Data_Preparation.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Hands-On Machine Learning Project**\n",
    "## **Topic 20: Credit Data**\n",
    "### Florian Korn, Vincent Bläske, Dennis Götz"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# **Table of Content**\n",
    "**[IV. Data Preparation](#data_prep)**<br>\n",
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **IV. Data Preparation** <a id='data_prep'></a>\n",
    "**[1. Data Cleaning](#clean)**<br>\n",
    "**[2. Feature Selection](#select)**<br>\n",
    "**[3. Feature Scaling](#scale)**<br>\n",
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Import of librariers:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Load stored data:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r attributes\n",
    "%store -r cat_attribs\n",
    "%store -r dict_ordinal_map\n",
    "%store -r data_values\n",
    "%store -r data_encoded\n",
    "%store -r data_dummies\n",
    "%store -r train_set\n",
    "%store -r test_set\n",
    "%store -r train_set_encoded\n",
    "%store -r X_train\n",
    "%store -r y_train\n",
    "%store -r X_test\n",
    "%store -r y_test\n",
    "%store -r binary_attribs\n",
    "%store -r onehot_attrbs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### **1. Data Cleaning** <a id='clean'></a> <br> \n",
    "Check for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0]\n",
       "Index: []"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "is_null_check = pd.DataFrame(data_values.isnull().sum())\n",
    "is_null_check[is_null_check[0] > 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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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>Status of existing checking account</th>\n",
       "      <th>Duration in month</th>\n",
       "      <th>Credit history</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Savings account/bonds</th>\n",
       "      <th>Present employment since</th>\n",
       "      <th>Installment rate in percentage of disposable income</th>\n",
       "      <th>Personal status and sex</th>\n",
       "      <th>Other debtors / guarantors</th>\n",
       "      <th>...</th>\n",
       "      <th>Property</th>\n",
       "      <th>Age in years</th>\n",
       "      <th>Other installment plans</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Number of existing credits at this bank</th>\n",
       "      <th>Job</th>\n",
       "      <th>Number of people being liable to provide maintenance for</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>foreign worker</th>\n",
       "      <th>Credit rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1169</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>2</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5951</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2096</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Status of existing checking account  Duration in month  Credit history  \\\n",
       "0                                    1                  6               4   \n",
       "1                                    2                 48               2   \n",
       "2                                    0                 12               4   \n",
       "\n",
       "   Purpose  Credit amount  Savings account/bonds  Present employment since  \\\n",
       "0        3           1169                      0                         4   \n",
       "1        3           5951                      1                         2   \n",
       "2        6           2096                      1                         3   \n",
       "\n",
       "   Installment rate in percentage of disposable income  \\\n",
       "0                                                  4     \n",
       "1                                                  2     \n",
       "2                                                  2     \n",
       "\n",
       "   Personal status and sex  Other debtors / guarantors  ...  Property  \\\n",
       "0                        0                           0  ...         3   \n",
       "1                        3                           0  ...         3   \n",
       "2                        0                           0  ...         3   \n",
       "\n",
       "   Age in years  Other installment plans  Housing  \\\n",
       "0            67                        0        2   \n",
       "1            22                        0        2   \n",
       "2            49                        0        2   \n",
       "\n",
       "   Number of existing credits at this bank  Job  \\\n",
       "0                                        2    2   \n",
       "1                                        1    2   \n",
       "2                                        1    1   \n",
       "\n",
       "   Number of people being liable to provide maintenance for  Telephone  \\\n",
       "0                                                  1                 0   \n",
       "1                                                  1                 1   \n",
       "2                                                  2                 1   \n",
       "\n",
       "   foreign worker  Credit rating  \n",
       "0               0              0  \n",
       "1               0              1  \n",
       "2               0              0  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded = data_values.copy()\n",
    "categories_order = [i for i in dict_ordinal_map.values()]\n",
    "ordinal_encoder = OrdinalEncoder(categories=categories_order, dtype='int')\n",
    "data_encoded[cat_attribs] = ordinal_encoder.fit_transform(data_values[cat_attribs])\n",
    "data_encoded.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Back to IV. Data Preparation](#data_prep)**<br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Feature Selection (analytical)** <a id='select'></a>\n",
    "Decisions to include features for the model training were made based on the [Data_Understanding](./Data_Understanding.ipynb) feature analysis and correlation, as well as semantically meaningful interpretation and in terms of unbiased machine learning models. Some features are not that different in patterns by themselves, but can influence a model in combination with other features. Therefore, we have included them so that the machine learning models decide for themselves which features to include and which not to include."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Status of existing checking account**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        -  We see patterns in the data (see [Data_Understanding](./Data_Understanding.ipynb)) \n",
    "        - It has the secound highest correlation\n",
    "    - Good credit ratings are seven times more likely to have no checking account with us\n",
    "    - The categories <200 DM habe a high probability (<0 DM: 69 % and 0 - 200 DM: 60 %) to be marked as bad credit rating\n",
    "    - Contrary to our thoughts it is not determining for the credit rating if we know the customer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Duration in month** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - we see differences between short and long durations (see [Data_Understanding](./Data_Understanding.ipynb))\n",
    "        - A closer look at \"credit amount\" and \"duration in month\" reveal for some durations patterns, for others no big difference.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = (10,3), sharey = True)\n",
    "fig.suptitle('Overall relation between duration and credit rating / amount')\n",
    "sns.boxplot(ax = ax[0], x = data_values['Credit rating'], y = data_values['Duration in month'])\n",
    "sns.scatterplot(ax = ax[1], x = data_values['Credit amount'], y = data_values['Duration in month'], hue = data_values['Credit rating'], alpha = 0.4)\n",
    "plt.figure(figsize = (15,3))\n",
    "sns.boxplot(data_values[data_values['Duration in month'].isin([7,9,13,16,20,39,33,40,45,54,47,28])], x = 'Duration in month', y = 'Credit amount', hue = 'Credit rating')\n",
    "plt.title('Detailed look on relation between credit amount and duration')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Credit History** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - Differences between good and bad credit ratings in relation to credit history\n",
    "        - Highest negative correlation between features\n",
    "    - Good credit ratings have often existing credits or are a critical account\n",
    "    - A equal likelihood for a good and bad credit is for existing credits paid back duly and dely in paying off\n",
    "    - The worst likelihood for bad credit ratings has \"no credits taken\" and \"all paid back dully\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Purpose** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - Systematic patterns as good credit ratings have often the purpose to retrain, buy a used car or radio / tv\n",
    "    - Credits related to new cars get often a bad credit rating\n",
    "    - A detailed look between the categories and credit amount reveal:\n",
    "        - good credits had low credit amounts for radio / tv\n",
    "        - bad credit ratings are loans for the purpose of education, car (used), business and others - we see that high amounts lead more likely to bad credit ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,3))\n",
    "sns.boxplot(y = data_values['Credit amount'], x = data_values['Purpose'], hue = data_values['Credit rating']).set_xticklabels(data_values['Purpose'].unique(), rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Credit Amount** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There is a slightly higher probability to get with a small credit amount a good credit rating\n",
    "        - The feature has explanatory value in interaction with other features\n",
    "        - Third highest positive correlation of all features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Savings account/bonds** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - Equal probabilities for a good and bad credit rating for 0 - 500 DM\n",
    "        - High probability for a good credit rating for unknown / no savings and >500 DM\n",
    "        "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Present employment since** <br>\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - Systematic patterns and differs with other features to more specific insights\n",
    "    - 1 - 4 years of employment has a high likelihood for good credit ratings\n",
    "    - We also see in the group of bad credit ratings that 1 - 4 years of employment have often 0 - 100 DM savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Florian Korn\\AppData\\Local\\Temp\\ipykernel_14916\\2296859800.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  sns.histplot(data=data_values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5,3))\n",
    "sns.histplot(data=data_values,\n",
    "            hue=\"Present employment since\",\n",
    "            hue_order = dict_ordinal_map[\"Present employment since\"],\n",
    "            stat='percent',\n",
    "            common_norm=False,\n",
    "            shrink=0.7,\n",
    "            discrete=True,\n",
    "            x='Savings account/bonds',\n",
    "            multiple='dodge').set_xticklabels(data_values['Savings account/bonds'].unique(), rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Installment rate in percentage of disposable income**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There is no pattern, good and bad credit ratings seem similar distributed\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Personal status and sex**\n",
    "    - **Don't include** this feature\n",
    "        - The category \"female : single\" is completly missing\n",
    "        - The category \"male : single\" is overly representated as good credit rating regarding the data (could bias the ML model)\n",
    "        - Categories seem differently in label consistency (male : divorced/seperated vs. female : divorced / separated / married)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Other debtors / guarantors**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There could be not enough explanatory value of this feature, as most data entries have no debtor / guarantors\n",
    "    - Contrary to our believes, it is more likely to get a good credit rating if we don't have debtors / guarantors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Present residence since**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There is no pattern, good and bad credit ratings seem similar distributed\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Property**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - Third lowest negative correlation with the target variable\n",
    "    - There is a higher probability to get a bad credit rating if the status is \"unknown / no property\"\n",
    "    - Car or other and building society / savings agreement / life insurance have the same probabilites for good or bad credit ratings"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Age in years**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - The distribution of the feature looks similar between good and bad credit rating\n",
    "    - There is a slightly higher probability for young people to get a bad credit rating, than a good one"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Other installment plans**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There is a high probability for a bad credit rating if there are other installment plans from stores or banks\n",
    "        - But no other installment plans are overly represented in the data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Housing**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - It has the secound lowest negative correlation with the target variable\n",
    "        - We see patterns, as you have a higher likelihood of getting a bad credit rating if you live for free or rent"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Number of existing credits at this bank**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - There is no pattern, good and bad credit ratings seem similar distributed\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Job**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - The likelihood for getting a good or bad credit rating is similar in each category\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Number of people being liable to provide maintenance for**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - The likelihood for getting a good or bad credit rating is similar in each category\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Telephone**\n",
    "    - **Include** this feature to train a model (and let the model decide)\n",
    "        - The likelihood for getting a good or bad credit rating is similar in each category\n",
    "    - We would rather not include this feature, but there could be interaction effects with other features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Foreign Worker**\n",
    "    - **Don't include** this feature\n",
    "        - Only a minority are not foreign workers\n",
    "        - There is a higher likelihhod for a good credit rating if it isn't a foreign worker\n",
    "        - We would bias here based on nationality / race, which shouldn't be done based on ethical reasoning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do feature selection based on analysis\n",
    "features_to_drop = ['foreign worker', 'Personal status and sex']\n",
    "\n",
    "# For the 1hot-encoded data\n",
    "for feature in features_to_drop:\n",
    "    if (feature in binary_attribs):\n",
    "        data_dummies.drop(feature, axis = 1, inplace=True)\n",
    "    else: data_dummies.drop([feature + '_' + column for column in pd.get_dummies(data_values[feature]).columns], axis=1, inplace=True)\n",
    "\n",
    "# For the encoded data\n",
    "data_list = [data_encoded, train_set, test_set, data_values, train_set_encoded]\n",
    "for df in data_list: df.drop(features_to_drop, axis = 1, inplace=True)\n",
    "\n",
    "# Recreate X_train, y_train, X_test, y_test\n",
    "X_train = np.array(train_set.iloc[:, :-1])\n",
    "y_train = np.array(train_set.iloc[:, -1])\n",
    "X_test = np.array(test_set.iloc[:, :-1])\n",
    "y_test = np.array(test_set.iloc[:, -1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Back to IV. Data Preparation](#data_prep)**<br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3. Feature Scaling** <a id='scale'></a> <br> \n",
    "Here the features are scaled with the StandardScaler of SciKit learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Status of existing checking account</th>\n",
       "      <th>Duration in month</th>\n",
       "      <th>Credit history</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Savings account/bonds</th>\n",
       "      <th>Present employment since</th>\n",
       "      <th>Installment rate in percentage of disposable income</th>\n",
       "      <th>Other debtors / guarantors</th>\n",
       "      <th>Present residence since</th>\n",
       "      <th>Property</th>\n",
       "      <th>Age in years</th>\n",
       "      <th>Other installment plans</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Number of existing credits at this bank</th>\n",
       "      <th>Job</th>\n",
       "      <th>Number of people being liable to provide maintenance for</th>\n",
       "      <th>Telephone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.054399</td>\n",
       "      <td>0.257194</td>\n",
       "      <td>-0.480888</td>\n",
       "      <td>-1.094744</td>\n",
       "      <td>-0.721798</td>\n",
       "      <td>-0.214436</td>\n",
       "      <td>-1.153383</td>\n",
       "      <td>0.918837</td>\n",
       "      <td>-0.318072</td>\n",
       "      <td>-0.785979</td>\n",
       "      <td>1.285768</td>\n",
       "      <td>-1.084571</td>\n",
       "      <td>0.987629</td>\n",
       "      <td>0.58595</td>\n",
       "      <td>-0.712435</td>\n",
       "      <td>-1.386847</td>\n",
       "      <td>-0.411814</td>\n",
       "      <td>0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003939</td>\n",
       "      <td>-0.743643</td>\n",
       "      <td>-0.480888</td>\n",
       "      <td>-1.094744</td>\n",
       "      <td>-0.846553</td>\n",
       "      <td>-1.241673</td>\n",
       "      <td>-0.321366</td>\n",
       "      <td>0.918837</td>\n",
       "      <td>-0.318072</td>\n",
       "      <td>-0.785979</td>\n",
       "      <td>-0.629715</td>\n",
       "      <td>-1.084571</td>\n",
       "      <td>-0.451539</td>\n",
       "      <td>0.58595</td>\n",
       "      <td>-0.712435</td>\n",
       "      <td>0.162703</td>\n",
       "      <td>-0.411814</td>\n",
       "      <td>0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.046520</td>\n",
       "      <td>-1.244061</td>\n",
       "      <td>-0.480888</td>\n",
       "      <td>-1.094744</td>\n",
       "      <td>-0.928761</td>\n",
       "      <td>-0.214436</td>\n",
       "      <td>-1.985400</td>\n",
       "      <td>-1.757388</td>\n",
       "      <td>-0.318072</td>\n",
       "      <td>1.033944</td>\n",
       "      <td>1.285768</td>\n",
       "      <td>1.618410</td>\n",
       "      <td>-0.451539</td>\n",
       "      <td>0.58595</td>\n",
       "      <td>-0.712435</td>\n",
       "      <td>-2.936397</td>\n",
       "      <td>-0.411814</td>\n",
       "      <td>-1.224745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Status of existing checking account  Duration in month  Credit history  \\\n",
       "0                             1.054399           0.257194       -0.480888   \n",
       "1                             0.003939          -0.743643       -0.480888   \n",
       "2                            -1.046520          -1.244061       -0.480888   \n",
       "\n",
       "    Purpose  Credit amount  Savings account/bonds  Present employment since  \\\n",
       "0 -1.094744      -0.721798              -0.214436                 -1.153383   \n",
       "1 -1.094744      -0.846553              -1.241673                 -0.321366   \n",
       "2 -1.094744      -0.928761              -0.214436                 -1.985400   \n",
       "\n",
       "   Installment rate in percentage of disposable income  \\\n",
       "0                                           0.918837     \n",
       "1                                           0.918837     \n",
       "2                                          -1.757388     \n",
       "\n",
       "   Other debtors / guarantors  Present residence since  Property  \\\n",
       "0                   -0.318072                -0.785979  1.285768   \n",
       "1                   -0.318072                -0.785979 -0.629715   \n",
       "2                   -0.318072                 1.033944  1.285768   \n",
       "\n",
       "   Age in years  Other installment plans  Housing  \\\n",
       "0     -1.084571                 0.987629  0.58595   \n",
       "1     -1.084571                -0.451539  0.58595   \n",
       "2      1.618410                -0.451539  0.58595   \n",
       "\n",
       "   Number of existing credits at this bank       Job  \\\n",
       "0                                -0.712435 -1.386847   \n",
       "1                                -0.712435  0.162703   \n",
       "2                                -0.712435 -2.936397   \n",
       "\n",
       "   Number of people being liable to provide maintenance for  Telephone  \n",
       "0                                          -0.411814          0.816497  \n",
       "1                                          -0.411814          0.816497  \n",
       "2                                          -0.411814         -1.224745  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This scaling is just for completness of the data preparation (the outcome dataframe isn't used afterwards as the scaling is done later in the pipelines)\n",
    "X_train_encoded = train_set_encoded[train_set_encoded.columns[:-1]]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaled_features = pd.DataFrame(scaler.fit_transform(X_train_encoded))\n",
    "scaled_features.columns = data_encoded.iloc[:,:-1].columns\n",
    "scaled_features.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Back to IV. Data Preparation](#data_prep)**<br>"
   ]
  }
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