
       "      <td>owner</td>\n",
       "      <td>60</td>\n",
       "      <td>47</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1900</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>ok</td>\n",
       "      <td>2</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>28</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4024</th>\n",
       "      <td>ok</td>\n",
       "      <td>2</td>\n",
       "      <td>parents</td>\n",
       "      <td>36</td>\n",
       "      <td>25</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3776</th>\n",
       "      <td>ok</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>2999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>parents</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>1809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3563 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  seniority     home  time  age  marital records        job  \\\n",
       "3669       ok         22    owner    48   48  married      no      fixed   \n",
       "2540  default          8    other    60   41  married      no  freelance   \n",
       "279        ok          2  parents    36   19  married      no      fixed   \n",
       "3537       ok          1    owner    12   61  married      no     others   \n",
       "3867       ok         13    owner    60   27  married      no      fixed   \n",
       "...       ...        ...      ...   ...  ...      ...     ...        ...   \n",
       "332   default          4    owner    60   47  married      no  freelance   \n",
       "1293       ok          2     rent    60   28   single      no      fixed   \n",
       "4024       ok          2  parents    36   25   single      no      fixed   \n",
       "3776       ok          4    other    60   25   single      no      fixed   \n",
       "1945  default          1  parents    48   25   single      no  freelance   \n",
       "\n",
       "      expenses  income   assets   debt  amount  price  \n",
       "3669        60   110.0   3000.0    0.0    1000   1460  \n",
       "2540        45    62.0      0.0    0.0    1800   2101  \n",
       "279         35   162.0   4000.0  100.0     400    570  \n",
       "3537        45   103.0  20000.0    0.0     300    650  \n",
       "3867        35   253.0   7000.0    0.0    1060   1750  \n",
       "...        ...     ...      ...    ...     ...    ...  \n",
       "332         75     0.0  13500.0    0.0    1900   1976  \n",
       "1293        45   101.0      0.0    0.0    1300   1333  \n",
       "4024        35   110.0      0.0    0.0     500   1200  \n",
       "3776        35   162.0      0.0    0.0    1800   2999  \n",
       "1945        35     0.0      0.0    0.0    1800   1809  \n",
       "\n",
       "[3563 rows x 14 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
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       "  </thead>\n",
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       "      <th>1207</th>\n",
       "      <td>default</td>\n",
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       "      <td>36</td>\n",
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       "      <td>3000.0</td>\n",
       "      <td>2250</td>\n",
       "      <td>2250</td>\n",
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       "    <tr>\n",
       "      <th>3750</th>\n",
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       "      <td>41</td>\n",
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       "      <td>53</td>\n",
       "      <td>181.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1150</td>\n",
       "      <td>2497</td>\n",
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       "    <tr>\n",
       "      <th>4246</th>\n",
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       "      <td>7500.0</td>\n",
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       "      <td>1300</td>\n",
       "      <td>1508</td>\n",
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       "    <tr>\n",
       "      <th>2227</th>\n",
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       "      <td>30</td>\n",
       "      <td>20</td>\n",
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       "      <td>35</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450</td>\n",
       "      <td>500</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>ok</td>\n",
       "      <td>19</td>\n",
       "      <td>owner</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>90</td>\n",
       "      <td>464.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>2413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>ok</td>\n",
       "      <td>7</td>\n",
       "      <td>owner</td>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>owner</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>parttime</td>\n",
       "      <td>45</td>\n",
       "      <td>219.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>other</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>freelance</td>\n",
       "      <td>75</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3049</th>\n",
       "      <td>ok</td>\n",
       "      <td>4</td>\n",
       "      <td>owner</td>\n",
       "      <td>24</td>\n",
       "      <td>49</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>132.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>850</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  seniority   home  time  age  marital records        job  \\\n",
       "1207  default          3  owner    36   26   single      no  freelance   \n",
       "1734       ok         10   rent    60   28  married      no      fixed   \n",
       "3750       ok         14   rent    60   41  married      no      fixed   \n",
       "4246       ok          4  owner    60   39  married      no  freelance   \n",
       "2227  default          1  other    30   20   single      no      fixed   \n",
       "...       ...        ...    ...   ...  ...      ...     ...        ...   \n",
       "975        ok         19  owner    12   42  married      no      fixed   \n",
       "1616       ok          7  owner    36   24  married      no      fixed   \n",
       "1670       ok          0  owner    24   28  married      no   parttime   \n",
       "1742  default          8  other    36   30  married     yes  freelance   \n",
       "3049       ok          4  owner    24   49  married      no      fixed   \n",
       "\n",
       "      expenses  income   assets    debt  amount  price  \n",
       "1207        35     0.0  60000.0  3000.0     800   1000  \n",
       "1734        78   325.0     18.0  3000.0    2250   2250  \n",
       "3750        53   181.0      0.0     0.0    1150   2497  \n",
       "4246        45     0.0   7500.0     0.0    1300   1508  \n",
       "2227        35    90.0      0.0     0.0     450    500  \n",
       "...        ...     ...      ...     ...     ...    ...  \n",
       "975         90   464.0   5000.0     0.0    1100   2413  \n",
       "1616        45    95.0   3000.0     0.0    1000   1597  \n",
       "1670        45   219.0   6000.0     0.0     600    943  \n",
       "1742        75   100.0   4000.0  1365.0    1220   1745  \n",
       "3049        45   132.0  15000.0     0.0     850   1338  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_val = train_test_split(df_train_full, test_size=0.25, random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2672, 891, 891)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = (df_train.status == 'default').values\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False,  True, False,  True, False, False, False,  True, False,\n",
       "       False,  True, False,  True, False, False, False, False, False,\n",
       "       False,  True, False, False, False, False,  True, False,  True,\n",
       "       False, False,  True, False, False, False, False,  True, False,\n",
       "       False, False,  True, False, False,  True, False, False, False,\n",
       "        True, False, False, False, False, False, False,  True, False,\n",
       "        True,  True, False, False,  True, False,  True, False, False,\n",
       "       False, False,  True, False, False, False, False, False,  True,\n",
       "       False,  True, False, False,  True, False, False,  True, False,\n",
       "       False, False,  True,  True, False, False,  True, False, False,\n",
       "       False, False, False, False, False,  True, False, False, False,\n",
       "        True, False,  True, False,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True, False, False,\n",
       "       False, False, False,  True, False,  True,  True, False, False,\n",
       "       False, False, False, False,  True, False, False,  True,  True,\n",
       "       False,  True, False, False, False, False,  True,  True, False,\n",
       "        True, False, False,  True, False, False, False, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True, False, False,\n",
       "        True, False, False, False, False,  True, False,  True, False,\n",
       "       False,  True,  True, False, False,  True, False, False, False,\n",
       "       False,  True,  True,  True, False, False, False, False, False,\n",
       "       False,  True, False, False, False, False,  True, False, False,\n",
       "       False,  True, False, False, False,  True,  True, False, False,\n",
       "        True, False, False, False, False, False,  True, False,  True,\n",
       "        True, False,  True, False, False,  True, False, False, False,\n",
       "        True,  True, False,  True,  True,  True,  True, False, False,\n",
       "        True, False,  True,  True, False, False,  True, False,  True,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False,  True, False, False, False,  True, False, False,\n",
       "        True,  True, False,  True, False, False, False, False, False,\n",
       "       False, False, False, False,  True, False, False,  True, False,\n",
       "       False, False, False, False, False,  True,  True, False, False,\n",
       "        True, False, False, False, False, False,  True, False, False,\n",
       "        True,  True, False, False, False, False,  True,  True, False,\n",
       "       False,  True, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True, False, False])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val = (df_val.status == 'default').values\n",
    "y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_train['status']\n",
    "del df_val['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        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>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>951</th>\n",
       "      <td>10</td>\n",
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       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>married</td>\n",
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       "      <td>freelance</td>\n",
       "      <td>75</td>\n",
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       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1400</td>\n",
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       "    <tr>\n",
       "      <th>688</th>\n",
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       "      <td>32</td>\n",
       "      <td>single</td>\n",
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       "      <td>1100</td>\n",
       "      <td>1330</td>\n",
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       "    <tr>\n",
       "      <th>2233</th>\n",
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       "      <th>3304</th>\n",
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       "      <td>parttime</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1079</td>\n",
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       "      <th>2271</th>\n",
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       "      <td>46</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>18</td>\n",
       "      <td>private</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>220.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>7</td>\n",
       "      <td>private</td>\n",
       "      <td>60</td>\n",
       "      <td>29</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>60</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>1</td>\n",
       "      <td>parents</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
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       "    <tr>\n",
       "      <th>1857</th>\n",
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       "      <td>43</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2500</td>\n",
       "      <td>2976</td>\n",
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       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>12</td>\n",
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       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>fixed</td>\n",
       "      <td>45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>450</td>\n",
       "      <td>1636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2672 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seniority     home  time  age  marital records        job  expenses  \\\n",
       "951          10    owner    36   36  married      no  freelance        75   \n",
       "688           6  parents    48   32   single     yes      fixed        35   \n",
       "2233          1  parents    48   40  married      no      fixed        75   \n",
       "3304          1  parents    48   23   single      no   parttime        35   \n",
       "2271          5    owner    36   46  married      no  freelance        60   \n",
       "...         ...      ...   ...  ...      ...     ...        ...       ...   \n",
       "2382         18  private    36   45  married      no      fixed        45   \n",
       "1784          7  private    60   29  married      no      fixed        60   \n",
       "808           1  parents    24   19   single      no      fixed        35   \n",
       "1857         15    owner    48   43  married      no  freelance        60   \n",
       "488          12    owner    48   27  married     yes      fixed        45   \n",
       "\n",
       "      income   assets    debt  amount  price  \n",
       "951      0.0  10000.0     0.0    1000   1400  \n",
       "688     85.0      0.0     0.0    1100   1330  \n",
       "2233   121.0      0.0     0.0    1320   1600  \n",
       "3304    72.0      0.0     0.0    1078   1079  \n",
       "2271   100.0   4000.0     0.0    1100   1897  \n",
       "...      ...      ...     ...     ...    ...  \n",
       "2382   220.0  20000.0     0.0     800   1600  \n",
       "1784    51.0   3500.0   500.0    1000   1290  \n",
       "808     28.0      0.0     0.0     400    600  \n",
       "1857   100.0  18000.0     0.0    2500   2976  \n",
       "488    110.0   5000.0  1300.0     450   1636  \n",
       "\n",
       "[2672 rows x 13 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from initial analysis, our data contains missing values-\n",
    "# we added these NaNs ourselves. Replace the missing values\n",
    "# with zero\n",
    "df_train = df_train.fillna(0)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "  <tbody>\n",
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       "      <td>1307</td>\n",
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       "    <tr>\n",
       "      <th>2464</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700</td>\n",
       "      <td>982</td>\n",
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       "    <tr>\n",
       "      <th>1663</th>\n",
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       "      <td>6000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>25</td>\n",
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       "      <td>61</td>\n",
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       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>57</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>750</td>\n",
       "      <td>830</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1968</th>\n",
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       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>12</td>\n",
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       "      <td>75</td>\n",
       "      <td>131.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1150</td>\n",
       "      <td>1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>2</td>\n",
       "      <td>owner</td>\n",
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       "      <td>36</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>freelance</td>\n",
       "      <td>60</td>\n",
       "      <td>140.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3900</td>\n",
       "      <td>4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4081</th>\n",
       "      <td>3</td>\n",
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       "      <td>25</td>\n",
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       "      <td>yes</td>\n",
       "      <td>fixed</td>\n",
       "      <td>35</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>15</td>\n",
       "      <td>owner</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>divorced</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1550</td>\n",
       "      <td>1612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seniority     home  time  age    marital records        job  expenses  \\\n",
       "2404          6     rent    36   31  separated      no      fixed        49   \n",
       "908          18     rent    60   38    married      no      fixed        56   \n",
       "2464         17     rent    24   40    married     yes      fixed        58   \n",
       "1663          2    other    48   29    married      no      fixed        75   \n",
       "1518         25     rent    42   61    married      no      fixed        57   \n",
       "...         ...      ...   ...  ...        ...     ...        ...       ...   \n",
       "1968          0    owner    12   21     single      no      fixed        35   \n",
       "4371         12    owner    60   45    married      no      fixed        75   \n",
       "1324          2    owner    60   36    married     yes  freelance        60   \n",
       "4081          3  parents    24   25     single     yes      fixed        35   \n",
       "2935         15    owner    60   32   divorced      no  freelance        35   \n",
       "\n",
       "      income   assets    debt  amount  price  \n",
       "2404   140.0      0.0     0.0     550    999  \n",
       "908    108.0      0.0     0.0    1000   1307  \n",
       "2464   128.0      0.0     0.0     700    982  \n",
       "1663    96.0   6000.0  3000.0    1200   1521  \n",
       "1518   121.0      0.0     0.0     750    830  \n",
       "...      ...      ...     ...     ...    ...  \n",
       "1968   147.0   1500.0     0.0     350    350  \n",
       "4371   131.0   5000.0     0.0    1150   1452  \n",
       "1324   140.0  29000.0     0.0    3900   4812  \n",
       "4081   128.0      0.0     0.0     300   1397  \n",
       "2935     0.0   6000.0  3300.0    1550   1612  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val = df_val.fillna(0)\n",
    "df_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to use categorical variables, we need to encode them. \n",
    "# use DictVectorizer from scikit-learn\n",
    "# DictVectorizer needs a list of dictionaries, so first convert\n",
    "# the dataframe into this format\n",
    "dict_train = df_train.to_dict(orient='records')\n",
    "dict_val = df_val.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "X_train = dv.fit_transform(dict_train)\n",
    "X_val = dv.transform(dict_val)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_train)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 1.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0.,\n",
       "       1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0.,\n",
       "       0., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0.,\n",
       "       0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1.,\n",
       "       0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 1.,\n",
       "       1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
       "       0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
       "       1., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 1., 0., 1., 0.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
       "       0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0.,\n",
       "       0., 0., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we see that the score is 100% - the perfect score.\n",
    "# Let's check the score on validation before jumping to conclusions\n",
    "y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6692529849119663"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model is overfitting as the tree learned the training data so well\n",
    "# that is simply memorized the outcome for each customer. However,\n",
    "# when we applied it to the validation set, the model failed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the depth of tree to make the model less complex\n",
    "dt = DecisionTreeClassifier(max_depth=2)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- records=yes <= 0.50\n",
      "|   |--- job=parttime <= 0.50\n",
      "|   |   |--- class: False\n",
      "|   |--- job=parttime >  0.50\n",
      "|   |   |--- class: True\n",
      "|--- records=yes >  0.50\n",
      "|   |--- seniority <= 6.50\n",
      "|   |   |--- class: True\n",
      "|   |--- seniority >  6.50\n",
      "|   |   |--- class: False\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# to visualize the tree use export_text\n",
    "from sklearn.tree import export_text\n",
    "tree_text = export_text(dt, feature_names=dv.feature_names_)\n",
    "print(tree_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train auc 0.7054989859726213\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_train)[:, 1]\n",
    "auc = roc_auc_score(y_train, y_pred)\n",
    "print('train auc', auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation auc 0.6685264343319367\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "auc = roc_auc_score(y_val, y_pred)\n",
    "print('validation auc', auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 -> 0.606\n",
      "   2 -> 0.669\n",
      "   3 -> 0.739\n",
      "   4 -> 0.761\n",
      "   5 -> 0.766\n",
      "   6 -> 0.746\n",
      "  10 -> 0.675\n",
      "  15 -> 0.650\n",
      "  20 -> 0.644\n",
      "None -> 0.649\n"
     ]
    }
   ],
   "source": [
    "# Parameter tuning for decision tree\n",
    "# we start by tuning max_depth. For that, we iterate over\n",
    "# a few reasonable values and see what works best\n",
    "# The value None means that there is no restriction on depth and\n",
    "# the tree will grow as large as it can.\n",
    "for depth in [1, 2, 3, 4, 5, 6, 10, 15, 20, None]:\n",
    "    dt = DecisionTreeClassifier(max_depth=depth)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%4s -> %.3f' % (depth, auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth: 4\n",
      "1 -> 0.761\n",
      "5 -> 0.761\n",
      "10 -> 0.761\n",
      "15 -> 0.764\n",
      "20 -> 0.761\n",
      "50 -> 0.753\n",
      "100 -> 0.756\n",
      "200 -> 0.747\n",
      "\n",
      "depth: 5\n",
      "1 -> 0.766\n",
      "5 -> 0.768\n",
      "10 -> 0.762\n",
      "15 -> 0.772\n",
      "20 -> 0.774\n",
      "50 -> 0.767\n",
      "100 -> 0.763\n",
      "200 -> 0.759\n",
      "\n",
      "depth: 6\n",
      "1 -> 0.747\n",
      "5 -> 0.762\n",
      "10 -> 0.778\n",
      "15 -> 0.786\n",
      "20 -> 0.774\n",
      "50 -> 0.770\n",
      "100 -> 0.776\n",
      "200 -> 0.768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# we tune min_leaf_size. For this, we iterate over the threee best\n",
    "# parameters of max_depth, and for each, go over different values of\n",
    "# min_leaf_size.\n",
    "for m in [4, 5, 6]:\n",
    "    print('depth: %s' % m)\n",
    "    for s in [1, 5, 10, 15, 20, 50, 100, 200]:\n",
    "        dt = DecisionTreeClassifier(max_depth=m, min_samples_leaf=s)\n",
    "        dt.fit(X_train, y_train)\n",
    "        y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print('%s -> %.3f' % (s, auc))\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After running it, we see that the best AUC is 78.5% with parameters\n",
    "# min_sample_leaf=15 and max_depth=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=6, min_samples_leaf=15)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the best parameters to train the final model\n",
    "dt = DecisionTreeClassifier(max_depth=6, min_samples_leaf=15)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11904762, 0.84285714, 0.20333333, ..., 0.30215827, 0.09486166,\n",
       "       0.12765957])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_train)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train auc 0.8612437327474509\n"
     ]
    }
   ],
   "source": [
    "auc = roc_auc_score(y_train, y_pred)\n",
    "print('train auc', auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation auc 0.7850954203095104\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "auc = roc_auc_score(y_val, y_pred)\n",
    "print('validation auc', auc)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# when training a model, we need to specify the number of trees\n",
    "# we want to have in the ensemble\n",
    "rf = RandomForestClassifier(n_estimators=10)\n",
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0. , 0.1, 0.2, 0.1, 0.3, 0. , 0.8, 0.1, 0. , 0.2, 0.9, 0.3,\n",
       "       0. , 0.2, 0.7, 0.1, 0.6, 0.1, 0.2, 0. , 0.2, 0. , 0.3, 0.7, 0.2,\n",
       "       0.8, 0.1, 0.7, 0.1, 0.8, 0.5, 0.3, 0.5, 0. , 0.1, 0.7, 0.5, 0.5,\n",
       "       0.6, 0.1, 0.9, 0. , 0.7, 0. , 0.1, 0.6, 0. , 0. , 0.1, 0. , 0.1,\n",
       "       0.1, 0.4, 0.6, 0.6, 0. , 0.1, 0.1, 0. , 0.1, 0.3, 0.3, 0.3, 0. ,\n",
       "       0.2, 0.2, 0. , 0. , 0.1, 0.3, 0.1, 0. , 0.1, 0.2, 0.6, 0.2, 0.8,\n",
       "       0.5, 0.1, 0.6, 0.2, 0.5, 0.1, 0.2, 0.6, 0.9, 0.1, 0.2, 0.3, 0.6,\n",
       "       0.1, 0. , 0. , 0.1, 0.1, 0.4, 0.3, 0. , 0.1, 0.2, 0. , 0.2, 0. ,\n",
       "       0.2, 0. , 0. , 0.2, 0.2, 0.2, 0.2, 0. , 0. , 0.4, 0.1, 0.1, 0.7,\n",
       "       0.2, 0.1, 0. , 0. , 0. , 0.6, 0. , 0.3, 0.2, 0.5, 0.4, 0. , 0.2,\n",
       "       0.7, 0. , 1. , 0.4, 0.2, 0.5, 0.2, 0.4, 0. , 0.9, 0.2, 0.5, 0. ,\n",
       "       0.7, 0.7, 1. , 0.8, 0. , 0.7, 0.8, 0.5, 0.6, 0.7, 0.8, 0. , 0. ,\n",
       "       0.1, 0.5, 0.2, 0.2, 0.5, 0.4, 0.2, 0.4, 0.5, 0.5, 0. , 0.2, 0.6,\n",
       "       0.3, 0.1, 0.7, 0.1, 0.4, 0.3, 0.1, 0.3, 0.1, 0.1, 0. , 0.1, 0. ,\n",
       "       0.1, 0.1, 0.5, 0.8, 0.1, 0.2, 0.8, 0.2, 0.2, 0. , 0. , 0.2, 0.9,\n",
       "       0. , 0.1, 0.2, 0.8, 0. , 0.5, 0.1, 0.7, 0.1, 0.5, 0.2, 0.2, 0.1,\n",
       "       0.5, 0.1, 0.1, 0.5, 0.6, 0.1, 0. , 0.5, 0.5, 0. , 0.8, 0. , 0. ,\n",
       "       0. , 0. , 0.2, 0.8, 0.4, 0. , 0.2, 0.2, 0. , 0.5, 0. , 0.4, 0.5,\n",
       "       0.4, 0.1, 0. , 0.2, 0.1, 0.7, 0.1, 0.8, 0. , 0.1, 0. , 0. , 0. ,\n",
       "       0.2, 0. , 0.4, 0. , 0.4, 0.6, 0.1, 0.6, 0.1, 0. , 0.2, 0.1, 0.3,\n",
       "       0. , 0. , 0.3, 0.2, 0.2, 0.1, 0.8, 0.4, 0. , 0. , 0.1, 0. , 0. ,\n",
       "       0. , 0.2, 0.9, 0. , 0. , 0.9, 0. , 0.3, 0. , 0.2, 0.9, 0.2, 0. ,\n",
       "       0. , 0.5, 0.1, 0.1, 0. , 0. , 0. , 0.3, 0.1, 0.2, 0.1, 0. , 0.4,\n",
       "       0. , 0. , 0.1, 0.1, 0. , 0. , 0.3, 0.4, 0.2, 0.7, 0.1, 0. , 0.6,\n",
       "       0.3, 0.3, 0.2, 0. , 0. , 0.7, 0.5, 0.8, 0.1, 0.4, 0. , 0.8, 0.8,\n",
       "       0.5, 0. , 0.1, 0.1, 0.2, 0.1, 0.3, 0.1, 0.1, 0.1, 0.4, 0.4, 0. ,\n",
       "       0.1, 0.4, 0.3, 0.1, 0.4, 0.1, 0.2, 0.1, 0.1, 0.2, 0. , 0.3, 0.2,\n",
       "       0.4, 0.2, 0.1, 0. , 0.7, 0.2, 0.2, 0.1, 0.1, 0.1, 0.7, 0.2, 0. ,\n",
       "       0. , 0.7, 0. , 0. , 0.8, 0.4, 0.1, 0.2, 0.7, 0.6, 0.3, 0. , 0.2,\n",
       "       0.4, 0.3, 0.1, 0.7, 0. , 0.3, 0.9, 0.5, 0.2, 0.5, 0.1, 0.8, 0.8,\n",
       "       0.6, 0.1, 0. , 0.1, 0. , 0.5, 0.5, 0. , 0.5, 0.4, 0.1, 0.1, 0. ,\n",
       "       0.4, 0.1, 0.2, 0.1, 0.5, 0.2, 0.2, 0.2, 0.1, 0. , 0.4, 0.4, 0. ,\n",
       "       0. , 0.2, 0.7, 0.9, 0.5, 0.6, 0.4, 0.2, 0.2, 0.4, 0. , 0.2, 0.7,\n",
       "       0.3, 0.4, 0.1, 0.5, 0.3, 0.2, 0.5, 0.3, 0. , 0.4, 0.1, 0.3, 0.3,\n",
       "       0.4, 0.6, 0.1, 0.4, 0.1, 0.6, 0.3, 0. , 0. , 0.3, 0.1, 0.4, 0. ,\n",
       "       0.2, 0.6, 0.9, 0.1, 0.5, 0.4, 0. , 0.4, 0.6, 0. , 0.2, 0.4, 0. ,\n",
       "       1. , 0. , 0.5, 0.1, 0.1, 0.3, 0.1, 0.3, 0.1, 0.2, 0.2, 0.7, 0.5,\n",
       "       0.1, 0.4, 0.5, 0.3, 0. , 0.5, 0.2, 0.6, 0. , 0.3, 0.6, 0.2, 0. ,\n",
       "       0. , 0.9, 0.2, 0.6, 0.2, 0.1, 0.7, 0. , 0.2, 0.1, 0.3, 0.6, 0. ,\n",
       "       0.1, 0. , 0. , 0.1, 0.1, 0.1, 0.1, 0.5, 0. , 0.1, 0. , 0.7, 0. ,\n",
       "       0.5, 0.9, 0.5, 0. , 0.3, 0.7, 0. , 0.9, 0.2, 0.3, 0.3, 0.6, 0.5,\n",
       "       0.3, 0.2, 0.1, 0.2, 0. , 0.5, 0.3, 0. , 0. , 0. , 0. , 0.9, 0. ,\n",
       "       0.3, 0.4, 0. , 0.1, 0. , 0.4, 0. , 0.1, 0.3, 0.1, 0.2, 0.1, 0.6,\n",
       "       0.1, 0.1, 0.5, 0.1, 0.9, 0. , 0.3, 0.3, 0.1, 0. , 0.5, 0.4, 0. ,\n",
       "       0.1, 0.2, 0. , 0.3, 0.1, 0.4, 0.1, 0.2, 0.2, 0.6, 0.1, 0.9, 0. ,\n",
       "       0.2, 0.7, 0.4, 0.5, 0.5, 0. , 0.2, 0.4, 0. , 0.3, 0.5, 0.5, 0. ,\n",
       "       0.3, 0.8, 0. , 0.4, 0.4, 0.7, 0.1, 0. , 0.1, 0.2, 0.6, 0.2, 0.7,\n",
       "       0.1, 0.1, 0.1, 0. , 0. , 0.9, 0.1, 0.4, 0.7, 0. , 0.7, 0.3, 0.8,\n",
       "       0.1, 0. , 0.2, 0.3, 0.2, 0.5, 0.1, 0.2, 0.6, 0. , 0.6, 0. , 0. ,\n",
       "       0.1, 0. , 0. , 0.1, 0.6, 0.6, 0.2, 0. , 0.6, 0.6, 0. , 0. , 0.2,\n",
       "       0.2, 0. , 0.3, 0.3, 0.4, 0.1, 0.2, 0.4, 0.4, 1. , 0.4, 0.6, 0.1,\n",
       "       0.5, 0.5, 0. , 0. , 0.2, 0.1, 0. , 0.1, 0.2, 0.5, 0.3, 0.1, 0. ,\n",
       "       0.4, 0. , 0.6, 0.3, 0.6, 1. , 0.6, 0.1, 0. , 0.1, 0. , 0.2, 0.3,\n",
       "       0.1, 0. , 0.6, 0.6, 0.2, 0.2, 0. , 0.1, 0.1, 0. , 0.4, 0.6, 0.2,\n",
       "       0.8, 0.2, 0.7, 0.3, 0.4, 0.2, 0.2, 0.2, 0.7, 0.1, 0.1, 0. , 0. ,\n",
       "       0. , 0.3, 0. , 0.3, 0.1, 0.3, 0. , 0.3, 0.1, 0. , 0.1, 0.7, 0.3,\n",
       "       0.2, 0.2, 0.4, 0.2, 0.5, 0. , 0.2, 0.2, 0.8, 0.3, 0. , 0.5, 0.6,\n",
       "       0.1, 0. , 0.3, 0. , 0.5, 0.6, 0. , 0.5, 0.5, 0.3, 0. , 0. , 0.2,\n",
       "       0. , 0.2, 0.9, 0.1, 0.4, 0.3, 0. , 0.2, 0.2, 0. , 0.1, 0.7, 0.7,\n",
       "       0.1, 0.9, 0. , 0.6, 0.4, 0. , 0.1, 0.4, 0.1, 0.1, 0.2, 0.3, 0.1,\n",
       "       0.5, 0.9, 0.2, 0.9, 0. , 1. , 0.3, 0.4, 0.6, 0.1, 0.1, 0. , 0.5,\n",
       "       0.6, 0.3, 0. , 0.3, 0.7, 0.4, 0.2, 0. , 0.5, 0. , 0.9, 0.2, 0.1,\n",
       "       0. , 0.5, 0.2, 0.3, 0.1, 0.1, 0.3, 0. , 0. , 0. , 0.2, 0.7, 0.3,\n",
       "       0.2, 0. , 0.9, 0.6, 0. , 0.4, 0. , 0.2, 0.2, 0.5, 0.4, 0.1, 0.7,\n",
       "       0. , 0.6, 0. , 0.8, 0.3, 0.3, 0.3, 0.2, 0.3, 0.7, 0.5, 0.1, 0.8,\n",
       "       0.7, 0.3, 0.5, 0.2, 0.3, 0.2, 0.4, 0.3, 0.1, 0.1, 0.5, 0.1, 0. ,\n",
       "       0.1, 0.3, 0.2, 0.7, 0.4, 0.1, 0.4, 0.6, 0. , 0.1, 0.2, 0.1, 0.2,\n",
       "       0.2, 0.2, 0.2, 0.3, 0.3, 0.2, 0.1, 0. , 0.5, 0.3, 0.2, 0.4, 0.5,\n",
       "       0.4, 0.1, 0.3, 0. , 0.5, 0.3, 0.5])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7846322443147417"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every time we retrain the model, the score changes: it varries from 775 to 80%. The reason for this is randomization: to train a tree, we randomly select a subset of features. To make the results consistent, we need to fix the seed for the random-number generator by assigning some value to the random_state parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=3)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10, random_state=3)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7811145285897654"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "aucs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 -> 0.7811145285897654\n",
      "20 -> 0.80073744883873\n",
      "30 -> 0.8134339202247464\n",
      "40 -> 0.8154198251434936\n",
      "50 -> 0.8159617107844324\n",
      "60 -> 0.8168426533627182\n",
      "70 -> 0.8184895013441186\n",
      "80 -> 0.8178628514688431\n",
      "90 -> 0.8198275653290064\n",
      "100 -> 0.819043496161391\n",
      "110 -> 0.8192856796880676\n",
      "120 -> 0.8211353563730595\n",
      "130 -> 0.8221283088324333\n",
      "140 -> 0.8218770434235063\n",
      "150 -> 0.8204663243806156\n",
      "160 -> 0.8198941657988423\n",
      "170 -> 0.8193310890993194\n",
      "180 -> 0.8195460269792448\n",
      "190 -> 0.8201726768545202\n",
      "200 -> 0.8202241408539391\n"
     ]
    }
   ],
   "source": [
    "for i in range(10, 201, 10):\n",
    "    rf = RandomForestClassifier(n_estimators=i, random_state=3)\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print(f'{i} -> {auc}')\n",
    "    aucs.append(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'AUCs')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(10, 201, 10), aucs)\n",
    "plt.title('Number of trees vs AUC')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUCs')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter tuning for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_aucs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth: 5\n",
      "10 -> 0.7886827637984064\n",
      "20 -> 0.7939381463272868\n",
      "30 -> 0.802123949528953\n",
      "40 -> 0.8023025598798769\n",
      "50 -> 0.8061653871303673\n",
      "60 -> 0.8051784892591606\n",
      "70 -> 0.8068253372405608\n",
      "80 -> 0.806189605483035\n",
      "90 -> 0.8065044440677145\n",
      "100 -> 0.8052450897289966\n",
      "110 -> 0.8051603254946599\n",
      "120 -> 0.8070614661790706\n",
      "130 -> 0.8067103000653896\n",
      "140 -> 0.8060140224261946\n",
      "150 -> 0.8060624591315299\n",
      "160 -> 0.8072612675885786\n",
      "170 -> 0.8069282652393983\n",
      "180 -> 0.8071583395897411\n",
      "190 -> 0.8071038482962389\n",
      "200 -> 0.8069101014748976\n",
      "\n",
      "depth: 10\n",
      "10 -> 0.7963811726526361\n",
      "20 -> 0.8174814124143275\n",
      "30 -> 0.8181413625245211\n",
      "40 -> 0.8217801700128358\n",
      "50 -> 0.821211038725146\n",
      "60 -> 0.8225430481218667\n",
      "70 -> 0.8230274151752199\n",
      "80 -> 0.8207629992007942\n",
      "90 -> 0.8212413116659805\n",
      "100 -> 0.8215137681334915\n",
      "110 -> 0.8222584824780219\n",
      "120 -> 0.8234209634060691\n",
      "130 -> 0.8225127751810322\n",
      "140 -> 0.8227610132958756\n",
      "150 -> 0.8214653314281563\n",
      "160 -> 0.8213563488411517\n",
      "170 -> 0.8215258773098254\n",
      "180 -> 0.8224159017703616\n",
      "190 -> 0.8231787798793926\n",
      "200 -> 0.8236873652854133\n",
      "\n",
      "depth: 20\n",
      "10 -> 0.7903810757792256\n",
      "20 -> 0.7997989876728585\n",
      "30 -> 0.8142149620982781\n",
      "40 -> 0.8189314862803032\n",
      "50 -> 0.8187044392240439\n",
      "60 -> 0.8197004189775011\n",
      "70 -> 0.8199940665035963\n",
      "80 -> 0.8171665738296481\n",
      "90 -> 0.8177054321765034\n",
      "100 -> 0.8180111888789325\n",
      "110 -> 0.8184380373446998\n",
      "120 -> 0.8201212128551016\n",
      "130 -> 0.8199789300331791\n",
      "140 -> 0.8193583347460706\n",
      "150 -> 0.8195218086265773\n",
      "160 -> 0.8194582354508246\n",
      "170 -> 0.81901625051464\n",
      "180 -> 0.8191797243951466\n",
      "190 -> 0.8202392773243564\n",
      "200 -> 0.8197972923881718\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for depth in [5, 10, 20]:\n",
    "    print(f'depth: {depth}')\n",
    "    aucs = []\n",
    "    for i in range(10, 201, 10):\n",
    "        rf = RandomForestClassifier(n_estimators=i, random_state=3, max_depth=depth)\n",
    "        rf.fit(X_train, y_train)\n",
    "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print(f'{i} -> {auc}')\n",
    "        aucs.append(auc)\n",
    "    all_aucs[depth] = aucs\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1140c8e0f40>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trees = list(range(10, 201, 10))\n",
    "plt.plot(num_trees, all_aucs[5], label='depth=5')\n",
    "plt.plot(num_trees, all_aucs[10], label='depth=10')\n",
    "plt.plot(num_trees, all_aucs[20], label='depth=20')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples_leaf: 3\n",
      "10 -> 0.8115963527160881\n",
      "20 -> 0.8226823036497057\n",
      "30 -> 0.8294028965149789\n",
      "40 -> 0.8286581821704487\n",
      "50 -> 0.8278165944152479\n",
      "60 -> 0.8253887045603157\n",
      "70 -> 0.8245592259814487\n",
      "80 -> 0.8239598217529243\n",
      "90 -> 0.8231242885858903\n",
      "100 -> 0.8230637427042212\n",
      "110 -> 0.8232272165847279\n",
      "120 -> 0.8231848344675594\n",
      "130 -> 0.8237297474025818\n",
      "140 -> 0.8223614104768594\n",
      "150 -> 0.8225006660046984\n",
      "160 -> 0.822736794943208\n",
      "170 -> 0.8229123780000484\n",
      "180 -> 0.8230879610568889\n",
      "190 -> 0.8232151074083941\n",
      "200 -> 0.8231908890557265\n",
      "\n",
      "min_samples_leaf: 5\n",
      "10 -> 0.8121775931801118\n",
      "20 -> 0.8196005182727472\n",
      "30 -> 0.8212836937831489\n",
      "40 -> 0.8235420551694074\n",
      "50 -> 0.8242686057494369\n",
      "60 -> 0.8248801191542953\n",
      "70 -> 0.8266722772517013\n",
      "80 -> 0.825715652321329\n",
      "90 -> 0.8255824513816571\n",
      "100 -> 0.8249406650359642\n",
      "110 -> 0.8255582330289895\n",
      "120 -> 0.8252191760916423\n",
      "130 -> 0.825170739386307\n",
      "140 -> 0.8253644862076481\n",
      "150 -> 0.8257883073793322\n",
      "160 -> 0.8260728730231769\n",
      "170 -> 0.8260789276113439\n",
      "180 -> 0.8258125257319997\n",
      "190 -> 0.8254916325591534\n",
      "200 -> 0.8253160495023129\n",
      "\n",
      "min_samples_leaf: 10\n",
      "10 -> 0.8081997287544502\n",
      "20 -> 0.8172695018284857\n",
      "30 -> 0.820853818023298\n",
      "40 -> 0.8217317333075005\n",
      "50 -> 0.8227489041195417\n",
      "60 -> 0.8215622048388269\n",
      "70 -> 0.8206237436729554\n",
      "80 -> 0.8193401709815699\n",
      "90 -> 0.8201514857959361\n",
      "100 -> 0.8195520815674119\n",
      "110 -> 0.8192856796880676\n",
      "120 -> 0.8208295996706304\n",
      "130 -> 0.8205692523794531\n",
      "140 -> 0.8210112373156377\n",
      "150 -> 0.8210536194328062\n",
      "160 -> 0.8220344627158461\n",
      "170 -> 0.8220223535395124\n",
      "180 -> 0.8222584824780219\n",
      "190 -> 0.8222282095371873\n",
      "200 -> 0.8219496984815093\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# with max_depth=10, AUC goes over 82%. Let us tune\n",
    "# min_samples_leaf with max_depth=10 from the previous step\n",
    "for m in [3, 5, 10]:\n",
    "    print(f'min_samples_leaf: {m}')\n",
    "    aucs = []\n",
    "    for i in range(10, 201, 10):\n",
    "        rf = RandomForestClassifier(n_estimators=i, random_state=3, max_depth=10, min_samples_leaf=m)\n",
    "        rf.fit(X_train, y_train)\n",
    "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print(f'{i} -> {auc}')\n",
    "        aucs.append(auc)\n",
    "    all_aucs[m] = aucs\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1140cba8cd0>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trees = list(range(10, 201, 10))\n",
    "plt.plot(num_trees, all_aucs[3], label='min_samples_leaf=5')\n",
    "plt.plot(num_trees, all_aucs[5], label='min_samples_leaf=10')\n",
    "plt.plot(num_trees, all_aucs[10], label='min_samples_leaf=20')\n",
    "plt.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best parameter for random forest for this problem seems to be max_depth=10 and min_samples_leaf=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=20, min_samples_leaf=5, n_estimators=200,\n",
       "                       random_state=3)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we achieved the best AUC with 200 trees, so we should set \n",
    "# the n_estimators=200\n",
    "rf = RandomForestClassifier(n_estimators=200, max_depth=20, min_samples_leaf=5, random_state=3)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.136891  , 0.03995756, 0.30238692, 0.23866987, 0.0871555 ,\n",
       "       0.15250214, 0.05320954, 0.59976554, 0.23859649, 0.00770171,\n",
       "       0.25079375, 0.50535735, 0.37357245, 0.10865367, 0.30835667,\n",
       "       0.61211554, 0.10384479, 0.48509027, 0.05541057, 0.26727731,\n",
       "       0.03916895, 0.20849015, 0.02291182, 0.39159335, 0.66723522,\n",
       "       0.32942867, 0.6586161 , 0.20930813, 0.70336283, 0.06314629,\n",
       "       0.8063084 , 0.31931477, 0.26940682, 0.34356454, 0.01663868,\n",
       "       0.03672811, 0.90591794, 0.50607504, 0.57070735, 0.54464715,\n",
       "       0.10497018, 0.82534281, 0.02925657, 0.42624265, 0.04031713,\n",
       "       0.03255525, 0.61608359, 0.03476785, 0.12362528, 0.08737759,\n",
       "       0.01205111, 0.26580287, 0.05202115, 0.36616609, 0.45086708,\n",
       "       0.15827594, 0.0084116 , 0.22890862, 0.06276063, 0.04364892,\n",
       "       0.03742583, 0.12683714, 0.30922175, 0.3200795 , 0.11878439,\n",
       "       0.31054311, 0.32530044, 0.06700599, 0.08576614, 0.10723211,\n",
       "       0.11002141, 0.07199223, 0.02627508, 0.06556981, 0.24181917,\n",
       "       0.56810054, 0.1158868 , 0.42528313, 0.35115343, 0.13282526,\n",
       "       0.63117713, 0.18935878, 0.42599579, 0.48742359, 0.0974839 ,\n",
       "       0.32913351, 0.93027688, 0.45067903, 0.50739876, 0.27415818,\n",
       "       0.54679043, 0.08665454, 0.07271721, 0.07708446, 0.25089505,\n",
       "       0.07857327, 0.4527656 , 0.21295835, 0.04677843, 0.07570833,\n",
       "       0.2356133 , 0.0458854 , 0.17855587, 0.0806415 , 0.15825274,\n",
       "       0.05231304, 0.01095988, 0.29423545, 0.21681797, 0.35254977,\n",
       "       0.20687987, 0.02236875, 0.10124489, 0.57407669, 0.17157807,\n",
       "       0.10565091, 0.39361801, 0.15011255, 0.07212652, 0.06486483,\n",
       "       0.0246212 , 0.0799545 , 0.58936264, 0.06825799, 0.25362871,\n",
       "       0.08172003, 0.4099724 , 0.27025687, 0.09536899, 0.25125446,\n",
       "       0.73737081, 0.01258244, 0.80186337, 0.17949687, 0.22140476,\n",
       "       0.29021517, 0.30141661, 0.48136772, 0.06604991, 0.60844293,\n",
       "       0.25860332, 0.27661671, 0.08783884, 0.70459416, 0.65169194,\n",
       "       0.92734602, 0.47682298, 0.05999343, 0.57584715, 0.62362911,\n",
       "       0.64104356, 0.34781967, 0.45340676, 0.4199622 , 0.02646998,\n",
       "       0.09694876, 0.30752679, 0.07788723, 0.06685695, 0.31000282,\n",
       "       0.67019319, 0.27474301, 0.22612177, 0.63306722, 0.51153249,\n",
       "       0.38919408, 0.04375681, 0.44026053, 0.71365471, 0.38056221,\n",
       "       0.02979698, 0.66694409, 0.10279015, 0.19091847, 0.27640464,\n",
       "       0.03896539, 0.32277914, 0.09415873, 0.22068326, 0.18849104,\n",
       "       0.16242226, 0.12762333, 0.08119898, 0.08855522, 0.48299507,\n",
       "       0.70882561, 0.0564076 , 0.23011061, 0.56384186, 0.03691738,\n",
       "       0.41142041, 0.10661027, 0.03010503, 0.15751754, 0.80739083,\n",
       "       0.02690873, 0.0806637 , 0.23008226, 0.54444986, 0.02028065,\n",
       "       0.39104601, 0.14631364, 0.70995361, 0.26002299, 0.39365087,\n",
       "       0.05767699, 0.08105897, 0.21925309, 0.313075  , 0.17714391,\n",
       "       0.05356573, 0.43781368, 0.73650985, 0.30527119, 0.04151683,\n",
       "       0.38811391, 0.33541444, 0.07387014, 0.76490534, 0.18338487,\n",
       "       0.3007933 , 0.04114982, 0.04624984, 0.22847312, 0.71401108,\n",
       "       0.37709708, 0.0199397 , 0.22240581, 0.05249095, 0.14544355,\n",
       "       0.3845001 , 0.02491539, 0.10946771, 0.36765206, 0.2089586 ,\n",
       "       0.05249263, 0.01767799, 0.05546732, 0.0530834 , 0.7081324 ,\n",
       "       0.06185695, 0.77179372, 0.01585575, 0.11907351, 0.05837038,\n",
       "       0.08835929, 0.04723455, 0.1678178 , 0.09560831, 0.41598995,\n",
       "       0.08791294, 0.59261167, 0.38963003, 0.37804349, 0.41217381,\n",
       "       0.06306027, 0.11762201, 0.26727731, 0.07350941, 0.32543605,\n",
       "       0.13563912, 0.05607125, 0.13168235, 0.28234806, 0.34352551,\n",
       "       0.04632849, 0.56640914, 0.3848123 , 0.00776196, 0.04329421,\n",
       "       0.04490256, 0.05769603, 0.12689626, 0.0076189 , 0.20251162,\n",
       "       0.76492599, 0.16170606, 0.05095622, 0.83149314, 0.03808699,\n",
       "       0.28984141, 0.1363452 , 0.16724414, 0.47641705, 0.22966708,\n",
       "       0.01340946, 0.01625298, 0.13827465, 0.01432553, 0.1381271 ,\n",
       "       0.04116816, 0.02319286, 0.07173388, 0.24654427, 0.12737189,\n",
       "       0.23113731, 0.12205616, 0.01341901, 0.30187804, 0.04526377,\n",
       "       0.22818264, 0.04627675, 0.1850061 , 0.1776097 , 0.05289951,\n",
       "       0.1228923 , 0.49079752, 0.08444772, 0.64485172, 0.14395205,\n",
       "       0.08578208, 0.66124044, 0.2332256 , 0.31719397, 0.14276107,\n",
       "       0.13884184, 0.04704839, 0.40969751, 0.4897151 , 0.76957901,\n",
       "       0.25352195, 0.31857531, 0.04495028, 0.73170711, 0.61899575,\n",
       "       0.58477983, 0.00743506, 0.04134911, 0.0658863 , 0.36594284,\n",
       "       0.27064378, 0.29534887, 0.27469499, 0.05351063, 0.07428054,\n",
       "       0.37911364, 0.53888584, 0.06201457, 0.16051553, 0.26684915,\n",
       "       0.38743057, 0.08030041, 0.62667583, 0.12601917, 0.12580505,\n",
       "       0.16832065, 0.23219594, 0.31413628, 0.05849036, 0.14477802,\n",
       "       0.0755236 , 0.34062471, 0.11309176, 0.19250168, 0.06772899,\n",
       "       0.642824  , 0.19681874, 0.07834688, 0.06995308, 0.20107726,\n",
       "       0.06383606, 0.7332517 , 0.25525289, 0.02664959, 0.02712816,\n",
       "       0.56094008, 0.14875757, 0.25936892, 0.62989214, 0.71313156,\n",
       "       0.41969874, 0.19541557, 0.75614204, 0.38288845, 0.30721604,\n",
       "       0.01071952, 0.4032954 , 0.38516634, 0.25855476, 0.21290519,\n",
       "       0.5246537 , 0.00685228, 0.42102571, 0.85522227, 0.4249503 ,\n",
       "       0.11578524, 0.2413674 , 0.03477459, 0.69835085, 0.82021903,\n",
       "       0.52418068, 0.19382243, 0.06127246, 0.01813591, 0.09696241,\n",
       "       0.63948516, 0.44612922, 0.0538695 , 0.47054477, 0.30081748,\n",
       "       0.20158047, 0.17527364, 0.2437095 , 0.36837437, 0.04133306,\n",
       "       0.09245612, 0.09368448, 0.47339296, 0.20037254, 0.15956359,\n",
       "       0.22183638, 0.06094044, 0.04338141, 0.38869807, 0.34047858,\n",
       "       0.216693  , 0.20698576, 0.11455867, 0.52138675, 0.87147509,\n",
       "       0.30899794, 0.67901577, 0.24057876, 0.37536759, 0.29486354,\n",
       "       0.55609755, 0.05100069, 0.42160762, 0.50413671, 0.28165126,\n",
       "       0.35739323, 0.0662952 , 0.40646226, 0.19929846, 0.19432594,\n",
       "       0.67262799, 0.17933042, 0.04978528, 0.31857136, 0.01188331,\n",
       "       0.24364133, 0.17600136, 0.36253477, 0.40505365, 0.08818774,\n",
       "       0.31346771, 0.05526991, 0.58014831, 0.31779682, 0.07922999,\n",
       "       0.12093876, 0.19920623, 0.24478431, 0.19844393, 0.04341382,\n",
       "       0.13890745, 0.63913998, 0.61788954, 0.03751824, 0.37403523,\n",
       "       0.37129623, 0.03914734, 0.47449005, 0.36775497, 0.1454606 ,\n",
       "       0.10858545, 0.36102793, 0.1157461 , 0.7527674 , 0.04719953,\n",
       "       0.38812303, 0.05849211, 0.14898977, 0.05396177, 0.11635156,\n",
       "       0.11655564, 0.30049587, 0.12878495, 0.1883128 , 0.33376692,\n",
       "       0.24923655, 0.15096561, 0.52250486, 0.49872852, 0.35126911,\n",
       "       0.048236  , 0.2720442 , 0.19891098, 0.52519246, 0.01235046,\n",
       "       0.51242652, 0.44331779, 0.11611059, 0.02405612, 0.24622913,\n",
       "       0.68342725, 0.27079245, 0.53661019, 0.29394635, 0.1438059 ,\n",
       "       0.4836439 , 0.01403529, 0.2315063 , 0.0681085 , 0.24050986,\n",
       "       0.46371692, 0.04501667, 0.25459638, 0.3621272 , 0.15601714,\n",
       "       0.0352098 , 0.04048408, 0.11457621, 0.15898756, 0.33503314,\n",
       "       0.05244046, 0.1173771 , 0.02240488, 0.64772214, 0.16579518,\n",
       "       0.82323153, 0.85051983, 0.44633988, 0.09732476, 0.54781729,\n",
       "       0.54947834, 0.03437039, 0.56562153, 0.13205949, 0.1051058 ,\n",
       "       0.43650817, 0.46999441, 0.46986046, 0.43360864, 0.11634272,\n",
       "       0.16052533, 0.04524244, 0.03748757, 0.38460643, 0.34847549,\n",
       "       0.23529491, 0.13251408, 0.00857848, 0.12036477, 0.84866306,\n",
       "       0.13306266, 0.45924667, 0.55176871, 0.28879643, 0.06210225,\n",
       "       0.06191202, 0.34019366, 0.05020073, 0.04256635, 0.21137005,\n",
       "       0.17385704, 0.19602682, 0.03379733, 0.47613887, 0.17138174,\n",
       "       0.05281893, 0.63535187, 0.19687957, 0.48926631, 0.02106072,\n",
       "       0.61442214, 0.3818574 , 0.07398866, 0.1683296 , 0.41043536,\n",
       "       0.19947475, 0.03038754, 0.1859777 , 0.32799307, 0.03540517,\n",
       "       0.33927752, 0.17402697, 0.31132276, 0.13435629, 0.197938  ,\n",
       "       0.25532065, 0.23202106, 0.06167976, 0.75598049, 0.28533795,\n",
       "       0.1619106 , 0.68507141, 0.32696485, 0.57424806, 0.53364594,\n",
       "       0.01622571, 0.13758374, 0.4261293 , 0.03293825, 0.23999327,\n",
       "       0.36006258, 0.53676114, 0.1357578 , 0.14522138, 0.46758857,\n",
       "       0.04352427, 0.1646148 , 0.35202912, 0.46990351, 0.27451523,\n",
       "       0.37524189, 0.10502212, 0.37046325, 0.59054259, 0.30163347,\n",
       "       0.76685997, 0.09075653, 0.11472289, 0.09894588, 0.17163386,\n",
       "       0.04042056, 0.82816375, 0.14628451, 0.54827621, 0.62193313,\n",
       "       0.00687566, 0.31237581, 0.14636813, 0.85396004, 0.34967424,\n",
       "       0.01195845, 0.41828436, 0.2808308 , 0.20377817, 0.53902332,\n",
       "       0.08277069, 0.43849601, 0.53439363, 0.02120558, 0.64791518,\n",
       "       0.03133918, 0.09284398, 0.20317514, 0.16627062, 0.04751065,\n",
       "       0.26580064, 0.60655127, 0.76925787, 0.16214564, 0.07221759,\n",
       "       0.42587375, 0.46092321, 0.04699169, 0.05827457, 0.20478999,\n",
       "       0.11153814, 0.16007569, 0.18388337, 0.50904101, 0.2239103 ,\n",
       "       0.32403681, 0.31217519, 0.16628078, 0.19989721, 0.84153864,\n",
       "       0.41234512, 0.63951966, 0.41469077, 0.46380837, 0.56260645,\n",
       "       0.00784575, 0.05110105, 0.20319356, 0.0523338 , 0.02332251,\n",
       "       0.04139146, 0.12314892, 0.44441404, 0.2054531 , 0.09621875,\n",
       "       0.02793374, 0.29297977, 0.14375454, 0.41393257, 0.22526848,\n",
       "       0.56639906, 0.88668502, 0.41797407, 0.07585666, 0.04612824,\n",
       "       0.12028141, 0.0143011 , 0.47418757, 0.17441814, 0.10049106,\n",
       "       0.00304505, 0.68020493, 0.60318752, 0.18908776, 0.16290127,\n",
       "       0.14196805, 0.07190439, 0.10778975, 0.02033625, 0.34517684,\n",
       "       0.62137973, 0.16238878, 0.90729683, 0.16013161, 0.45711443,\n",
       "       0.38597181, 0.49330641, 0.10472277, 0.15987935, 0.28523654,\n",
       "       0.41646181, 0.15058404, 0.08362053, 0.09549459, 0.12725977,\n",
       "       0.07874955, 0.48539049, 0.06966612, 0.3119551 , 0.21351505,\n",
       "       0.2810121 , 0.0391831 , 0.25840735, 0.10695621, 0.02703647,\n",
       "       0.10190619, 0.79499597, 0.18216436, 0.33769021, 0.36244909,\n",
       "       0.4844732 , 0.04782615, 0.3904524 , 0.00264895, 0.24262064,\n",
       "       0.23069997, 0.55937031, 0.27462283, 0.14112002, 0.29232181,\n",
       "       0.78322377, 0.12876544, 0.0718064 , 0.34961972, 0.09307744,\n",
       "       0.40468987, 0.36000115, 0.07768517, 0.43651358, 0.76450958,\n",
       "       0.10853962, 0.01828866, 0.00924252, 0.15842812, 0.03377663,\n",
       "       0.24097715, 0.7423025 , 0.20466257, 0.25775117, 0.37031607,\n",
       "       0.06338506, 0.12777454, 0.25915972, 0.06636394, 0.35271036,\n",
       "       0.67474824, 0.70467597, 0.06612607, 0.82861513, 0.04030784,\n",
       "       0.43280297, 0.36482897, 0.03796288, 0.29428732, 0.33769226,\n",
       "       0.13173191, 0.02562797, 0.3284    , 0.16299995, 0.21861564,\n",
       "       0.5477448 , 0.66387938, 0.1714727 , 0.49538215, 0.08854088,\n",
       "       0.81796636, 0.32674252, 0.36872886, 0.4691158 , 0.14392669,\n",
       "       0.07691887, 0.07673483, 0.48463179, 0.28031876, 0.11285671,\n",
       "       0.10139163, 0.23963737, 0.55064678, 0.38773296, 0.26708401,\n",
       "       0.01084456, 0.51583615, 0.13180556, 0.54771715, 0.34198828,\n",
       "       0.18260276, 0.19991223, 0.47832587, 0.20262386, 0.33538365,\n",
       "       0.17348928, 0.11586515, 0.23002195, 0.07221024, 0.04033411,\n",
       "       0.01495556, 0.05916385, 0.64566079, 0.10425891, 0.26148467,\n",
       "       0.03991025, 0.85059664, 0.45283573, 0.10747474, 0.31119757,\n",
       "       0.28114776, 0.13229742, 0.25621256, 0.24856093, 0.48013788,\n",
       "       0.0972679 , 0.62456452, 0.10744758, 0.55810444, 0.04089025,\n",
       "       0.36568471, 0.2374357 , 0.1507199 , 0.47687094, 0.12813101,\n",
       "       0.59903858, 0.65323734, 0.40041139, 0.16446246, 0.60130617,\n",
       "       0.56489454, 0.39186967, 0.3172551 , 0.17423669, 0.17017174,\n",
       "       0.17285672, 0.31072307, 0.504401  , 0.04740751, 0.29719999,\n",
       "       0.35433331, 0.09415502, 0.03457695, 0.09520741, 0.47363798,\n",
       "       0.10771209, 0.76538452, 0.23757479, 0.04498435, 0.55557111,\n",
       "       0.29757119, 0.08003201, 0.01960207, 0.12704949, 0.06324353,\n",
       "       0.3080425 , 0.16899859, 0.08548323, 0.08827174, 0.17995068,\n",
       "       0.21413661, 0.06572046, 0.16664802, 0.02251534, 0.53845179,\n",
       "       0.13568312, 0.14002829, 0.38787377, 0.56711008, 0.40785729,\n",
       "       0.11077125, 0.22328307, 0.03525166, 0.54140737, 0.34136318,\n",
       "       0.31124838])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8247166452737884"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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