1. Credit Risk Modeling.ipynb

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    "<a href=\"https://colab.research.google.com/github/danpele/2024-XAI-Intro/blob/main/1.%20Credit%20Risk%20Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# Standard libraries for data data handling and plotting\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BAD</th>\n",
       "      <th>LOAN</th>\n",
       "      <th>MORTDUE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>REASON</th>\n",
       "      <th>JOB</th>\n",
       "      <th>YOJ</th>\n",
       "      <th>DEROG</th>\n",
       "      <th>DELINQ</th>\n",
       "      <th>CLAGE</th>\n",
       "      <th>NINQ</th>\n",
       "      <th>CLNO</th>\n",
       "      <th>DEBTINC</th>\n",
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       "      <td>39025.0</td>\n",
       "      <td>HomeImp</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>208.692070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
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       "      BAD   LOAN  MORTDUE     VALUE   REASON     JOB   YOJ  DEROG  DELINQ  \\\n",
       "0       1   1100  25860.0   39025.0  HomeImp   Other  10.5    0.0     0.0   \n",
       "1       1   1300  70053.0   68400.0  HomeImp   Other   7.0    0.0     2.0   \n",
       "2       1   1500  13500.0   16700.0  HomeImp   Other   4.0    0.0     0.0   \n",
       "3       1   1500      NaN       NaN      NaN     NaN   NaN    NaN     NaN   \n",
       "4       0   1700  97800.0  112000.0  HomeImp  Office   3.0    0.0     0.0   \n",
       "...   ...    ...      ...       ...      ...     ...   ...    ...     ...   \n",
       "5955    0  88900  57264.0   90185.0  DebtCon   Other  16.0    0.0     0.0   \n",
       "5956    0  89000  54576.0   92937.0  DebtCon   Other  16.0    0.0     0.0   \n",
       "5957    0  89200  54045.0   92924.0  DebtCon   Other  15.0    0.0     0.0   \n",
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       "5959    0  89900  48811.0   88934.0  DebtCon   Other  15.0    0.0     0.0   \n",
       "\n",
       "           CLAGE  NINQ  CLNO    DEBTINC  \n",
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       "...          ...   ...   ...        ...  \n",
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       "\n",
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   "source": [
    "# Enter code to download the demo data set from the web \n",
    "data_url = \"https://raw.githubusercontent.com/danpele/2024-XAI-Intro/main/hmeq.csv\"\n",
    "df = pd.read_csv(data_url)\n",
    "\n",
    "# Taking a first glance at the data\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rqvOwIik6WIC"
   },
   "source": [
    "#### Introducing the HMEQ data set\n",
    "Our data set, called the  \"Home Equity\" or, in brief, HMEQ data set, is provided by www.creditriskanalytics.net. It comprises  information about a set of borrowers, which are categorized along demographic variables and variables concerning their business relationship with the lender. A binary target variable called 'BAD' is  provided and indicates whether a borrower has repaid her/his debt. You can think of the data as a standard use case of binary classification.\n",
    "\n",
    "You obtain the data, together with other interesting finance data sets, directly from www.creditriskanalytics.net. The website also provides a brief description of the data set. Specifically, the data set consists of 5,960 observations and 13 features including the target variable. The variables are defined as follows:\n",
    "\n",
    "- BAD: the target variable, 1=default; 0=non-default \n",
    "- LOAN: amount of the loan request\n",
    "- MORTDUE: amount due on an existing mortgage\n",
    "- VALUE: value of current property\n",
    "- REASON: DebtCon=debt consolidation; HomeImp=home improvement\n",
    "- JOB: occupational categories\n",
    "- YOJ: years at present job\n",
    "- DEROG: number of major derogatory reports\n",
    "- DELINQ: number of delinquent credit lines\n",
    "- CLAGE: age of oldest credit line in months\n",
    "- NINQ: number of recent credit inquiries\n",
    "- CLNO: number of credit lines\n",
    "- DEBTINC: debt-to-income ratio\n",
    "\n",
    "As you can see, the features aim at describing the financial situation of a borrower. We will keep using the data set for many modeling tasks in this demo notebook and future demo notebook. So it makes sense to familiarize yourself with the above features. Make sure you understand what type of information they provide and what this information might reveal about the risk of defaulting.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9kBNd8886D_o"
   },
   "source": [
    "## Eyeballing data using the Pandas library\n",
    "In this part, we briefly demonstrate some practices for obtaining a first intuition about a data set. Let's first identify some standard methods that the `Pandas` library provides for this purpose. This is not meant to give you a fully-comprehensive list. With that disclaimer, however, it is fair to say that you will apply all of the following methods to a new data frame.  \n",
    "- `info()` \n",
    "- `describe()`\n",
    "- `head()` / `tail()`\n",
    "\n",
    "Try them out and inspect the output. Briefly summarize your findings."
   ]
  },
  {
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   "execution_count": 34,
   "metadata": {
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1680268951372,
     "user": {
      "displayName": "Stefan Lessmann",
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    "id": "HXKMAQNV_y1Z",
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5960 entries, 0 to 5959\n",
      "Data columns (total 13 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   BAD      5960 non-null   int64  \n",
      " 1   LOAN     5960 non-null   int64  \n",
      " 2   MORTDUE  5442 non-null   float64\n",
      " 3   VALUE    5848 non-null   float64\n",
      " 4   REASON   5708 non-null   object \n",
      " 5   JOB      5681 non-null   object \n",
      " 6   YOJ      5445 non-null   float64\n",
      " 7   DEROG    5252 non-null   float64\n",
      " 8   DELINQ   5380 non-null   float64\n",
      " 9   CLAGE    5652 non-null   float64\n",
      " 10  NINQ     5450 non-null   float64\n",
      " 11  CLNO     5738 non-null   float64\n",
      " 12  DEBTINC  4693 non-null   float64\n",
      "dtypes: float64(9), int64(2), object(2)\n",
      "memory usage: 605.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Standard methods to exploring a DataFrame \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
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    "colab": {
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     "height": 300
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    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1680268951373,
     "user": {
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       "      <td>7.573982</td>\n",
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       "      <td>1.728675</td>\n",
       "      <td>10.138933</td>\n",
       "      <td>8.601746</td>\n",
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       "      <th>min</th>\n",
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       "      <td>1100.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>29.140031</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16300.000000</td>\n",
       "      <td>65019.000000</td>\n",
       "      <td>89235.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>23300.000000</td>\n",
       "      <td>91488.000000</td>\n",
       "      <td>119824.250000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>231.562278</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>39.003141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>89900.000000</td>\n",
       "      <td>399550.000000</td>\n",
       "      <td>855909.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1168.233561</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>203.312149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BAD          LOAN        MORTDUE          VALUE          YOJ  \\\n",
       "count  5960.000000   5960.000000    5442.000000    5848.000000  5445.000000   \n",
       "mean      0.199497  18607.969799   73760.817200  101776.048741     8.922268   \n",
       "std       0.399656  11207.480417   44457.609458   57385.775334     7.573982   \n",
       "min       0.000000   1100.000000    2063.000000    8000.000000     0.000000   \n",
       "25%       0.000000  11100.000000   46276.000000   66075.500000     3.000000   \n",
       "50%       0.000000  16300.000000   65019.000000   89235.500000     7.000000   \n",
       "75%       0.000000  23300.000000   91488.000000  119824.250000    13.000000   \n",
       "max       1.000000  89900.000000  399550.000000  855909.000000    41.000000   \n",
       "\n",
       "             DEROG       DELINQ        CLAGE         NINQ         CLNO  \\\n",
       "count  5252.000000  5380.000000  5652.000000  5450.000000  5738.000000   \n",
       "mean      0.254570     0.449442   179.766275     1.186055    21.296096   \n",
       "std       0.846047     1.127266    85.810092     1.728675    10.138933   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   115.116702     0.000000    15.000000   \n",
       "50%       0.000000     0.000000   173.466667     1.000000    20.000000   \n",
       "75%       0.000000     0.000000   231.562278     2.000000    26.000000   \n",
       "max      10.000000    15.000000  1168.233561    17.000000    71.000000   \n",
       "\n",
       "           DEBTINC  \n",
       "count  4693.000000  \n",
       "mean     33.779915  \n",
       "std       8.601746  \n",
       "min       0.524499  \n",
       "25%      29.140031  \n",
       "50%      34.818262  \n",
       "75%      39.003141  \n",
       "max     203.312149  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1680268951374,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "SppRdAqGuQtd",
    "outputId": "a8a9d776-5015-45f2-f04b-a854b00a92c0"
   },
   "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>BAD</th>\n",
       "      <th>LOAN</th>\n",
       "      <th>MORTDUE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>REASON</th>\n",
       "      <th>JOB</th>\n",
       "      <th>YOJ</th>\n",
       "      <th>DEROG</th>\n",
       "      <th>DELINQ</th>\n",
       "      <th>CLAGE</th>\n",
       "      <th>NINQ</th>\n",
       "      <th>CLNO</th>\n",
       "      <th>DEBTINC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>39025.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Other</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.366667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1300</td>\n",
       "      <td>70053.0</td>\n",
       "      <td>68400.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Other</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>121.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>16700.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Other</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.466667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1700</td>\n",
       "      <td>97800.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Office</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAD  LOAN  MORTDUE     VALUE   REASON     JOB   YOJ  DEROG  DELINQ  \\\n",
       "0    1  1100  25860.0   39025.0  HomeImp   Other  10.5    0.0     0.0   \n",
       "1    1  1300  70053.0   68400.0  HomeImp   Other   7.0    0.0     2.0   \n",
       "2    1  1500  13500.0   16700.0  HomeImp   Other   4.0    0.0     0.0   \n",
       "3    1  1500      NaN       NaN      NaN     NaN   NaN    NaN     NaN   \n",
       "4    0  1700  97800.0  112000.0  HomeImp  Office   3.0    0.0     0.0   \n",
       "\n",
       "        CLAGE  NINQ  CLNO  DEBTINC  \n",
       "0   94.366667   1.0   9.0      NaN  \n",
       "1  121.833333   0.0  14.0      NaN  \n",
       "2  149.466667   1.0  10.0      NaN  \n",
       "3         NaN   NaN   NaN      NaN  \n",
       "4   93.333333   0.0  14.0      NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXqdAZMZAKol"
   },
   "source": [
    "Creating outputs in the form of lists and tables is fine and can provide useful insights. However, you probably know the saying \"*a picture says more than a thousand words*\". Suitable graphics are often more insightful than (big) tables and can convey information in a more accessible way. Fortunately, `Pandas` offers many useful features to visualize the data within a DataFrame.\n",
    "\n",
    "Try  accomplish the following tasks. Web search using competendly selected search phrases will easily reveal the `Pandas` methods you need and how these are invoked. \n",
    "- Create a *histogram* for the the dependent (aka target) variable **BAD**\n",
    "- Create a density plot to depict the distribution of the variable **LOAN**\n",
    "- It is more common to visualize the distribution of numerical variables using *boxplots*. Considering again the variable, **LOAN** and depict its distribution using a *boxplot*\n",
    "- Create one last boxplot of the variable **LOAN**. This time, depict the distribution of loan amounts seperately for good and bad borrowers. To that end, use the target variable **BAD** and use it to add a grouping to your boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "executionInfo": {
     "elapsed": 960,
     "status": "ok",
     "timestamp": 1680268952320,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "hbaaz89TBCv7",
    "outputId": "284cca85-b83d-4729-9b50-6215a4631d99"
   },
   "outputs": [
    {
     "data": {
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Vy+VSQUGBva22tlaFhYXq16+fJCkxMVFBQUEeNaWlpdq7d69dk5ycLLfbre3bt9s127Ztk9vttmsAAIDZAr150a233qr7779fmZmZ+sUvfqG2bdt69eb/8i//omHDhikuLk5VVVXKy8vTX/7yF+Xn58vhcCgrK0vZ2dmKj49XfHy8srOzFRYWpvT0dEmS0+lUZmampk6dqqioKLVv317Tpk1T7969NWjQIElSz549NXToUI0dO1ZLliyRJI0bN06pqanXfIcZAAC4sXm1QvTJJ5/orrvu0tSpU+VyuTR+/HiPFZhrdfLkSWVkZKhHjx4aOHCgtm3bpvz8fA0ePFiSNH36dGVlZWnChAnq06ePvvrqK23YsEERERH2PhYuXKiRI0dq1KhRuvfeexUWFqb3339fAQEBds3q1avVu3dvpaSkKCUlRXfccYdWrVrlzaEDAIAb0HV9D9GFCxf0/vvva8WKFVq/fr3i4+OVmZmpjIwMdezYsSn79Du+h8gT30MEAGgNfPI9RIGBgXr44Yf1hz/8QfPmzdPhw4c1bdo0de7cWY899phKS0uvZ/cAAAA+cV2BaOfOnZowYYI6deqk3NxcTZs2TYcPH9aHH36or776Sj//+c+bqk8AAIBm49VF1bm5uVq+fLkOHjyo4cOH67XXXtPw4cPVps33+apr165asmSJbrvttiZtFgAAoDl4FYgWL16sJ554Qo8//rj9fUGXuummm7Rs2bLrag4AAMAXvApEhw4dumpNcHCwxowZ483uAQAAfMqra4iWL1+ut956q8H4W2+9pZUrV153UwAAAL7kVSCaO3euOnTo0GA8Ojpa2dnZ190UAACAL3kViI4ePaquXbs2GO/SpYuOHTt23U0BAAD4kleBKDo6Wp9++mmD8U8++URRUVHX3RQAAIAveRWIfvnLX2rSpEnauHGj6urqVFdXpw8//FCTJ0/WL3/5y6buEQAAoFl5dZfZnDlzdPToUQ0cOFCBgd/vor6+Xo899hjXEAEAgFbHq0AUHBysN998U//xH/+hTz75RKGhoerdu7e6dOnS1P0BAAA0O68C0UXdu3dX9+7dm6oXAAAAv/AqENXV1WnFihX685//rPLyctXX13ts//DDD5ukOQAAAF/wKhBNnjxZK1as0IgRI5SQkCCHw9HUfQEAAPiMV4EoLy9Pf/jDHzR8+PCm7gcAAMDnvLrtPjg4WLfeemtT9wIAAOAXXgWiqVOn6qWXXpJlWU3dDwAAgM959ZHZ5s2btXHjRq1fv1633367goKCPLa/8847TdIcAACAL3gViH7yk5/o4YcfbupeAAAA/MKrQLR8+fKm7gMAAMBvvLqGSJIuXLigDz74QEuWLFFVVZUk6cSJE6qurm6y5gAAAHzBqxWio0ePaujQoTp27Jhqamo0ePBgRUREaP78+fruu+/06quvNnWfAAAAzcarFaLJkyerT58+qqioUGhoqD3+8MMP689//nOTNQcAAOALXt9l9tFHHyk4ONhjvEuXLvrqq6+apDEAAABf8WqFqL6+XnV1dQ3GS0pKFBERcd1NAQAA+JJXgWjw4MF68cUX7ecOh0PV1dV6/vnn+TkPAADQ6nj1kdnChQv1wAMPqFevXvruu++Unp6uQ4cOqUOHDnrjjTeaukcAAIBm5VUgio2NVXFxsd544w19/PHHqq+vV2Zmph599FGPi6wBAABaA68CkSSFhobqiSee0BNPPNGU/QAAAPicV4Hotddeu+L2xx57zKtmAAAA/MGrQDR58mSP5+fPn9e3336r4OBghYWFEYgAAECr4tVdZhUVFR6P6upqHTx4UD/72c+4qBoAALQ6Xv+W2aXi4+M1d+7cBqtHAAAALV2TBSJJCggI0IkTJ5pylwAAAM3Oq2uI3nvvPY/nlmWptLRUixYt0r333tskjQEAAPiKV4Fo5MiRHs8dDoc6duyoBx98UC+88EJT9AUAAOAzXgWi+vr6pu4DAADAb5r0GiIAAIDWyKsVoilTplxzbW5urjdvAQAA4DNeBaLdu3fr448/1oULF9SjRw9J0ueff66AgADdfffddp3D4WiaLgEAAJqRV4HooYceUkREhFauXKl27dpJ+v7LGh9//HHdd999mjp1apM2CQAA0Jy8uobohRdeUE5Ojh2GJKldu3aaM2cOd5kBAIBWx6tAVFlZqZMnTzYYLy8vV1VV1XU3BQAA4EteBaKHH35Yjz/+uN5++22VlJSopKREb7/9tjIzM/XII480dY8AAADNyqtriF599VVNmzZNv/rVr3T+/PnvdxQYqMzMTC1YsKBJGwQAAGhuXgWisLAwvfLKK1qwYIEOHz4sy7J06623Kjw8vKn7AwAAaHbX9cWMpaWlKi0tVffu3RUeHi7LspqqLwAAAJ/xKhB9/fXXGjhwoLp3767hw4ertLRUkvTkk09yyz0AAGh1vApEv/nNbxQUFKRjx44pLCzMHh89erTy8/ObrDkAAABf8Ooaog0bNuhPf/qTOnfu7DEeHx+vo0ePNkljAAAAvuLVCtHZs2c9VoYuOn36tEJCQq67KQAAAF/yKhDdf//9eu211+znDodD9fX1WrBggR544IEmaw4AAMAXvPrIbMGCBRowYIB27typ2tpaTZ8+Xfv27dM333yjjz76qKl7BAAAaFZerRD16tVLn376qe655x4NHjxYZ8+e1SOPPKLdu3frlltuaeoeAQAAmlWjV4jOnz+vlJQULVmyRLNnz26OngAAAHyq0StEQUFB2rt3rxwOR3P0AwAA4HNefWT22GOPadmyZU3dCwAAgF94dVF1bW2tfv/736ugoEB9+vRp8Btmubm5TdIcAACALzQqEH3xxRe6+eabtXfvXt19992SpM8//9yjho/SAABAa9OoQBQfH6/S0lJt3LhR0vc/1fHyyy8rJiamWZoDAADwhUZdQ3Tpr9mvX79eZ8+ebdKGAAAAfM2ri6ovujQgNVZOTo7+4R/+QREREYqOjtbIkSN18ODBBu8xa9YsxcbGKjQ0VAMGDNC+ffs8ampqajRx4kR16NBB4eHhSktLU0lJiUdNRUWFMjIy5HQ65XQ6lZGRoTNnzlxX/wAA4MbQqEDkcDgaXCN0PdcMFRYW6umnn1ZRUZEKCgp04cIFpaSkeKw6zZ8/X7m5uVq0aJF27Nghl8ulwYMHq6qqyq7JysrS2rVrlZeXp82bN6u6ulqpqamqq6uza9LT01VcXKz8/Hzl5+eruLhYGRkZXvcOAABuHA6rEcs8bdq00bBhw+wfcH3//ff14IMPNrjL7J133vGqmVOnTik6OlqFhYW6//77ZVmWYmNjlZWVpWeffVbS96tBMTExmjdvnsaPHy+3262OHTtq1apVGj16tCTpxIkTiouL07p16zRkyBAdOHBAvXr1UlFRkZKSkiRJRUVFSk5O1meffaYePXpctbfKyko5nU653W5FRkZ6dXw/5uYZf2zS/fnCl3NH+LsFAACu6lrP341aIRozZoyio6Ptj51+9atfKTY21n5+8eEtt9stSWrfvr0k6ciRIyorK1NKSopdExISov79+2vLli2SpF27dtnfnn1RbGysEhIS7JqtW7fK6XTaYUiS+vbtK6fTadcAAABzNeous+XLlzdXH7IsS1OmTNHPfvYzJSQkSJLKysokqcFdbDExMTp69KhdExwcrHbt2jWoufj6srIyRUdHN3jP6Ohou+ZSNTU1qqmpsZ9XVlZ6eWQAAKClu66LqpvSM888o08//VRvvPFGg22XXqdkWdZVr126tOZy9VfaT05OjseqV1xc3LUcBgAAaIVaRCCaOHGi3nvvPW3cuFGdO3e2x10ulyQ1WMUpLy+3V41cLpdqa2tVUVFxxZqTJ082eN9Tp0796HcozZw5U263234cP37c+wMEAAAtml8DkWVZeuaZZ/TOO+/oww8/VNeuXT22d+3aVS6XSwUFBfZYbW2tCgsL1a9fP0lSYmKigoKCPGpKS0u1d+9euyY5OVlut1vbt2+3a7Zt2ya3223XXCokJESRkZEeDwAAcGPy6rfMmsrTTz+tNWvW6H/+538UERFhrwQ5nU6FhobK4XAoKytL2dnZio+PV3x8vLKzsxUWFqb09HS7NjMzU1OnTlVUVJTat2+vadOmqXfv3ho0aJAkqWfPnho6dKjGjh2rJUuWSJLGjRun1NTUa7rDDAAA3Nj8GogWL14sSRowYIDH+PLly/XrX/9akjR9+nSdO3dOEyZMUEVFhZKSkrRhwwZFRETY9QsXLlRgYKBGjRqlc+fOaeDAgVqxYoUCAgLsmtWrV2vSpEn23WhpaWlatGhR8x4gAABoFRr1PUQm43uIPPE9RACA1qBZvocIAADgRkQgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgvEB/NwAAAJrWzTP+6O8WGu3LuSP8+v6sEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxvNrIPrrX/+qhx56SLGxsXI4HHr33Xc9tluWpVmzZik2NlahoaEaMGCA9u3b51FTU1OjiRMnqkOHDgoPD1daWppKSko8aioqKpSRkSGn0ymn06mMjAydOXOmmY8OAAC0Fn4NRGfPntWdd96pRYsWXXb7/PnzlZubq0WLFmnHjh1yuVwaPHiwqqqq7JqsrCytXbtWeXl52rx5s6qrq5Wamqq6ujq7Jj09XcXFxcrPz1d+fr6Ki4uVkZHR7McHAABaB7/+dMewYcM0bNiwy26zLEsvvviinnvuOT3yyCOSpJUrVyomJkZr1qzR+PHj5Xa7tWzZMq1atUqDBg2SJL3++uuKi4vTBx98oCFDhujAgQPKz89XUVGRkpKSJElLly5VcnKyDh48qB49evjmYAEAQIvVYq8hOnLkiMrKypSSkmKPhYSEqH///tqyZYskadeuXTp//rxHTWxsrBISEuyarVu3yul02mFIkvr27Sun02nXXE5NTY0qKys9HgAA4MbUYgNRWVmZJCkmJsZjPCYmxt5WVlam4OBgtWvX7oo10dHRDfYfHR1t11xOTk6Ofc2R0+lUXFzcdR0PAABouVpsILrI4XB4PLcsq8HYpS6tuVz91fYzc+ZMud1u+3H8+PFGdg4AAFqLFhuIXC6XJDVYxSkvL7dXjVwul2pra1VRUXHFmpMnTzbY/6lTpxqsPv1QSEiIIiMjPR4AAODG1GIDUdeuXeVyuVRQUGCP1dbWqrCwUP369ZMkJSYmKigoyKOmtLRUe/futWuSk5Pldru1fft2u2bbtm1yu912DQAAMJtf7zKrrq7W//3f/9nPjxw5ouLiYrVv31433XSTsrKylJ2drfj4eMXHxys7O1thYWFKT0+XJDmdTmVmZmrq1KmKiopS+/btNW3aNPXu3du+66xnz54aOnSoxo4dqyVLlkiSxo0bp9TUVO4wAwAAkvwciHbu3KkHHnjAfj5lyhRJ0pgxY7RixQpNnz5d586d04QJE1RRUaGkpCRt2LBBERER9msWLlyowMBAjRo1SufOndPAgQO1YsUKBQQE2DWrV6/WpEmT7LvR0tLSfvS7jwAAgHkclmVZ/m6iNaisrJTT6ZTb7W7y64lunvHHJt2fL3w5d4S/WwAA/AjOK39zrefvFnsNEQAAgK8QiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMZ1QgeuWVV9S1a1e1bdtWiYmJ2rRpk79bAgAALYAxgejNN99UVlaWnnvuOe3evVv33Xefhg0bpmPHjvm7NQAA4GfGBKLc3FxlZmbqySefVM+ePfXiiy8qLi5Oixcv9ndrAADAzwL93YAv1NbWateuXZoxY4bHeEpKirZs2XLZ19TU1KimpsZ+7na7JUmVlZVN3l99zbdNvs/m1hzzAABoGpxXGu7Xsqwr1hkRiE6fPq26ujrFxMR4jMfExKisrOyyr8nJydHs2bMbjMfFxTVLj62N80V/dwAAuJE093mlqqpKTqfzR7cbEYgucjgcHs8ty2owdtHMmTM1ZcoU+3l9fb2++eYbRUVF/ehrvFFZWam4uDgdP35ckZGRTbZfNMRc+wbz7BvMs28wz77RnPNsWZaqqqoUGxt7xTojAlGHDh0UEBDQYDWovLy8warRRSEhIQoJCfEY+8lPftJcLSoyMpJ/2XyEufYN5tk3mGffYJ59o7nm+UorQxcZcVF1cHCwEhMTVVBQ4DFeUFCgfv36+akrAADQUhixQiRJU6ZMUUZGhvr06aPk5GT97ne/07Fjx/TUU0/5uzUAAOBnxgSi0aNH6+uvv9a///u/q7S0VAkJCVq3bp26dOni175CQkL0/PPPN/h4Dk2PufYN5tk3mGffYJ59oyXMs8O62n1oAAAANzgjriECAAC4EgIRAAAwHoEIAAAYj0AEAACMRyDygVdeeUVdu3ZV27ZtlZiYqE2bNl2xvrCwUImJiWrbtq26deumV1991Uedtm6Nmed33nlHgwcPVseOHRUZGank5GT96U9/8mG3rVdj/54v+uijjxQYGKi///u/b94GbyCNneuamho999xz6tKli0JCQnTLLbfov/7rv3zUbevV2HlevXq17rzzToWFhalTp056/PHH9fXXX/uo29bpr3/9qx566CHFxsbK4XDo3XffveprfH4utNCs8vLyrKCgIGvp0qXW/v37rcmTJ1vh4eHW0aNHL1v/xRdfWGFhYdbkyZOt/fv3W0uXLrWCgoKst99+28edty6NnefJkydb8+bNs7Zv3259/vnn1syZM62goCDr448/9nHnrUtj5/miM2fOWN26dbNSUlKsO++80zfNtnLezHVaWpqVlJRkFRQUWEeOHLG2bdtmffTRRz7suvVp7Dxv2rTJatOmjfXSSy9ZX3zxhbVp0ybr9ttvt0aOHOnjzluXdevWWc8995z13//935Yka+3atVes98e5kEDUzO655x7rqaee8hi77bbbrBkzZly2fvr06dZtt93mMTZ+/Hirb9++zdbjjaCx83w5vXr1smbPnt3Urd1QvJ3n0aNHW//6r/9qPf/88wSia9TYuV6/fr3ldDqtr7/+2hft3TAaO88LFiywunXr5jH28ssvW507d262Hm801xKI/HEu5COzZlRbW6tdu3YpJSXFYzwlJUVbtmy57Gu2bt3aoH7IkCHauXOnzp8/32y9tmbezPOl6uvrVVVVpfbt2zdHizcEb+d5+fLlOnz4sJ5//vnmbvGG4c1cv/fee+rTp4/mz5+vn/70p+revbumTZumc+fO+aLlVsmbee7Xr59KSkq0bt06WZalkydP6u2339aIESN80bIx/HEuNOabqv3h9OnTqqura/ADsjExMQ1+aPaisrKyy9ZfuHBBp0+fVqdOnZqt39bKm3m+1AsvvKCzZ89q1KhRzdHiDcGbeT506JBmzJihTZs2KTCQ/9xcK2/m+osvvtDmzZvVtm1brV27VqdPn9aECRP0zTffcB3Rj/Bmnvv166fVq1dr9OjR+u6773ThwgWlpaXpP//zP33RsjH8cS5khcgHHA6Hx3PLshqMXa3+cuPw1Nh5vuiNN97QrFmz9Oabbyo6Orq52rthXOs819XVKT09XbNnz1b37t191d4NpTF/0/X19XI4HFq9erXuueceDR8+XLm5uVqxYgWrRFfRmHnev3+/Jk2apH/7t3/Trl27lJ+fryNHjvC7mM3A1+dC/petGXXo0EEBAQEN/k+jvLy8QfK9yOVyXbY+MDBQUVFRzdZra+bNPF/05ptvKjMzU2+99ZYGDRrUnG22eo2d56qqKu3cuVO7d+/WM888I+n7k7ZlWQoMDNSGDRv04IMP+qT31sabv+lOnTrppz/9qZxOpz3Ws2dPWZalkpISxcfHN2vPrZE385yTk6N7771X//zP/yxJuuOOOxQeHq777rtPc+bMYRW/ifjjXMgKUTMKDg5WYmKiCgoKPMYLCgrUr1+/y74mOTm5Qf2GDRvUp08fBQUFNVuvrZk38yx9vzL061//WmvWrOHz/2vQ2HmOjIzUnj17VFxcbD+eeuop9ejRQ8XFxUpKSvJV662ON3/T9957r06cOKHq6mp77PPPP1ebNm3UuXPnZu23tfJmnr/99lu1aeN56gwICJD0txUMXD+/nAub7XJtWJb1t1s6ly1bZu3fv9/KysqywsPDrS+//NKyLMuaMWOGlZGRYddfvNXwN7/5jbV//35r2bJl3HZ/DRo7z2vWrLECAwOt3/72t1Zpaan9OHPmjL8OoVVo7DxfirvMrl1j57qqqsrq3Lmz9Ytf/MLat2+fVVhYaMXHx1tPPvmkvw6hVWjsPC9fvtwKDAy0XnnlFevw4cPW5s2brT59+lj33HOPvw6hVaiqqrJ2795t7d6925Jk5ebmWrt377a/3qAlnAsJRD7w29/+1urSpYsVHBxs3X333VZhYaG9bcyYMVb//v096v/yl79Yd911lxUcHGzdfPPN1uLFi33ccevUmHnu37+/JanBY8yYMb5vvJVp7N/zDxGIGqexc33gwAFr0KBBVmhoqNW5c2drypQp1rfffuvjrlufxs7zyy+/bPXq1csKDQ21OnXqZD366KNWSUmJj7tuXTZu3HjF/+a2hHOhw7JY4wMAAGbjGiIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjPf/mjQVRcERRa8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram for the the dependent (aka target) variable BAD\n",
    "df[\"BAD\"].plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "executionInfo": {
     "elapsed": 418,
     "status": "ok",
     "timestamp": 1680268952731,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "1T6u04_kBXS2",
    "outputId": "e8add1f5-00fb-4d3b-c0ca-2174e4ac724d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a density plot to depict the distribution of the variable LOAN\n",
    "df['LOAN'].plot.density();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1680268952732,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "2YTuJy_wBXVu",
    "outputId": "e377608f-dfa5-4651-fab7-41ba4bad49ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot to depict the distribution of the variable LOAN\n",
    "df['LOAN'].plot.box();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "executionInfo": {
     "elapsed": 410,
     "status": "ok",
     "timestamp": 1680268953132,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "trsGDeANBXXf",
    "outputId": "2136095d-0678-4952-d1f4-87fd5fa91d18"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot of the variable LOAN while grouping by the target\n",
    "df.plot.box(by=\"BAD\", column=\"LOAN\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9qRx_48h-Ydc"
   },
   "source": [
    "## Perform basic data preprocessing\n",
    "Data preparation is a topic of great importance. We could easily devote an entire lecture to this topic. Below, we sketch only a tiny little bit of the tasks typically performed in the scope of data preparation. \n",
    "\n",
    "In fact, we could not proceed with the data in its present form. For example, going back to the tabular preview of our data, you might notice that several variables show some missing values (denoted as NaN for *not a number* in the preview*). Leaving those missing values w/o treatment would break our neural network.\n",
    "\n",
    "Similarly, the data preview highlights that different variables show substantialyl different value ranges. Simply compare the values (or descriptive statistics also shown above) of the variables **LOAN** to those of **VALUE** to see this. For example, the maximal value of the variable **LOAN** is 89.900,00 whreas the largest value for the variable **VALUE** is 855.909,00 so almost ten times bigger. Differences in the value range of variables are normal but, unless suitably treated in data prepration, will also harm our neural network. \n",
    "\n",
    "Last, the data set includes some categorical variables. These would also need special treatment, which we do not have the time to detail. Thus, we simply delete them.\n",
    "\n",
    "In sum, we need to accomplish the following tasks:\n",
    "- Deleting the categorical variables **REASON** and **JOB**\n",
    "- Replacing all missing values in the data using *mean imputation*\n",
    "- Transforming the value range of all variables such that values are scaled between zero and one.\n",
    "\n",
    "Let's go..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 235,
     "status": "ok",
     "timestamp": 1680268953364,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
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     "user_tz": -120
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    "id": "FbWoD2YsBXZf",
    "outputId": "ad1f9b2d-50c4-4b69-f78c-6b8662a2cc91"
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5960 entries, 0 to 5959\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   BAD      5960 non-null   int64  \n",
      " 1   LOAN     5960 non-null   int64  \n",
      " 2   MORTDUE  5442 non-null   float64\n",
      " 3   VALUE    5848 non-null   float64\n",
      " 4   YOJ      5445 non-null   float64\n",
      " 5   DEROG    5252 non-null   float64\n",
      " 6   DELINQ   5380 non-null   float64\n",
      " 7   CLAGE    5652 non-null   float64\n",
      " 8   NINQ     5450 non-null   float64\n",
      " 9   CLNO     5738 non-null   float64\n",
      " 10  DEBTINC  4693 non-null   float64\n",
      "dtypes: float64(9), int64(2)\n",
      "memory usage: 512.3 KB\n"
     ]
    }
   ],
   "source": [
    "# Delete selected columns\n",
    "df = df.drop(columns=[\"REASON\", \"JOB\"], axis=1)\n",
    "df.info() # verify the columns are no longer part of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
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    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1680268953365,
     "user": {
      "displayName": "Stefan Lessmann",
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    "id": "ZWOTgAr7H2xK",
    "outputId": "45b84e2d-b70f-4fda-9764-8969e2a1b143"
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       "      BAD   LOAN     MORTDUE          VALUE        YOJ    DEROG    DELINQ  \\\n",
       "0       1   1100  25860.0000   39025.000000  10.500000  0.00000  0.000000   \n",
       "1       1   1300  70053.0000   68400.000000   7.000000  0.00000  2.000000   \n",
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       "4       0   1700  97800.0000  112000.000000   3.000000  0.00000  0.000000   \n",
       "...   ...    ...         ...            ...        ...      ...       ...   \n",
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       "5959    0  89900  48811.0000   88934.000000  15.000000  0.00000  0.000000   \n",
       "\n",
       "           CLAGE      NINQ       CLNO    DEBTINC  \n",
       "0      94.366667  1.000000   9.000000  33.779915  \n",
       "1     121.833333  0.000000  14.000000  33.779915  \n",
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       "\n",
       "[5960 rows x 11 columns]"
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     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values in a column with the mean of that column \n",
    "df = df.fillna(value=df.mean())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
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     "status": "ok",
     "timestamp": 1680268954173,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
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     "user_tz": -120
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    "id": "emd4Y6srJdIE",
    "outputId": "14942a20-6eae-4548-a27e-30932b4c18cc"
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Assuming `df` is your DataFrame and 'BAD' is the target variable column\n",
    "y = df['BAD']\n",
    "\n",
    "# Scale value ranges of variables to zero and one\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "# Scale the remaining columns\n",
    "df_ready = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)\n",
    "\n",
    "# Verify our transformed data shows the same value ranges for all columns\n",
    "X = df_ready.drop(columns=['BAD'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hxYbZz2c-eIB"
   },
   "source": [
    "\n",
    "## Partition the data into training a testing data\n",
    "Data partitioning is standard practice to ensure we use disjoint data for training and evaluating a machine learning model. \n",
    "\n",
    "the `sklearn` library provides many methods to partition data for standard machine learning workflows. Arguably, the easiest approach is to use the method `train_test_split()`. Check out its documentation and partition our *prepared* data. Let's say we use 30% of the data for testing and the rest for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 977,
     "status": "ok",
     "timestamp": 1680273999025,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "HuGa1nKXLUBx",
    "outputId": "83f3f7e3-5358-411e-ccf5-c6403e58f1d0"
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       "      <td>0.076509</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.450704</td>\n",
       "      <td>0.194571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>0.337838</td>\n",
       "      <td>0.179435</td>\n",
       "      <td>0.118956</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.025457</td>\n",
       "      <td>0.029963</td>\n",
       "      <td>0.102325</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.208603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.085586</td>\n",
       "      <td>0.190867</td>\n",
       "      <td>0.085108</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300050</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.661972</td>\n",
       "      <td>0.143687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4768 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          LOAN   MORTDUE     VALUE       YOJ     DEROG    DELINQ     CLAGE  \\\n",
       "944   0.090090  0.153245  0.082354  0.000000  0.000000  0.133333  0.112301   \n",
       "5755  0.480856  0.009613  0.067094  0.217616  0.000000  0.000000  0.265210   \n",
       "2259  0.143018  0.183853  0.103392  0.463415  0.000000  0.000000  0.197270   \n",
       "12    0.010135  0.108021  0.055430  0.073171  0.000000  0.000000  0.073672   \n",
       "1117  0.099099  0.113976  0.126001  0.097561  0.000000  0.000000  0.180131   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3772  0.212838  0.244629  0.154675  0.243902  0.000000  0.000000  0.138985   \n",
       "5191  0.302928  0.120006  0.078459  0.585366  0.025457  0.066667  0.252171   \n",
       "5226  0.306306  0.175880  0.131174  0.097561  0.000000  0.000000  0.076509   \n",
       "5390  0.337838  0.179435  0.118956  0.121951  0.025457  0.029963  0.102325   \n",
       "860   0.085586  0.190867  0.085108  0.487805  0.000000  0.000000  0.300050   \n",
       "\n",
       "          NINQ      CLNO   DEBTINC  \n",
       "944   0.000000  0.323944  0.126072  \n",
       "5755  0.000000  0.267606  0.077105  \n",
       "2259  0.588235  0.338028  0.162036  \n",
       "12    0.117647  0.352113  0.163991  \n",
       "1117  0.000000  0.140845  0.120903  \n",
       "...        ...       ...       ...  \n",
       "3772  0.058824  0.323944  0.163991  \n",
       "5191  0.058824  0.422535  0.182885  \n",
       "5226  0.117647  0.450704  0.194571  \n",
       "5390  0.588235  0.295775  0.208603  \n",
       "860   0.058824  0.661972  0.143687  \n",
       "\n",
       "[4768 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q8AyLv82Okw1"
   },
   "source": [
    "## Train and assess an XGB classifier\n",
    " - Import relevant libraries to train an XGB classifier\n",
    " - Train our XGB classifier using the training data\n",
    " - Compute XGB predictions for the test data\n",
    " - Compute a performance indicator over the test set predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1717,
     "status": "ok",
     "timestamp": 1680274016012,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "idSazLynPXfd",
    "outputId": "82ba1fec-c892-4858-a5a3-64aca6aace78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.93\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Train an XGBoost model\n",
    "model = xgb.XGBClassifier(eval_metric='logloss')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 929,
     "status": "ok",
     "timestamp": 1680274018079,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "sY3D0b6IQlXi",
    "outputId": "0b68cd8e-f905-44e4-ec38-8a9e09b169f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[907,  20],\n",
       "       [ 64, 201]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A more informative way to assess the NN involves creating a cross-table of\n",
    "# its predictions and the true classes of test set observations.\n",
    "# An easy - quick and dirty - way to do this is as follows\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_true=y_test, y_pred=yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "executionInfo": {
     "elapsed": 656,
     "status": "ok",
     "timestamp": 1680274021743,
     "user": {
      "displayName": "Stefan Lessmann",
      "userId": "06342662613942148717"
     },
     "user_tz": -120
    },
    "id": "M0oGw4P1Q6cC",
    "outputId": "5c9e14e3-7f7a-4598-b792-510179ad3682"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A nice visualization of the so-called confusion matrix can be produced as follows\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(y_true=y_test, y_pred=yhat, labels=[0, 1], display_labels=['Good(0)', 'Bad(1)']);"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}