BankLoans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bank Loans test  \n",
    "Note: this is not a complete notebook and will be removed. It is just a demo to help some trainees with categorical data. The data itself is irrelevant, you can use it for any dataset with categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_balance</th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings_balance</th>\n",
       "      <th>employment_length</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>personal_status</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>residence_history</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>default</th>\n",
       "      <th>dependents</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>1169</td>\n",
       "      <td>unknown</td>\n",
       "      <td>&gt; 7 yrs</td>\n",
       "      <td>4</td>\n",
       "      <td>single male</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>skilled employee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>repaid</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>5951</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>none</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>skilled employee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>critical</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>single male</td>\n",
       "      <td>none</td>\n",
       "      <td>3</td>\n",
       "      <td>real estate</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>unskilled resident</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  checking_balance  months_loan_duration credit_history    purpose  amount  \\\n",
       "0           < 0 DM                     6       critical   radio/tv    1169   \n",
       "1       1 - 200 DM                    48         repaid   radio/tv    5951   \n",
       "2          unknown                    12       critical  education    2096   \n",
       "\n",
       "  savings_balance employment_length  installment_rate personal_status  \\\n",
       "0         unknown           > 7 yrs                 4     single male   \n",
       "1        < 100 DM         1 - 4 yrs                 2          female   \n",
       "2        < 100 DM         4 - 7 yrs                 2     single male   \n",
       "\n",
       "  other_debtors  residence_history     property  age installment_plan housing  \\\n",
       "0          none                  4  real estate   67             none     own   \n",
       "1          none                  2  real estate   22             none     own   \n",
       "2          none                  3  real estate   49             none     own   \n",
       "\n",
       "   existing_credits  default  dependents telephone foreign_worker  \\\n",
       "0                 2        1           1       yes            yes   \n",
       "1                 1        2           1      none            yes   \n",
       "2                 1        1           2      none            yes   \n",
       "\n",
       "                  job  \n",
       "0    skilled employee  \n",
       "1    skilled employee  \n",
       "2  unskilled resident  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans = pd.read_csv('loans.csv', header=0, sep = ',')\n",
    "pd.options.display.max_columns = 100 # display all columns\n",
    "loans.head(3)\n",
    "#loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking_balance: ['< 0 DM' '1 - 200 DM' 'unknown' '> 200 DM']\n",
      "savings_balance: ['unknown' '< 100 DM' '501 - 1000 DM' '> 1000 DM' '101 - 500 DM']\n",
      "months_loan_duration: [ 6 48 12 42 24 36 30 15  9 10  7 60 18 45 11 27  8 54 20 14 33 21 16  4 47\n",
      " 13 22 39 28  5 26 72 40]\n",
      "credit_history: ['critical' 'repaid' 'delayed' 'fully repaid' 'fully repaid this bank']\n",
      "purpose: ['radio/tv' 'education' 'furniture' 'car (new)' 'car (used)' 'business'\n",
      " 'domestic appliances' 'repairs' 'others' 'retraining']\n",
      "employment_length: ['> 7 yrs' '1 - 4 yrs' '4 - 7 yrs' 'unemployed' '0 - 1 yrs']\n",
      "installment_rate: [4 2 3 1]\n",
      "personal_status: ['single male' 'female' 'divorced male' 'married male']\n",
      "jobs: ['skilled employee' 'unskilled resident' 'mangement self-employed'\n",
      " 'unemployed non-resident']\n"
     ]
    }
   ],
   "source": [
    "#loans.columns[-1]\n",
    "print(\"checking_balance:\",loans['checking_balance'].unique())\n",
    "print(\"savings_balance:\",loans['savings_balance'].unique())\n",
    "print(\"months_loan_duration:\",loans['months_loan_duration'].unique()) # can be considered continuous\n",
    "print(\"credit_history:\",loans['credit_history'].unique()) # consolidate: critical, repaid, delayed [labels]\n",
    "print(\"purpose:\",loans['purpose'].unique())\n",
    "print(\"employment_length:\",loans['employment_length'].unique()) # encode as numbers from 0 (unemployed) to 4(>7 yrs)\n",
    "print(\"installment_rate:\",loans['installment_rate'].unique())\n",
    "print(\"personal_status:\",loans['personal_status'].unique()) # split into gender and personal status\n",
    "# .... check the rest of the columns\n",
    "print(\"jobs:\",loans['job'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown       394\n",
       "< 0 DM        274\n",
       "1 - 200 DM    269\n",
       "> 200 DM       63\n",
       "Name: checking_balance, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['checking_balance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< 100 DM         603\n",
       "unknown          183\n",
       "101 - 500 DM     103\n",
       "501 - 1000 DM     63\n",
       "> 1000 DM         48\n",
       "Name: savings_balance, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['savings_balance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "repaid                    530\n",
       "critical                  293\n",
       "delayed                    88\n",
       "fully repaid this bank     49\n",
       "fully repaid               40\n",
       "Name: credit_history, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['credit_history'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# parse the personal status into gender and status\n",
    "# female, male or N(one)\n",
    "# we already know the options from above, all lower case, so no further text processing\n",
    "def getGender(txt):\n",
    "    if \"female\" in txt:\n",
    "        return \"F\"\n",
    "    if \"male\" in txt:\n",
    "        return \"M\"\n",
    "    return \"N\"\n",
    "\n",
    "# married, single, divorced or U(nknown)\n",
    "def getStatus(txt):\n",
    "    if \"maried\" in txt:\n",
    "        return \"M\"\n",
    "    if \"single\" in txt:\n",
    "        return \"S\"\n",
    "    if \"divorced\" in txt:\n",
    "        return \"D\"    \n",
    "    return \"U\"\n",
    "# note: you can also encode the personal status as is (using get_dummies). Worth trying.\n",
    "\n",
    "# if credit_history is based on data from other banks (and not what we are predicting), then process as such:\n",
    "def getHistory(txt):\n",
    "    if \"repaid\" in txt:\n",
    "        return \"R\"\n",
    "    if \"critical\" in txt:\n",
    "        return \"C\"  \n",
    "    return \"D\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans[\"gender\"] = loans[\"personal_status\"].apply(getGender)\n",
    "loans[\"pstatus\"] = loans[\"personal_status\"].apply(getStatus)\n",
    "loans[\"history\"] = loans[\"credit_history\"].apply(getHistory)\n",
    "loans[\"default\"] = loans[\"default\"] -1  # get zero based categories (assuming this is our target/label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_balance</th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings_balance</th>\n",
       "      <th>employment_length</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>personal_status</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>residence_history</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>default</th>\n",
       "      <th>dependents</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>job</th>\n",
       "      <th>gender</th>\n",
       "      <th>pstatus</th>\n",
       "      <th>history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>1169</td>\n",
       "      <td>unknown</td>\n",
       "      <td>&gt; 7 yrs</td>\n",
       "      <td>4</td>\n",
       "      <td>single male</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>repaid</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>5951</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>none</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  checking_balance  months_loan_duration credit_history   purpose  amount  \\\n",
       "0           < 0 DM                     6       critical  radio/tv    1169   \n",
       "1       1 - 200 DM                    48         repaid  radio/tv    5951   \n",
       "\n",
       "  savings_balance employment_length  installment_rate personal_status  \\\n",
       "0         unknown           > 7 yrs                 4     single male   \n",
       "1        < 100 DM         1 - 4 yrs                 2          female   \n",
       "\n",
       "  other_debtors  residence_history     property  age installment_plan housing  \\\n",
       "0          none                  4  real estate   67             none     own   \n",
       "1          none                  2  real estate   22             none     own   \n",
       "\n",
       "   existing_credits  default  dependents telephone foreign_worker  \\\n",
       "0                 2        0           1       yes            yes   \n",
       "1                 1        1           1      none            yes   \n",
       "\n",
       "                job gender pstatus history  \n",
       "0  skilled employee      M       S       C  \n",
       "1  skilled employee      F       U       R  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>amount</th>\n",
       "      <th>personal_status</th>\n",
       "      <th>age</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>default</th>\n",
       "      <th>dependents</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>checking_balance_&lt; 0 DM</th>\n",
       "      <th>checking_balance_&gt; 200 DM</th>\n",
       "      <th>checking_balance_unknown</th>\n",
       "      <th>purpose_car (new)</th>\n",
       "      <th>purpose_car (used)</th>\n",
       "      <th>purpose_domestic appliances</th>\n",
       "      <th>purpose_education</th>\n",
       "      <th>purpose_furniture</th>\n",
       "      <th>purpose_others</th>\n",
       "      <th>purpose_radio/tv</th>\n",
       "      <th>purpose_repairs</th>\n",
       "      <th>purpose_retraining</th>\n",
       "      <th>savings_balance_501 - 1000 DM</th>\n",
       "      <th>savings_balance_&lt; 100 DM</th>\n",
       "      <th>savings_balance_&gt; 1000 DM</th>\n",
       "      <th>savings_balance_unknown</th>\n",
       "      <th>employment_length_1 - 4 yrs</th>\n",
       "      <th>employment_length_4 - 7 yrs</th>\n",
       "      <th>employment_length_&gt; 7 yrs</th>\n",
       "      <th>employment_length_unemployed</th>\n",
       "      <th>installment_rate_2</th>\n",
       "      <th>installment_rate_3</th>\n",
       "      <th>installment_rate_4</th>\n",
       "      <th>other_debtors_guarantor</th>\n",
       "      <th>other_debtors_none</th>\n",
       "      <th>residence_history_2</th>\n",
       "      <th>residence_history_3</th>\n",
       "      <th>residence_history_4</th>\n",
       "      <th>property_other</th>\n",
       "      <th>property_real estate</th>\n",
       "      <th>property_unknown/none</th>\n",
       "      <th>installment_plan_none</th>\n",
       "      <th>installment_plan_stores</th>\n",
       "      <th>housing_own</th>\n",
       "      <th>housing_rent</th>\n",
       "      <th>telephone_yes</th>\n",
       "      <th>foreign_worker_yes</th>\n",
       "      <th>job_skilled employee</th>\n",
       "      <th>job_unemployed non-resident</th>\n",
       "      <th>job_unskilled resident</th>\n",
       "      <th>pstatus_S</th>\n",
       "      <th>pstatus_U</th>\n",
       "      <th>history_D</th>\n",
       "      <th>history_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>critical</td>\n",
       "      <td>1169</td>\n",
       "      <td>single male</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>repaid</td>\n",
       "      <td>5951</td>\n",
       "      <td>female</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_loan_duration credit_history  amount personal_status  age  \\\n",
       "0                     6       critical    1169     single male   67   \n",
       "1                    48         repaid    5951          female   22   \n",
       "\n",
       "   existing_credits  default  dependents  gender_M  checking_balance_< 0 DM  \\\n",
       "0                 2        0           1         1                        1   \n",
       "1                 1        1           1         0                        0   \n",
       "\n",
       "   checking_balance_> 200 DM  checking_balance_unknown  purpose_car (new)  \\\n",
       "0                          0                         0                  0   \n",
       "1                          0                         0                  0   \n",
       "\n",
       "   purpose_car (used)  purpose_domestic appliances  purpose_education  \\\n",
       "0                   0                            0                  0   \n",
       "1                   0                            0                  0   \n",
       "\n",
       "   purpose_furniture  purpose_others  purpose_radio/tv  purpose_repairs  \\\n",
       "0                  0               0                 1                0   \n",
       "1                  0               0                 1                0   \n",
       "\n",
       "   purpose_retraining  savings_balance_501 - 1000 DM  \\\n",
       "0                   0                              0   \n",
       "1                   0                              0   \n",
       "\n",
       "   savings_balance_< 100 DM  savings_balance_> 1000 DM  \\\n",
       "0                         0                          0   \n",
       "1                         1                          0   \n",
       "\n",
       "   savings_balance_unknown  employment_length_1 - 4 yrs  \\\n",
       "0                        1                            0   \n",
       "1                        0                            1   \n",
       "\n",
       "   employment_length_4 - 7 yrs  employment_length_> 7 yrs  \\\n",
       "0                            0                          1   \n",
       "1                            0                          0   \n",
       "\n",
       "   employment_length_unemployed  installment_rate_2  installment_rate_3  \\\n",
       "0                             0                   0                   0   \n",
       "1                             0                   1                   0   \n",
       "\n",
       "   installment_rate_4  other_debtors_guarantor  other_debtors_none  \\\n",
       "0                   1                        0                   1   \n",
       "1                   0                        0                   1   \n",
       "\n",
       "   residence_history_2  residence_history_3  residence_history_4  \\\n",
       "0                    0                    0                    1   \n",
       "1                    1                    0                    0   \n",
       "\n",
       "   property_other  property_real estate  property_unknown/none  \\\n",
       "0               0                     1                      0   \n",
       "1               0                     1                      0   \n",
       "\n",
       "   installment_plan_none  installment_plan_stores  housing_own  housing_rent  \\\n",
       "0                      1                        0            1             0   \n",
       "1                      1                        0            1             0   \n",
       "\n",
       "   telephone_yes  foreign_worker_yes  job_skilled employee  \\\n",
       "0              1                   1                     1   \n",
       "1              0                   1                     1   \n",
       "\n",
       "   job_unemployed non-resident  job_unskilled resident  pstatus_S  pstatus_U  \\\n",
       "0                            0                       0          1          0   \n",
       "1                            0                       0          0          1   \n",
       "\n",
       "   history_D  history_R  \n",
       "0          0          0  \n",
       "1          0          1  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not sure about existing_credits. This may not be the best way ...\n",
    "dummy_columns = ['gender','checking_balance','purpose','savings_balance','employment_length',\n",
    "                 'installment_rate','other_debtors','residence_history', 'property','installment_plan',\n",
    "                'housing','telephone','foreign_worker','job','pstatus', 'history']\n",
    "new_loans = pd.get_dummies(loans, columns=dummy_columns, drop_first=True)\n",
    "new_loans.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>amount</th>\n",
       "      <th>age</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>default</th>\n",
       "      <th>dependents</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>checking_balance_&lt; 0 DM</th>\n",
       "      <th>checking_balance_&gt; 200 DM</th>\n",
       "      <th>checking_balance_unknown</th>\n",
       "      <th>purpose_car (new)</th>\n",
       "      <th>purpose_car (used)</th>\n",
       "      <th>purpose_domestic appliances</th>\n",
       "      <th>purpose_education</th>\n",
       "      <th>purpose_furniture</th>\n",
       "      <th>purpose_others</th>\n",
       "      <th>purpose_radio/tv</th>\n",
       "      <th>purpose_repairs</th>\n",
       "      <th>purpose_retraining</th>\n",
       "      <th>savings_balance_501 - 1000 DM</th>\n",
       "      <th>savings_balance_&lt; 100 DM</th>\n",
       "      <th>savings_balance_&gt; 1000 DM</th>\n",
       "      <th>savings_balance_unknown</th>\n",
       "      <th>employment_length_1 - 4 yrs</th>\n",
       "      <th>employment_length_4 - 7 yrs</th>\n",
       "      <th>employment_length_&gt; 7 yrs</th>\n",
       "      <th>employment_length_unemployed</th>\n",
       "      <th>installment_rate_2</th>\n",
       "      <th>installment_rate_3</th>\n",
       "      <th>installment_rate_4</th>\n",
       "      <th>other_debtors_guarantor</th>\n",
       "      <th>other_debtors_none</th>\n",
       "      <th>residence_history_2</th>\n",
       "      <th>residence_history_3</th>\n",
       "      <th>residence_history_4</th>\n",
       "      <th>property_other</th>\n",
       "      <th>property_real estate</th>\n",
       "      <th>property_unknown/none</th>\n",
       "      <th>installment_plan_none</th>\n",
       "      <th>installment_plan_stores</th>\n",
       "      <th>housing_own</th>\n",
       "      <th>housing_rent</th>\n",
       "      <th>telephone_yes</th>\n",
       "      <th>foreign_worker_yes</th>\n",
       "      <th>job_skilled employee</th>\n",
       "      <th>job_unemployed non-resident</th>\n",
       "      <th>job_unskilled resident</th>\n",
       "      <th>pstatus_S</th>\n",
       "      <th>pstatus_U</th>\n",
       "      <th>history_D</th>\n",
       "      <th>history_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1169</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>5951</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_loan_duration  amount  age  existing_credits  default  dependents  \\\n",
       "0                     6    1169   67                 2        0           1   \n",
       "1                    48    5951   22                 1        1           1   \n",
       "\n",
       "   gender_M  checking_balance_< 0 DM  checking_balance_> 200 DM  \\\n",
       "0         1                        1                          0   \n",
       "1         0                        0                          0   \n",
       "\n",
       "   checking_balance_unknown  purpose_car (new)  purpose_car (used)  \\\n",
       "0                         0                  0                   0   \n",
       "1                         0                  0                   0   \n",
       "\n",
       "   purpose_domestic appliances  purpose_education  purpose_furniture  \\\n",
       "0                            0                  0                  0   \n",
       "1                            0                  0                  0   \n",
       "\n",
       "   purpose_others  purpose_radio/tv  purpose_repairs  purpose_retraining  \\\n",
       "0               0                 1                0                   0   \n",
       "1               0                 1                0                   0   \n",
       "\n",
       "   savings_balance_501 - 1000 DM  savings_balance_< 100 DM  \\\n",
       "0                              0                         0   \n",
       "1                              0                         1   \n",
       "\n",
       "   savings_balance_> 1000 DM  savings_balance_unknown  \\\n",
       "0                          0                        1   \n",
       "1                          0                        0   \n",
       "\n",
       "   employment_length_1 - 4 yrs  employment_length_4 - 7 yrs  \\\n",
       "0                            0                            0   \n",
       "1                            1                            0   \n",
       "\n",
       "   employment_length_> 7 yrs  employment_length_unemployed  \\\n",
       "0                          1                             0   \n",
       "1                          0                             0   \n",
       "\n",
       "   installment_rate_2  installment_rate_3  installment_rate_4  \\\n",
       "0                   0                   0                   1   \n",
       "1                   1                   0                   0   \n",
       "\n",
       "   other_debtors_guarantor  other_debtors_none  residence_history_2  \\\n",
       "0                        0                   1                    0   \n",
       "1                        0                   1                    1   \n",
       "\n",
       "   residence_history_3  residence_history_4  property_other  \\\n",
       "0                    0                    1               0   \n",
       "1                    0                    0               0   \n",
       "\n",
       "   property_real estate  property_unknown/none  installment_plan_none  \\\n",
       "0                     1                      0                      1   \n",
       "1                     1                      0                      1   \n",
       "\n",
       "   installment_plan_stores  housing_own  housing_rent  telephone_yes  \\\n",
       "0                        0            1             0              1   \n",
       "1                        0            1             0              0   \n",
       "\n",
       "   foreign_worker_yes  job_skilled employee  job_unemployed non-resident  \\\n",
       "0                   1                     1                            0   \n",
       "1                   1                     1                            0   \n",
       "\n",
       "   job_unskilled resident  pstatus_S  pstatus_U  history_D  history_R  \n",
       "0                       0          1          0          0          0  \n",
       "1                       0          0          1          0          1  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans = new_loans.drop(['credit_history', 'personal_status'], axis=1)\n",
    "loans.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 50)\n",
      "(1000,)\n"
     ]
    }
   ],
   "source": [
    "X = loans.drop('default',axis=1)\n",
    "y = loans['default']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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