1. Data Preparation.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "166aeb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "# pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e12342e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 21)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Checking_acc</th>\n",
       "      <th>duration_mnth</th>\n",
       "      <th>Credit_hist</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>credit_amt</th>\n",
       "      <th>Saving_acc</th>\n",
       "      <th>Emp_status</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>Pstatus_and_sex</th>\n",
       "      <th>Guarantor</th>\n",
       "      <th>Residence_LOS</th>\n",
       "      <th>Property</th>\n",
       "      <th>age</th>\n",
       "      <th>Installment_plan</th>\n",
       "      <th>Housing</th>\n",
       "      <th>existing_credit</th>\n",
       "      <th>Job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>credit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A11</td>\n",
       "      <td>6</td>\n",
       "      <td>A34</td>\n",
       "      <td>A43</td>\n",
       "      <td>1169</td>\n",
       "      <td>A65</td>\n",
       "      <td>A75</td>\n",
       "      <td>4</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A121</td>\n",
       "      <td>67</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>1</td>\n",
       "      <td>A192</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A12</td>\n",
       "      <td>48</td>\n",
       "      <td>A32</td>\n",
       "      <td>A43</td>\n",
       "      <td>5951</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>2</td>\n",
       "      <td>A92</td>\n",
       "      <td>A101</td>\n",
       "      <td>2</td>\n",
       "      <td>A121</td>\n",
       "      <td>22</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>1</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A14</td>\n",
       "      <td>12</td>\n",
       "      <td>A34</td>\n",
       "      <td>A46</td>\n",
       "      <td>2096</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>2</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>3</td>\n",
       "      <td>A121</td>\n",
       "      <td>49</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A172</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A11</td>\n",
       "      <td>42</td>\n",
       "      <td>A32</td>\n",
       "      <td>A42</td>\n",
       "      <td>7882</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>2</td>\n",
       "      <td>A93</td>\n",
       "      <td>A103</td>\n",
       "      <td>4</td>\n",
       "      <td>A122</td>\n",
       "      <td>45</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A11</td>\n",
       "      <td>24</td>\n",
       "      <td>A33</td>\n",
       "      <td>A40</td>\n",
       "      <td>4870</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>3</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A124</td>\n",
       "      <td>53</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Checking_acc  duration_mnth Credit_hist Purpose  credit_amt Saving_acc  \\\n",
       "0          A11              6         A34     A43        1169        A65   \n",
       "1          A12             48         A32     A43        5951        A61   \n",
       "2          A14             12         A34     A46        2096        A61   \n",
       "3          A11             42         A32     A42        7882        A61   \n",
       "4          A11             24         A33     A40        4870        A61   \n",
       "\n",
       "  Emp_status  installment_rate Pstatus_and_sex Guarantor  Residence_LOS  \\\n",
       "0        A75                 4             A93      A101              4   \n",
       "1        A73                 2             A92      A101              2   \n",
       "2        A74                 2             A93      A101              3   \n",
       "3        A74                 2             A93      A103              4   \n",
       "4        A73                 3             A93      A101              4   \n",
       "\n",
       "  Property  age Installment_plan Housing  existing_credit   Job  dependents  \\\n",
       "0     A121   67             A143    A152                2  A173           1   \n",
       "1     A121   22             A143    A152                1  A173           1   \n",
       "2     A121   49             A143    A152                1  A172           2   \n",
       "3     A122   45             A143    A153                1  A173           2   \n",
       "4     A124   53             A143    A153                2  A173           2   \n",
       "\n",
       "  Telephone Foreign_worker  credit  \n",
       "0      A192           A201       1  \n",
       "1      A191           A201       2  \n",
       "2      A191           A201       1  \n",
       "3      A191           A201       1  \n",
       "4      A191           A201       2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read CSV\n",
    "data = pd.read_csv('./data/german.data', sep=\" \", names=['Checking_acc', 'duration_mnth', 'Credit_hist', \n",
    "                'Purpose', 'credit_amt', 'Saving_acc', 'Emp_status','installment_rate','Pstatus_and_sex',\n",
    "                'Guarantor','Residence_LOS', 'Property', 'age', 'Installment_plan', 'Housing',\n",
    "                 'existing_credit', 'Job', 'dependents', 'Telephone', 'Foreign_worker', 'credit'])\n",
    "print(data.shape) \n",
    "data.to_csv('./data/GermanCredit_num.csv', index=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cd5988",
   "metadata": {},
   "source": [
    "#### Creating a dictionary for each features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "755640af",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code1</th>\n",
       "      <th>checking_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A11</td>\n",
       "      <td>Less than 0 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A12</td>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A13</td>\n",
       "      <td>Equal or Greater than 200 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A14</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  code1                  checking_acc\n",
       "0   A11                Less than 0 DM\n",
       "1   A12               Btw 0 to 199 DM\n",
       "2   A13  Equal or Greater than 200 DM\n",
       "3   A14                          none"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Account feature\n",
    "checking_acc_dict = {\n",
    "    'code1': ['A11', 'A12', 'A13', 'A14'],\n",
    "    'checking_acc': ['Less than 0 DM', 'Btw 0 to 199 DM', 'Equal or Greater than 200 DM', 'none']\n",
    "}\n",
    "\n",
    "checking_acc_dict = pd.DataFrame(checking_acc_dict)\n",
    "checking_acc_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1793dae9",
   "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>code2</th>\n",
       "      <th>credit_hist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A30</td>\n",
       "      <td>No credits taken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>All credits taken paid back duly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A33</td>\n",
       "      <td>Delays in paying off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A34</td>\n",
       "      <td>Critical account</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  code2                       credit_hist\n",
       "0   A30                  No credits taken\n",
       "1   A31  All credits taken paid back duly\n",
       "2   A32   Existing credits paid back duly\n",
       "3   A33              Delays in paying off\n",
       "4   A34                  Critical account"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Credit History Feature\n",
    "credit_hist_dict = {\n",
    "    'code2': ['A30','A31','A32','A33','A34'],\n",
    "    'credit_hist': ['No credits taken','All credits taken paid back duly', 'Existing credits paid back duly',\n",
    "                'Delays in paying off','Critical account']\n",
    "}\n",
    "credit_hist_dict = pd.DataFrame(credit_hist_dict)\n",
    "credit_hist_dict   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c8f5e5d",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code3</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A40</td>\n",
       "      <td>new car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A41</td>\n",
       "      <td>used car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A43</td>\n",
       "      <td>radio/television</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A44</td>\n",
       "      <td>domestic appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A45</td>\n",
       "      <td>repairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A46</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A47</td>\n",
       "      <td>vacation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A48</td>\n",
       "      <td>retraining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A49</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A410</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   code3              purpose\n",
       "0    A40              new car\n",
       "1    A41             used car\n",
       "2    A42  furniture/equipment\n",
       "3    A43     radio/television\n",
       "4    A44  domestic appliances\n",
       "5    A45              repairs\n",
       "6    A46            education\n",
       "7    A47             vacation\n",
       "8    A48           retraining\n",
       "9    A49             business\n",
       "10  A410               others"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Purpose  Feature\n",
    "purpose_dict = {\n",
    "    'code3': ['A40','A41', 'A42', 'A43', 'A44', 'A45', 'A46', 'A47', 'A48', 'A49', 'A410' ],\n",
    "    'purpose': ['new car', 'used car', 'furniture/equipment', 'radio/television', 'domestic appliances',\n",
    "                'repairs', 'education', 'vacation', 'retraining', 'business', 'others']\n",
    "}\n",
    "purpose_dict= pd.DataFrame(purpose_dict)\n",
    "purpose_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c02df322",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code4</th>\n",
       "      <th>savings_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A61</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A62</td>\n",
       "      <td>Btw 100 to 499 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A63</td>\n",
       "      <td>Btw 500 to 999 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A64</td>\n",
       "      <td>Equal or Greater than 1000 DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A65</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code4                    savings_acc\n",
       "0   A61               Less than 100 DM\n",
       "1   A62              Btw 100 to 499 DM\n",
       "2   A63              Btw 500 to 999 DM\n",
       "3   A64  Equal or Greater than 1000 DM\n",
       "4   A65                           none"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Savings account Feature\n",
    "savings_acc_dict = {\n",
    "    'code4': ['A61', 'A62', 'A63', 'A64', 'A65'],\n",
    "    'savings_acc': ['Less than 100 DM','Btw 100 to 499 DM','Btw 500 to 999 DM','Equal or Greater than 1000 DM', 'none']\n",
    "}\n",
    "savings_acc_dict= pd.DataFrame(savings_acc_dict)\n",
    "savings_acc_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "324dc999",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code5</th>\n",
       "      <th>emp_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A71</td>\n",
       "      <td>unemployed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A72</td>\n",
       "      <td>Less than a year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A73</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A74</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A75</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code5            emp_status\n",
       "0   A71            unemployed\n",
       "1   A72      Less than a year\n",
       "2   A73      Btw 1 to 4 years\n",
       "3   A74      Btw 4 to 7 years\n",
       "4   A75  Greater than 7 years"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Employment Status  Feature\n",
    "emp_status_dict = {\n",
    "    'code5': ['A71', 'A72', 'A73', 'A74', 'A75'],\n",
    "    'emp_status': ['unemployed', 'Less than a year', 'Btw 1 to 4 years', 'Btw 4 to 7 years', 'Greater than 7 years']\n",
    "}\n",
    "emp_status_dict= pd.DataFrame(emp_status_dict)\n",
    "emp_status_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9557bb21",
   "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>code6</th>\n",
       "      <th>pstatus_sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A91</td>\n",
       "      <td>male_divorced/separated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A92</td>\n",
       "      <td>female_divorced/separated/married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A93</td>\n",
       "      <td>male_single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A94</td>\n",
       "      <td>male_married/widowed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A95</td>\n",
       "      <td>female_single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code6                        pstatus_sex\n",
       "0   A91            male_divorced/separated\n",
       "1   A92  female_divorced/separated/married\n",
       "2   A93                        male_single\n",
       "3   A94               male_married/widowed\n",
       "4   A95                      female_single"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Personal Status and sex  Feature\n",
    "pstatus_sex_dict = {\n",
    "    'code6': ['A91', 'A92', 'A93', 'A94', 'A95'],\n",
    "    'pstatus_sex': [ 'male_divorced/separated', 'female_divorced/separated/married','male_single', 'male_married/widowed',\n",
    "                'female_single']   \n",
    "}\n",
    "pstatus_sex_dict= pd.DataFrame(pstatus_sex_dict)\n",
    "pstatus_sex_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43418b04",
   "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>code7</th>\n",
       "      <th>guarantor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A101</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A102</td>\n",
       "      <td>co-applicant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A103</td>\n",
       "      <td>guarantor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code7     guarantor\n",
       "0  A101          none\n",
       "1  A102  co-applicant\n",
       "2  A103     guarantor"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Guarantor Feature\n",
    "guarantor_dict = {\n",
    "    'code7': ['A101', 'A102', 'A103'],\n",
    "    'guarantor': ['none','co-applicant','guarantor']   \n",
    "}\n",
    "guarantor_dict= pd.DataFrame(guarantor_dict)\n",
    "guarantor_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47f4d3d7",
   "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>code8</th>\n",
       "      <th>property</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A121</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A122</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A123</td>\n",
       "      <td>car or other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A124</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code8                property\n",
       "0  A121             real estate\n",
       "1  A122  BSSA or life insurance\n",
       "2  A123            car or other\n",
       "3  A124                    none"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Property Feature\n",
    "property_dict = {\n",
    "    'code8': ['A121', 'A122', 'A123', 'A124'],\n",
    "    'property': ['real estate','BSSA or life insurance', 'car or other', 'none']       \n",
    "}\n",
    "property_dict= pd.DataFrame(property_dict)\n",
    "property_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd8a85d3",
   "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>code9</th>\n",
       "      <th>installment_plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A141</td>\n",
       "      <td>bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A142</td>\n",
       "      <td>stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code9 installment_plan\n",
       "0  A141             bank\n",
       "1  A142           stores\n",
       "2  A143             none"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Installment Plan Feature\n",
    "instl_plan_dict = {\n",
    "    'code9': ['A141', 'A142', 'A143'],\n",
    "    'installment_plan': ['bank','stores', 'none']  \n",
    "}\n",
    "instl_plan_dict= pd.DataFrame(instl_plan_dict)\n",
    "instl_plan_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4773b03a",
   "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>code10</th>\n",
       "      <th>housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A151</td>\n",
       "      <td>rent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A152</td>\n",
       "      <td>own</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A153</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code10 housing\n",
       "0   A151    rent\n",
       "1   A152     own\n",
       "2   A153    free"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Housing Feature\n",
    "housing_dict = {\n",
    "    'code10': ['A151', 'A152', 'A153'],\n",
    "    'housing': ['rent','own', 'free']  \n",
    "}\n",
    "housing_dict= pd.DataFrame(housing_dict)\n",
    "housing_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "99b58eac",
   "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>code11</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A171</td>\n",
       "      <td>unemployed/unskilled - non-resident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A172</td>\n",
       "      <td>unskilled - resident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A173</td>\n",
       "      <td>skilled employee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A174</td>\n",
       "      <td>self-employed/management employee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code11                                  job\n",
       "0   A171  unemployed/unskilled - non-resident\n",
       "1   A172                 unskilled - resident\n",
       "2   A173                     skilled employee\n",
       "3   A174    self-employed/management employee"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Feature\n",
    "job_dict = {\n",
    "    'code11': ['A171', 'A172', 'A173', 'A174'],\n",
    "    'job': ['unemployed/unskilled - non-resident','unskilled - resident','skilled employee',\n",
    "                'self-employed/management employee']\n",
    "}\n",
    "job_dict= pd.DataFrame(job_dict)\n",
    "job_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d723ed9a",
   "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>code12</th>\n",
       "      <th>telephone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A191</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A192</td>\n",
       "      <td>registered</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code12   telephone\n",
       "0   A191        none\n",
       "1   A192  registered"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# telephone\n",
    "telephone_dict = {\n",
    "    'code12':['A191', 'A192'],\n",
    "    'telephone': ['none','registered']\n",
    "}\n",
    "telephone_dict= pd.DataFrame(telephone_dict)\n",
    "telephone_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f1ed66ce",
   "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>code13</th>\n",
       "      <th>foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A202</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code13 foreign_worker\n",
       "0   A201            yes\n",
       "1   A202             no"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Foreign worker Feature\n",
    "foreign_worker_dict = {\n",
    "    'code13':['A201', 'A202'],\n",
    "    'foreign_worker': ['yes','no']\n",
    "}\n",
    "foreign_worker_dict= pd.DataFrame(foreign_worker_dict)\n",
    "foreign_worker_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10d49ce",
   "metadata": {},
   "source": [
    "#### Merging Data and Dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "216a23f3",
   "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>Checking_acc</th>\n",
       "      <th>duration_mnth</th>\n",
       "      <th>Credit_hist</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>credit_amt</th>\n",
       "      <th>Saving_acc</th>\n",
       "      <th>Emp_status</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>Pstatus_and_sex</th>\n",
       "      <th>Guarantor</th>\n",
       "      <th>Residence_LOS</th>\n",
       "      <th>Property</th>\n",
       "      <th>age</th>\n",
       "      <th>Installment_plan</th>\n",
       "      <th>Housing</th>\n",
       "      <th>existing_credit</th>\n",
       "      <th>Job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>credit</th>\n",
       "      <th>code1</th>\n",
       "      <th>checking_acc</th>\n",
       "      <th>code2</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>code3</th>\n",
       "      <th>purpose</th>\n",
       "      <th>code4</th>\n",
       "      <th>savings_acc</th>\n",
       "      <th>code5</th>\n",
       "      <th>emp_status</th>\n",
       "      <th>code6</th>\n",
       "      <th>pstatus_sex</th>\n",
       "      <th>code7</th>\n",
       "      <th>guarantor</th>\n",
       "      <th>code8</th>\n",
       "      <th>property</th>\n",
       "      <th>code9</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>code10</th>\n",
       "      <th>housing</th>\n",
       "      <th>code11</th>\n",
       "      <th>job</th>\n",
       "      <th>code12</th>\n",
       "      <th>telephone</th>\n",
       "      <th>code13</th>\n",
       "      <th>foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A11</td>\n",
       "      <td>6</td>\n",
       "      <td>A34</td>\n",
       "      <td>A43</td>\n",
       "      <td>1169</td>\n",
       "      <td>A65</td>\n",
       "      <td>A75</td>\n",
       "      <td>4</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A121</td>\n",
       "      <td>67</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>1</td>\n",
       "      <td>A192</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "      <td>A11</td>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>A34</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>A43</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>A65</td>\n",
       "      <td>none</td>\n",
       "      <td>A75</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "      <td>A93</td>\n",
       "      <td>male_single</td>\n",
       "      <td>A101</td>\n",
       "      <td>none</td>\n",
       "      <td>A121</td>\n",
       "      <td>real estate</td>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "      <td>A152</td>\n",
       "      <td>own</td>\n",
       "      <td>A173</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>A192</td>\n",
       "      <td>registered</td>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A12</td>\n",
       "      <td>48</td>\n",
       "      <td>A32</td>\n",
       "      <td>A43</td>\n",
       "      <td>5951</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>2</td>\n",
       "      <td>A92</td>\n",
       "      <td>A101</td>\n",
       "      <td>2</td>\n",
       "      <td>A121</td>\n",
       "      <td>22</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>1</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "      <td>A12</td>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "      <td>A32</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>A43</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>A61</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>A73</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>A92</td>\n",
       "      <td>female_divorced/separated/married</td>\n",
       "      <td>A101</td>\n",
       "      <td>none</td>\n",
       "      <td>A121</td>\n",
       "      <td>real estate</td>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "      <td>A152</td>\n",
       "      <td>own</td>\n",
       "      <td>A173</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>A191</td>\n",
       "      <td>none</td>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A14</td>\n",
       "      <td>12</td>\n",
       "      <td>A34</td>\n",
       "      <td>A46</td>\n",
       "      <td>2096</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>2</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>3</td>\n",
       "      <td>A121</td>\n",
       "      <td>49</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A172</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "      <td>A14</td>\n",
       "      <td>none</td>\n",
       "      <td>A34</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>A46</td>\n",
       "      <td>education</td>\n",
       "      <td>A61</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>A74</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>A93</td>\n",
       "      <td>male_single</td>\n",
       "      <td>A101</td>\n",
       "      <td>none</td>\n",
       "      <td>A121</td>\n",
       "      <td>real estate</td>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "      <td>A152</td>\n",
       "      <td>own</td>\n",
       "      <td>A172</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>A191</td>\n",
       "      <td>none</td>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A11</td>\n",
       "      <td>42</td>\n",
       "      <td>A32</td>\n",
       "      <td>A42</td>\n",
       "      <td>7882</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>2</td>\n",
       "      <td>A93</td>\n",
       "      <td>A103</td>\n",
       "      <td>4</td>\n",
       "      <td>A122</td>\n",
       "      <td>45</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "      <td>A11</td>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>A32</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>A42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>A61</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>A74</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>A93</td>\n",
       "      <td>male_single</td>\n",
       "      <td>A103</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>A122</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "      <td>A153</td>\n",
       "      <td>free</td>\n",
       "      <td>A173</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>A191</td>\n",
       "      <td>none</td>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A11</td>\n",
       "      <td>24</td>\n",
       "      <td>A33</td>\n",
       "      <td>A40</td>\n",
       "      <td>4870</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>3</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A124</td>\n",
       "      <td>53</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>2</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "      <td>A11</td>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>A33</td>\n",
       "      <td>Delays in paying off</td>\n",
       "      <td>A40</td>\n",
       "      <td>new car</td>\n",
       "      <td>A61</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>A73</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>A93</td>\n",
       "      <td>male_single</td>\n",
       "      <td>A101</td>\n",
       "      <td>none</td>\n",
       "      <td>A124</td>\n",
       "      <td>none</td>\n",
       "      <td>A143</td>\n",
       "      <td>none</td>\n",
       "      <td>A153</td>\n",
       "      <td>free</td>\n",
       "      <td>A173</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>A191</td>\n",
       "      <td>none</td>\n",
       "      <td>A201</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Checking_acc  duration_mnth Credit_hist Purpose  credit_amt Saving_acc  \\\n",
       "0          A11              6         A34     A43        1169        A65   \n",
       "1          A12             48         A32     A43        5951        A61   \n",
       "2          A14             12         A34     A46        2096        A61   \n",
       "3          A11             42         A32     A42        7882        A61   \n",
       "4          A11             24         A33     A40        4870        A61   \n",
       "\n",
       "  Emp_status  installment_rate Pstatus_and_sex Guarantor  Residence_LOS  \\\n",
       "0        A75                 4             A93      A101              4   \n",
       "1        A73                 2             A92      A101              2   \n",
       "2        A74                 2             A93      A101              3   \n",
       "3        A74                 2             A93      A103              4   \n",
       "4        A73                 3             A93      A101              4   \n",
       "\n",
       "  Property  age Installment_plan Housing  existing_credit   Job  dependents  \\\n",
       "0     A121   67             A143    A152                2  A173           1   \n",
       "1     A121   22             A143    A152                1  A173           1   \n",
       "2     A121   49             A143    A152                1  A172           2   \n",
       "3     A122   45             A143    A153                1  A173           2   \n",
       "4     A124   53             A143    A153                2  A173           2   \n",
       "\n",
       "  Telephone Foreign_worker  credit code1     checking_acc code2  \\\n",
       "0      A192           A201       1   A11   Less than 0 DM   A34   \n",
       "1      A191           A201       2   A12  Btw 0 to 199 DM   A32   \n",
       "2      A191           A201       1   A14             none   A34   \n",
       "3      A191           A201       1   A11   Less than 0 DM   A32   \n",
       "4      A191           A201       2   A11   Less than 0 DM   A33   \n",
       "\n",
       "                       credit_hist code3              purpose code4  \\\n",
       "0                 Critical account   A43     radio/television   A65   \n",
       "1  Existing credits paid back duly   A43     radio/television   A61   \n",
       "2                 Critical account   A46            education   A61   \n",
       "3  Existing credits paid back duly   A42  furniture/equipment   A61   \n",
       "4             Delays in paying off   A40              new car   A61   \n",
       "\n",
       "        savings_acc code5            emp_status code6  \\\n",
       "0              none   A75  Greater than 7 years   A93   \n",
       "1  Less than 100 DM   A73      Btw 1 to 4 years   A92   \n",
       "2  Less than 100 DM   A74      Btw 4 to 7 years   A93   \n",
       "3  Less than 100 DM   A74      Btw 4 to 7 years   A93   \n",
       "4  Less than 100 DM   A73      Btw 1 to 4 years   A93   \n",
       "\n",
       "                         pstatus_sex code7  guarantor code8  \\\n",
       "0                        male_single  A101       none  A121   \n",
       "1  female_divorced/separated/married  A101       none  A121   \n",
       "2                        male_single  A101       none  A121   \n",
       "3                        male_single  A103  guarantor  A122   \n",
       "4                        male_single  A101       none  A124   \n",
       "\n",
       "                 property code9 installment_plan code10 housing code11  \\\n",
       "0             real estate  A143             none   A152     own   A173   \n",
       "1             real estate  A143             none   A152     own   A173   \n",
       "2             real estate  A143             none   A152     own   A172   \n",
       "3  BSSA or life insurance  A143             none   A153    free   A173   \n",
       "4                    none  A143             none   A153    free   A173   \n",
       "\n",
       "                    job code12   telephone code13 foreign_worker  \n",
       "0      skilled employee   A192  registered   A201            yes  \n",
       "1      skilled employee   A191        none   A201            yes  \n",
       "2  unskilled - resident   A191        none   A201            yes  \n",
       "3      skilled employee   A191        none   A201            yes  \n",
       "4      skilled employee   A191        none   A201            yes  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging all the columns\n",
    "merged_df = pd.merge(data, checking_acc_dict, left_on='Checking_acc', right_on='code1', how='left')\n",
    "merged_df = pd.merge(merged_df, credit_hist_dict, left_on='Credit_hist', right_on='code2', how='left')\n",
    "merged_df = pd.merge(merged_df, purpose_dict, left_on='Purpose', right_on='code3', how='left')\n",
    "merged_df = pd.merge(merged_df, savings_acc_dict, left_on='Saving_acc', right_on='code4', how='left')\n",
    "merged_df = pd.merge(merged_df, emp_status_dict, left_on='Emp_status', right_on='code5', how='left')\n",
    "merged_df = pd.merge(merged_df, pstatus_sex_dict, left_on='Pstatus_and_sex', right_on='code6', how='left')\n",
    "merged_df = pd.merge(merged_df, guarantor_dict, left_on='Guarantor', right_on='code7', how='left')\n",
    "merged_df = pd.merge(merged_df, property_dict, left_on='Property', right_on='code8', how='left')\n",
    "merged_df = pd.merge(merged_df, instl_plan_dict, left_on='Installment_plan', right_on='code9', how='left')\n",
    "merged_df = pd.merge(merged_df, housing_dict, left_on='Housing', right_on='code10', how='left')\n",
    "merged_df = pd.merge(merged_df, job_dict, left_on='Job', right_on='code11', how='left')\n",
    "merged_df = pd.merge(merged_df, telephone_dict, left_on='Telephone', right_on='code12', how='left')\n",
    "merged_df = pd.merge(merged_df, foreign_worker_dict, left_on='Foreign_worker', right_on='code13', how='left')\n",
    "merged_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11f22b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping repeated data\n",
    "merged_df.drop(['code1','code2','code3', 'code4', 'code5', 'code6','code7', 'code8', 'code9', 'code10',\n",
    "               'code11', 'code12', 'code13', 'Checking_acc', 'Credit_hist', 'Purpose', 'Saving_acc',\n",
    "               'Emp_status', 'Pstatus_and_sex', 'Guarantor','Property', 'Installment_plan', 'Housing',\n",
    "               'Job', 'Telephone', 'Foreign_worker'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "be5d743c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arranging the columns to suit the original data\n",
    "df = merged_df[['checking_acc', 'duration_mnth', 'credit_hist', 'purpose', 'credit_amt',\n",
    "           'savings_acc', 'emp_status',  'installment_rate', 'pstatus_sex', 'guarantor',\n",
    "           'Residence_LOS', 'property', 'age', 'installment_plan', 'housing', 'existing_credit',\n",
    "           'job', 'dependents', 'telephone', 'foreign_worker','credit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e0ebe17",
   "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>checking_acc</th>\n",
       "      <th>duration_mnth</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amt</th>\n",
       "      <th>savings_acc</th>\n",
       "      <th>emp_status</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>pstatus_sex</th>\n",
       "      <th>guarantor</th>\n",
       "      <th>Residence_LOS</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credit</th>\n",
       "      <th>job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>credit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>1169</td>\n",
       "      <td>none</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_single</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>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>registered</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>5951</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>female_divorced/separated/married</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>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>12</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</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>unskilled - resident</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>4</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>Delays in paying off</td>\n",
       "      <td>new car</td>\n",
       "      <td>4870</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>none</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      checking_acc  duration_mnth                      credit_hist  \\\n",
       "0   Less than 0 DM              6                 Critical account   \n",
       "1  Btw 0 to 199 DM             48  Existing credits paid back duly   \n",
       "2             none             12                 Critical account   \n",
       "3   Less than 0 DM             42  Existing credits paid back duly   \n",
       "4   Less than 0 DM             24             Delays in paying off   \n",
       "\n",
       "               purpose  credit_amt       savings_acc            emp_status  \\\n",
       "0     radio/television        1169              none  Greater than 7 years   \n",
       "1     radio/television        5951  Less than 100 DM      Btw 1 to 4 years   \n",
       "2            education        2096  Less than 100 DM      Btw 4 to 7 years   \n",
       "3  furniture/equipment        7882  Less than 100 DM      Btw 4 to 7 years   \n",
       "4              new car        4870  Less than 100 DM      Btw 1 to 4 years   \n",
       "\n",
       "   installment_rate                        pstatus_sex  guarantor  \\\n",
       "0                 4                        male_single       none   \n",
       "1                 2  female_divorced/separated/married       none   \n",
       "2                 2                        male_single       none   \n",
       "3                 2                        male_single  guarantor   \n",
       "4                 3                        male_single       none   \n",
       "\n",
       "   Residence_LOS                property  age installment_plan housing  \\\n",
       "0              4             real estate   67             none     own   \n",
       "1              2             real estate   22             none     own   \n",
       "2              3             real estate   49             none     own   \n",
       "3              4  BSSA or life insurance   45             none    free   \n",
       "4              4                    none   53             none    free   \n",
       "\n",
       "   existing_credit                   job  dependents   telephone  \\\n",
       "0                2      skilled employee           1  registered   \n",
       "1                1      skilled employee           1        none   \n",
       "2                1  unskilled - resident           2        none   \n",
       "3                1      skilled employee           2        none   \n",
       "4                2      skilled employee           2        none   \n",
       "\n",
       "  foreign_worker  credit  \n",
       "0            yes       1  \n",
       "1            yes       2  \n",
       "2            yes       1  \n",
       "3            yes       1  \n",
       "4            yes       2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8417005f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing the target columns\n",
    "df['credit'] = df['credit'].replace({1: 'bad', 2: 'good'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b28abb5",
   "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>checking_acc</th>\n",
       "      <th>duration_mnth</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amt</th>\n",
       "      <th>savings_acc</th>\n",
       "      <th>emp_status</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>pstatus_sex</th>\n",
       "      <th>guarantor</th>\n",
       "      <th>Residence_LOS</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credit</th>\n",
       "      <th>job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>credit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>1169</td>\n",
       "      <td>none</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_single</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>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>registered</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>5951</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>female_divorced/separated/married</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>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>12</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</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>unskilled - resident</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>4</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>Delays in paying off</td>\n",
       "      <td>new car</td>\n",
       "      <td>4870</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>none</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>none</td>\n",
       "      <td>12</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>1736</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>3</td>\n",
       "      <td>female_divorced/separated/married</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>30</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>used car</td>\n",
       "      <td>3857</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_divorced/separated</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "      <td>40</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>self-employed/management employee</td>\n",
       "      <td>1</td>\n",
       "      <td>registered</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>none</td>\n",
       "      <td>12</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>804</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>car or other</td>\n",
       "      <td>38</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>1845</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>none</td>\n",
       "      <td>23</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>registered</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>used car</td>\n",
       "      <td>4576</td>\n",
       "      <td>Btw 100 to 499 DM</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>3</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>car or other</td>\n",
       "      <td>27</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        checking_acc  duration_mnth                      credit_hist  \\\n",
       "0     Less than 0 DM              6                 Critical account   \n",
       "1    Btw 0 to 199 DM             48  Existing credits paid back duly   \n",
       "2               none             12                 Critical account   \n",
       "3     Less than 0 DM             42  Existing credits paid back duly   \n",
       "4     Less than 0 DM             24             Delays in paying off   \n",
       "..               ...            ...                              ...   \n",
       "995             none             12  Existing credits paid back duly   \n",
       "996   Less than 0 DM             30  Existing credits paid back duly   \n",
       "997             none             12  Existing credits paid back duly   \n",
       "998   Less than 0 DM             45  Existing credits paid back duly   \n",
       "999  Btw 0 to 199 DM             45                 Critical account   \n",
       "\n",
       "                 purpose  credit_amt        savings_acc            emp_status  \\\n",
       "0       radio/television        1169               none  Greater than 7 years   \n",
       "1       radio/television        5951   Less than 100 DM      Btw 1 to 4 years   \n",
       "2              education        2096   Less than 100 DM      Btw 4 to 7 years   \n",
       "3    furniture/equipment        7882   Less than 100 DM      Btw 4 to 7 years   \n",
       "4                new car        4870   Less than 100 DM      Btw 1 to 4 years   \n",
       "..                   ...         ...                ...                   ...   \n",
       "995  furniture/equipment        1736   Less than 100 DM      Btw 4 to 7 years   \n",
       "996             used car        3857   Less than 100 DM      Btw 1 to 4 years   \n",
       "997     radio/television         804   Less than 100 DM  Greater than 7 years   \n",
       "998     radio/television        1845   Less than 100 DM      Btw 1 to 4 years   \n",
       "999             used car        4576  Btw 100 to 499 DM            unemployed   \n",
       "\n",
       "     installment_rate                        pstatus_sex  guarantor  \\\n",
       "0                   4                        male_single       none   \n",
       "1                   2  female_divorced/separated/married       none   \n",
       "2                   2                        male_single       none   \n",
       "3                   2                        male_single  guarantor   \n",
       "4                   3                        male_single       none   \n",
       "..                ...                                ...        ...   \n",
       "995                 3  female_divorced/separated/married       none   \n",
       "996                 4            male_divorced/separated       none   \n",
       "997                 4                        male_single       none   \n",
       "998                 4                        male_single       none   \n",
       "999                 3                        male_single       none   \n",
       "\n",
       "     Residence_LOS                property  age installment_plan housing  \\\n",
       "0                4             real estate   67             none     own   \n",
       "1                2             real estate   22             none     own   \n",
       "2                3             real estate   49             none     own   \n",
       "3                4  BSSA or life insurance   45             none    free   \n",
       "4                4                    none   53             none    free   \n",
       "..             ...                     ...  ...              ...     ...   \n",
       "995              4             real estate   31             none     own   \n",
       "996              4  BSSA or life insurance   40             none     own   \n",
       "997              4            car or other   38             none     own   \n",
       "998              4                    none   23             none    free   \n",
       "999              4            car or other   27             none     own   \n",
       "\n",
       "     existing_credit                                job  dependents  \\\n",
       "0                  2                   skilled employee           1   \n",
       "1                  1                   skilled employee           1   \n",
       "2                  1               unskilled - resident           2   \n",
       "3                  1                   skilled employee           2   \n",
       "4                  2                   skilled employee           2   \n",
       "..               ...                                ...         ...   \n",
       "995                1               unskilled - resident           1   \n",
       "996                1  self-employed/management employee           1   \n",
       "997                1                   skilled employee           1   \n",
       "998                1                   skilled employee           1   \n",
       "999                1                   skilled employee           1   \n",
       "\n",
       "      telephone foreign_worker credit  \n",
       "0    registered            yes    bad  \n",
       "1          none            yes   good  \n",
       "2          none            yes    bad  \n",
       "3          none            yes    bad  \n",
       "4          none            yes   good  \n",
       "..          ...            ...    ...  \n",
       "995        none            yes    bad  \n",
       "996  registered            yes    bad  \n",
       "997        none            yes    bad  \n",
       "998  registered            yes   good  \n",
       "999        none            yes    bad  \n",
       "\n",
       "[1000 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ebea37b",
   "metadata": {},
   "source": [
    "#### Exporting the Prepared Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e22d6cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating full data\n",
    "df.to_csv('./data/GermanCredit.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "25a2ffb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating data without the target\n",
    "X = df [['checking_acc', 'duration_mnth', 'credit_hist', 'purpose', 'credit_amt',\n",
    "           'savings_acc', 'emp_status',  'installment_rate', 'pstatus_sex', 'guarantor',\n",
    "           'Residence_LOS', 'property', 'age', 'installment_plan', 'housing', 'existing_credit',\n",
    "           'job', 'dependents', 'telephone', 'foreign_worker']]\n",
    "df.to_csv('./data/GermanCredit_X.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f44fcd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating data with only the target\n",
    "y = df[['credit']]\n",
    "df.to_csv('./data/GermanCredit_y.csv', index=False)"
   ]
  }
 ],
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