Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Business Problem\n",
    "\n",
    "Our client is a credit card company. \n",
    "\n",
    "**Goal: Develop a predictive model for whether an account will default next month, given demographics and historical variables (explanatory variables) for each observation account ID.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset \n",
    "\n",
    "- Collected in 2005 within April-August.\n",
    "\n",
    "\n",
    "- **Variables/Columns (25):** demographic and recent (past 6 months: August to April) financial variables for each credit account ID.\n",
    "\n",
    "\n",
    "- **Observation/Rows (30,000):** credit account IDS.\n",
    "\n",
    "\n",
    "Rows are labeled (i.e. by the \"default payment next month\" column) by whether, in the next month after the 6-month historical data period, an account owner has defaulted (i.e. failed to make the minimum payment) --> **object/test variable/response variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>798fc410-45c1</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a8c8f3b-8eb4</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85698822-43f5</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0737c11b-be42</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3b7f77cc-dbc0</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "0  798fc410-45c1      20000    2          2         1   24     2      2   \n",
       "1  8a8c8f3b-8eb4     120000    2          2         2   26    -1      2   \n",
       "2  85698822-43f5      90000    2          2         2   34     0      0   \n",
       "3  0737c11b-be42      50000    2          2         1   37     0      0   \n",
       "4  3b7f77cc-dbc0      50000    1          2         1   57    -1      0   \n",
       "\n",
       "   PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0     -1     -1  ...          0          0          0         0       689   \n",
       "1      0      0  ...       3272       3455       3261         0      1000   \n",
       "2      0      0  ...      14331      14948      15549      1518      1500   \n",
       "3      0      0  ...      28314      28959      29547      2000      2019   \n",
       "4     -1      0  ...      20940      19146      19131      2000     36681   \n",
       "\n",
       "   PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0         0                           1  \n",
       "1      1000      1000         0      2000                           1  \n",
       "2      1000      1000      1000      5000                           0  \n",
       "3      1200      1100      1069      1000                           0  \n",
       "4     10000      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel(\"Data/CreditCards.xls\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data dictionary \n",
    "\n",
    "### Variables overview\n",
    "\n",
    "\n",
    "### Indexing:\n",
    "\n",
    "\n",
    "- **ID:** credit account IDS.\n",
    "\n",
    "\n",
    "### Explanatory variables:\n",
    "\n",
    "\n",
    "- **LIMIT_BAL:** Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.\n",
    "\n",
    "\n",
    "- **SEX:** (1 = male; 2 = female).\n",
    "\n",
    "\n",
    "- **EDUCATION:** (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
    "\n",
    "\n",
    "- **MARRIAGE:** (1 = married; 2 = single; 3 = others).\n",
    "\n",
    "\n",
    "- **AGE:** (year).\n",
    "\n",
    "\n",
    "- **PAY_1-PAY_6:** History of the past monthly repayment(Rimborsi, quanto devi rimborsare) (PAY_1 = repayment status in September, PAY_2 = ... August, PAY_3 = ... July, Pay_4 = ... June, Pay_5 = ... May, PAY_6 = ... April). Measurement scale for each the repayment status (PAY_N): (-1 = pay duly(i.e. pay rigth); 1 = payment delay for 1 month; 2 = ... 2 months; up to 9 = payment delay for 9 months and above.).\n",
    "\n",
    "\n",
    "- **BILL_AMT1–BILL_AMT6:** Monthly bill statement amount(Estratto conto, capitale utile mensile ) (BILL_AMT1 = Bill statement amount in September, BILL_AMT2 = ... August, BILL_AMT3 = ... July, BILL_AMT4 = ... June, BILL_AMT5 = ... May, BILL_AMT6 = ... April). Measurement scale for each bill statement amount (BILL_AMTN) : (NT dollar).\n",
    "\n",
    "\n",
    "- **PAY_AMT1–PAY_AMT6:** Monthly previous payment amount (PAY_AMT1 = Bill statement amount in September, PAY_AMT2 = ... August, PAY_AMT3 = ... July, PAY_AMT4 = ... June, PAY_AMT5 = ... May, PAY_AMT6 = ... April). Measurement scale for each previous payment amount (PAY_AMTN) : (NT dollar).\n",
    "\n",
    "\n",
    "### Response variable:\n",
    "\n",
    "\n",
    "- **default payment next month:** (0 = No default, 1 = default).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal\n",
    "\n",
    "**Develop a predictive model for the response variable default payment next month for the credit account IDs given the explanatory variables.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note \n",
    "\n",
    "Due to the relative small number of variables we can afford them individually without dimensionality reduction techniques or methods of feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA (Exploratory Data Analysis) and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype \n",
      "---  ------                      --------------  ----- \n",
      " 0   ID                          30000 non-null  object\n",
      " 1   LIMIT_BAL                   30000 non-null  int64 \n",
      " 2   SEX                         30000 non-null  int64 \n",
      " 3   EDUCATION                   30000 non-null  int64 \n",
      " 4   MARRIAGE                    30000 non-null  int64 \n",
      " 5   AGE                         30000 non-null  int64 \n",
      " 6   PAY_1                       30000 non-null  object\n",
      " 7   PAY_2                       30000 non-null  int64 \n",
      " 8   PAY_3                       30000 non-null  int64 \n",
      " 9   PAY_4                       30000 non-null  int64 \n",
      " 10  PAY_5                       30000 non-null  int64 \n",
      " 11  PAY_6                       30000 non-null  int64 \n",
      " 12  BILL_AMT1                   30000 non-null  int64 \n",
      " 13  BILL_AMT2                   30000 non-null  int64 \n",
      " 14  BILL_AMT3                   30000 non-null  int64 \n",
      " 15  BILL_AMT4                   30000 non-null  int64 \n",
      " 16  BILL_AMT5                   30000 non-null  int64 \n",
      " 17  BILL_AMT6                   30000 non-null  int64 \n",
      " 18  PAY_AMT1                    30000 non-null  int64 \n",
      " 19  PAY_AMT2                    30000 non-null  int64 \n",
      " 20  PAY_AMT3                    30000 non-null  int64 \n",
      " 21  PAY_AMT4                    30000 non-null  int64 \n",
      " 22  PAY_AMT5                    30000 non-null  int64 \n",
      " 23  PAY_AMT6                    30000 non-null  int64 \n",
      " 24  default payment next month  30000 non-null  int64 \n",
      "dtypes: int64(23), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29687"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ID\"].nunique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ad23fe5c-7b09    2\n",
       "1fb3e3e6-a68d    2\n",
       "89f8f447-fca8    2\n",
       "7c9b7473-cc2f    2\n",
       "90330d02-82d9    2\n",
       "                ..\n",
       "2edeb3a6-d38d    1\n",
       "27e11893-92e9    1\n",
       "2b1c282b-441e    1\n",
       "bd737997-0eb0    1\n",
       "15d69f9f-5ad3    1\n",
       "Name: ID, Length: 29687, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_counts = df[\"ID\"].value_counts()\n",
    "id_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    29374\n",
       "2      313\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_counts.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "313 duplicates of size 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ad23fe5c-7b09    True\n",
      "1fb3e3e6-a68d    True\n",
      "89f8f447-fca8    True\n",
      "7c9b7473-cc2f    True\n",
      "90330d02-82d9    True\n",
      "Name: ID, dtype: bool\n",
      "ad23fe5c-7b09    2\n",
      "1fb3e3e6-a68d    2\n",
      "89f8f447-fca8    2\n",
      "7c9b7473-cc2f    2\n",
      "90330d02-82d9    2\n",
      "Name: ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "dupe_mask = id_counts == 2  # Boolean mask for identify the duplicates of size 2\n",
    "print(dupe_mask[0:5])\n",
    "print(id_counts[0:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313\n",
      "['ad23fe5c-7b09', '1fb3e3e6-a68d', '89f8f447-fca8', '7c9b7473-cc2f', '90330d02-82d9']\n"
     ]
    }
   ],
   "source": [
    "dupe_ids = id_counts.index[dupe_mask]  # Storing duplicate indexes\n",
    "dupe_ids = list(dupe_ids)\n",
    "\n",
    "print(len(dupe_ids))\n",
    "print(dupe_ids[0:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>89f8f447-fca8</td>\n",
       "      <td>320000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>169371</td>\n",
       "      <td>172868</td>\n",
       "      <td>150827</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>5500</td>\n",
       "      <td>6100</td>\n",
       "      <td>6000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5133</th>\n",
       "      <td>89f8f447-fca8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16727</th>\n",
       "      <td>1fb3e3e6-a68d</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27394</td>\n",
       "      <td>29922</td>\n",
       "      <td>31879</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16827</th>\n",
       "      <td>1fb3e3e6-a68d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29685</th>\n",
       "      <td>ad23fe5c-7b09</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12882</td>\n",
       "      <td>8131</td>\n",
       "      <td>3983</td>\n",
       "      <td>3000</td>\n",
       "      <td>2871</td>\n",
       "      <td>1000</td>\n",
       "      <td>163</td>\n",
       "      <td>3983</td>\n",
       "      <td>3771</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29785</th>\n",
       "      <td>ad23fe5c-7b09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "5033   89f8f447-fca8     320000    2          2         1   32     0      0   \n",
       "5133   89f8f447-fca8          0    0          0         0    0     0      0   \n",
       "16727  1fb3e3e6-a68d      80000    1          2         2   33     2      2   \n",
       "16827  1fb3e3e6-a68d          0    0          0         0    0     0      0   \n",
       "29685  ad23fe5c-7b09      50000    1          3         1   32     0      0   \n",
       "29785  ad23fe5c-7b09          0    0          0         0    0     0      0   \n",
       "\n",
       "       PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "5033       0      0  ...     169371     172868     150827      8000      8000   \n",
       "5133       0      0  ...          0          0          0         0         0   \n",
       "16727      0      0  ...      27394      29922      31879         0      2000   \n",
       "16827      0      0  ...          0          0          0         0         0   \n",
       "29685      0      0  ...      12882       8131       3983      3000      2871   \n",
       "29785      0      0  ...          0          0          0         0         0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "5033       5500      6100      6000      5000                           0  \n",
       "5133          0         0         0         0                           0  \n",
       "16727      2000      3000      2600         0                           1  \n",
       "16827         0         0         0         0                           0  \n",
       "29685      1000       163      3983      3771                           1  \n",
       "29785         0         0         0         0                           0  \n",
       "\n",
       "[6 rows x 25 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\n",
    "    df[\"ID\"].isin(dupe_ids[0:3]), :\n",
    "]  # Masking with respect the first 3 duplicate IDs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our duplicates seems observations with all 0 for the variables.\n",
    "\n",
    "\n",
    "\"0 rows\" are also invalid data since makes no sense LIMIT_BAL = 0,  AGE = 0, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
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       "      <th>PAY_AMT5</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL    SEX  EDUCATION  MARRIAGE    AGE  PAY_1  PAY_2  \\\n",
       "0      False      False  False      False     False  False  False  False   \n",
       "1      False      False  False      False     False  False  False  False   \n",
       "2      False      False  False      False     False  False   True   True   \n",
       "3      False      False  False      False     False  False   True   True   \n",
       "4      False      False  False      False     False  False  False   True   \n",
       "...      ...        ...    ...        ...       ...    ...    ...    ...   \n",
       "29995  False      False  False      False     False  False   True   True   \n",
       "29996  False      False  False      False     False  False  False  False   \n",
       "29997  False      False  False      False     False  False  False  False   \n",
       "29998  False      False  False      False     False  False  False  False   \n",
       "29999  False      False  False      False     False  False   True   True   \n",
       "\n",
       "       PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0      False  False  ...       True       True       True      True     False   \n",
       "1       True   True  ...      False      False      False      True     False   \n",
       "2       True   True  ...      False      False      False     False     False   \n",
       "3       True   True  ...      False      False      False     False     False   \n",
       "4      False   True  ...      False      False      False     False     False   \n",
       "...      ...    ...  ...        ...        ...        ...       ...       ...   \n",
       "29995   True   True  ...      False      False      False     False     False   \n",
       "29996  False  False  ...      False      False       True     False     False   \n",
       "29997  False  False  ...      False      False      False      True      True   \n",
       "29998   True   True  ...      False      False      False     False     False   \n",
       "29999   True   True  ...      False      False      False     False     False   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0          True      True      True      True                       False  \n",
       "1         False     False      True     False                       False  \n",
       "2         False     False     False     False                        True  \n",
       "3         False     False     False     False                        True  \n",
       "4         False     False     False     False                        True  \n",
       "...         ...       ...       ...       ...                         ...  \n",
       "29995     False     False     False     False                        True  \n",
       "29996     False     False      True      True                        True  \n",
       "29997     False     False     False     False                       False  \n",
       "29998     False     False     False     False                       False  \n",
       "29999     False     False     False     False                       False  \n",
       "\n",
       "[30000 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero_mask = df == 0  # Boolean mask for 0 rows.\n",
    "df_zero_mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
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       "4        False\n",
       "         ...  \n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "feature_zero_mask = df_zero_mask.iloc[:, 1:].all(\n",
    "    axis=1\n",
    ")  # For single output for each rows\n",
    "feature_zero_mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(feature_zero_mask)  # Boolean sums are equal to the sum of Trues\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "315 0 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29685, 25)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1 = df.loc[~feature_zero_mask, :].copy()\n",
    "df_clean_1.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "29685 valid IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_1.to_csv(\"Data/df_clean_1.csv\", index=False)  # Saving\n",
    "\n",
    "# Check point\n",
    "\n",
    "# ID -> removal of duplicate and invalid IDs (observations)\n",
    "# LIMIT_BAL\n",
    "# SEX\n",
    "# EDUCATION\n",
    "# MARRIAGE\n",
    "# AGE\n",
    "# PAY_1-PAY_6\n",
    "# BILL_AMT1-BILL_AMT6\n",
    "# PAY_AMT1-PAY_AMT6\n",
    "# default payment next month\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PAY_1-PAY_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 29685 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype \n",
      "---  ------                      --------------  ----- \n",
      " 0   ID                          29685 non-null  object\n",
      " 1   LIMIT_BAL                   29685 non-null  int64 \n",
      " 2   SEX                         29685 non-null  int64 \n",
      " 3   EDUCATION                   29685 non-null  int64 \n",
      " 4   MARRIAGE                    29685 non-null  int64 \n",
      " 5   AGE                         29685 non-null  int64 \n",
      " 6   PAY_1                       29685 non-null  object\n",
      " 7   PAY_2                       29685 non-null  int64 \n",
      " 8   PAY_3                       29685 non-null  int64 \n",
      " 9   PAY_4                       29685 non-null  int64 \n",
      " 10  PAY_5                       29685 non-null  int64 \n",
      " 11  PAY_6                       29685 non-null  int64 \n",
      " 12  BILL_AMT1                   29685 non-null  int64 \n",
      " 13  BILL_AMT2                   29685 non-null  int64 \n",
      " 14  BILL_AMT3                   29685 non-null  int64 \n",
      " 15  BILL_AMT4                   29685 non-null  int64 \n",
      " 16  BILL_AMT5                   29685 non-null  int64 \n",
      " 17  BILL_AMT6                   29685 non-null  int64 \n",
      " 18  PAY_AMT1                    29685 non-null  int64 \n",
      " 19  PAY_AMT2                    29685 non-null  int64 \n",
      " 20  PAY_AMT3                    29685 non-null  int64 \n",
      " 21  PAY_AMT4                    29685 non-null  int64 \n",
      " 22  PAY_AMT5                    29685 non-null  int64 \n",
      " 23  PAY_AMT6                    29685 non-null  int64 \n",
      " 24  default payment next month  29685 non-null  int64 \n",
      "dtypes: int64(23), object(2)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean_1.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                13087\n",
       "-1                5047\n",
       "1                 3261\n",
       "Not available     3021\n",
       "-2                2476\n",
       "2                 2378\n",
       "3                  292\n",
       "4                   63\n",
       "5                   23\n",
       "8                   17\n",
       "6                   11\n",
       "7                    9\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1[\"PAY_1\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PAY_1 'Not available' value (for missing) drive the column to be an object type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "         ... \n",
       "29995    True\n",
       "29996    True\n",
       "29997    True\n",
       "29998    True\n",
       "29999    True\n",
       "Name: PAY_1, Length: 29685, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For now I decide to remove this missing type.\n",
    "valid_pay_1_mask = df_clean_1[\"PAY_1\"] != \"Not available\"\n",
    "valid_pay_1_mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26664"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(valid_pay_1_mask)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "26664 valid rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    13087\n",
       "-1     5047\n",
       " 1     3261\n",
       "-2     2476\n",
       " 2     2378\n",
       " 3      292\n",
       " 4       63\n",
       " 5       23\n",
       " 8       17\n",
       " 6       11\n",
       " 7        9\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2 = df_clean_1.loc[valid_pay_1_mask, :].copy()\n",
    "df_clean_2[\"PAY_1\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26664 entries, 0 to 29999\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   PAY_1   26664 non-null  int64\n",
      " 1   PAY_2   26664 non-null  int64\n",
      " 2   PAY_3   26664 non-null  int64\n",
      " 3   PAY_4   26664 non-null  int64\n",
      " 4   PAY_5   26664 non-null  int64\n",
      " 5   PAY_6   26664 non-null  int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Now that the features are consistent int64 type.\n",
    "df_clean_2[\"PAY_1\"] = df_clean_2[\"PAY_1\"].astype(\"int64\")\n",
    "df_clean_2[[\"PAY_1\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    13087\n",
       "-1     5047\n",
       " 1     3261\n",
       "-2     2476\n",
       " 2     2378\n",
       " 3      292\n",
       " 4       63\n",
       " 5       23\n",
       " 8       17\n",
       " 6       11\n",
       " 7        9\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"PAY_1\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Undocumented values of –2 and 0 in PAY_1.\n",
    "\n",
    "Learned from the company:\n",
    "\n",
    "- -2 means the account started that month with a zero balance and never used\n",
    "any credit.\n",
    "\n",
    "- 0 means that at least the minimum payment was made, but the entire balance\n",
    "wasn't paid (that is, a positive balance was carried to the next month)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.017777</td>\n",
       "      <td>-0.133363</td>\n",
       "      <td>-0.167679</td>\n",
       "      <td>-0.225023</td>\n",
       "      <td>-0.269764</td>\n",
       "      <td>-0.293579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.126769</td>\n",
       "      <td>1.198640</td>\n",
       "      <td>1.199165</td>\n",
       "      <td>1.167897</td>\n",
       "      <td>1.131735</td>\n",
       "      <td>1.150229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PAY_1         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  26664.000000  26664.000000  26664.000000  26664.000000  26664.000000   \n",
       "mean      -0.017777     -0.133363     -0.167679     -0.225023     -0.269764   \n",
       "std        1.126769      1.198640      1.199165      1.167897      1.131735   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "              PAY_6  \n",
       "count  26664.000000  \n",
       "mean      -0.293579  \n",
       "std        1.150229  \n",
       "min       -2.000000  \n",
       "25%       -1.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        8.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_feats = ['PAY_1', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
    "df_clean_2[pay_feats].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'9 = payment delay for nine months and above' seems to not never appear (max = 8 for each PAY_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # plotting package\n",
    "# Plot direct under the cells\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl # additional plotting functionality\n",
    "mpl.rcParams['figure.dpi'] = 400 # high resolution figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'PAY_1'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_2'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_3'}>],\n",
       "       [<AxesSubplot:title={'center':'PAY_4'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_5'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_6'}>]], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pay_bins = np.array(range(-2,10)) - 0.5 # Array of 12 numbers (11 bins), centered around 1(- 0.5) of the unique values of the PAY features (11 values).\n",
    "# Since the default number of bins is 10 is always better to set them correctly for realiable results.\n",
    "# BIN EDGES -> All bins have left edge closed and right open while for the last one are both closed. For control over plot appearance, it's usually better to specify\n",
    "# the bin edge locations.\n",
    "\n",
    "mpl.rcParams['font.size'] = 4 \n",
    "df_clean_2[pay_feats].hist(bins=pay_bins, layout=(2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are very few accounts that had a 1-month payment delay in July(PAY_3) while in August(PAY_2) there are thousands of accounts with a 2-month payment delay. \n",
    "\n",
    "The number of accounts with a 2-month delay in a given month(e.g August/PAY_2) should be <= to the number of accounts with a 1-month delay in the previous month(e.g July/PAY_3). \n",
    "\n",
    "Due to this invalid issue in the data recording the only reliable feature is PAY_1 so we can drop variables from PAY_2 up to PAY_6.\n",
    "\n",
    "When we see something like this in the data is better to check the logic in the query used to create the dataset or contact the creator of the dataset.\n",
    "\n",
    "Learned from the company:\n",
    "\n",
    "They had been having problems with pulling the most recent month of data (1-month delay in payment). \n",
    "\n",
    "In September(PAY_1), they had \"mostly\" fixed these problems, that is why there were missing values in the PAY_1 feature, as we found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_2.to_csv(\"Data/df_clean_2.csv\", index=False)  # saving\n",
    "\n",
    "# Check point\n",
    "# ID -> removal of duplicate and invalid IDs (observations)\n",
    "# LIMIT_BAL\n",
    "# SEX\n",
    "# EDUCATION\n",
    "# MARRIAGE\n",
    "# AGE\n",
    "# PAY_1-PAY_6 -> Adjustment Pay_1 type + drop of the missing (\"not avaible\") + To drop from PAY_2 to PAY_6 since invalid data (Related to the drop of the missing)\n",
    "# BILL_AMT1-BILL_AMT6\n",
    "# PAY_AMT1-PAY_AMT6\n",
    "# default payment next month\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BILL_AMT1–BILL_AMT6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26664 entries, 0 to 29999\n",
      "Data columns (total 26 columns):\n",
      " #   Column                      Non-Null Count  Dtype \n",
      "---  ------                      --------------  ----- \n",
      " 0   ID                          26664 non-null  object\n",
      " 1   LIMIT_BAL                   26664 non-null  int64 \n",
      " 2   SEX                         26664 non-null  int64 \n",
      " 3   EDUCATION                   26664 non-null  int64 \n",
      " 4   MARRIAGE                    26664 non-null  int64 \n",
      " 5   AGE                         26664 non-null  int64 \n",
      " 6   PAY_1                       26664 non-null  int64 \n",
      " 7   PAY_2                       26664 non-null  int64 \n",
      " 8   PAY_3                       26664 non-null  int64 \n",
      " 9   PAY_4                       26664 non-null  int64 \n",
      " 10  PAY_5                       26664 non-null  int64 \n",
      " 11  PAY_6                       26664 non-null  int64 \n",
      " 12  BILL_AMT1                   26664 non-null  int64 \n",
      " 13  BILL_AMT2                   26664 non-null  int64 \n",
      " 14  BILL_AMT3                   26664 non-null  int64 \n",
      " 15  BILL_AMT4                   26664 non-null  int64 \n",
      " 16  BILL_AMT5                   26664 non-null  int64 \n",
      " 17  BILL_AMT6                   26664 non-null  int64 \n",
      " 18  PAY_AMT1                    26664 non-null  int64 \n",
      " 19  PAY_AMT2                    26664 non-null  int64 \n",
      " 20  PAY_AMT3                    26664 non-null  int64 \n",
      " 21  PAY_AMT4                    26664 non-null  int64 \n",
      " 22  PAY_AMT5                    26664 non-null  int64 \n",
      " 23  PAY_AMT6                    26664 non-null  int64 \n",
      " 24  default payment next month  26664 non-null  int64 \n",
      " 25  EDUCATION_CAT               26664 non-null  object\n",
      "dtypes: int64(24), object(2)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df_clean_2.copy()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6\n",
       "0          0          0          0          0          0            774\n",
       "390        390        390        390        390        390           33\n",
       "2400       2400       2400       2400       2400       2400          27\n",
       "2500       0          0          0          0          0             23\n",
       "1050       1050       1050       1050       1050       1050          18\n",
       "                                                                   ... \n",
       "10083      11211      13669      316        120961     123355         1\n",
       "           11091      12117      13125      13407      13692          1\n",
       "10078      8886       8815       9600       0          0              1\n",
       "10072      7728       2344       9536       9043       0              1\n",
       "746814     374028     351588     86927      66111      38491          1\n",
       "Length: 25541, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No missing entries for features BILL_AMT1 – BILL_AMT6\n",
    "bill_feat = [\"BILL_AMT1\", \"BILL_AMT2\", \"BILL_AMT3\", \"BILL_AMT4\", \"BILL_AMT5\", \"BILL_AMT6\"]\n",
    "df[bill_feat].value_counts()\n",
    "# df.BILL_AMT1.value_counts()\n",
    "\n",
    "# Rec: BILL_AMT1–BILL_AMT6: Monthly bill statement amount(Estratto conto, capitale utile mensile )\n",
    "# (BILL_AMT1 = Bill statement amount in September, BILL_AMT2 = ... August, BILL_AMT3 = ... July, BILL_AMT4 = ... June, BILL_AMT5 = ... May, BILL_AMT6 = ... April). \n",
    "# Measurement scale for each bill statement amount (BILL_AMTN) : (NT dollar).\n",
    "\n",
    "# NB lot of 0 values and REC that is time related features --> look if it has sense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51405.730723</td>\n",
       "      <td>49300.001500</td>\n",
       "      <td>47026.340047</td>\n",
       "      <td>43338.894539</td>\n",
       "      <td>40338.136701</td>\n",
       "      <td>38889.872337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73633.687106</td>\n",
       "      <td>70934.549534</td>\n",
       "      <td>68705.359524</td>\n",
       "      <td>64275.250740</td>\n",
       "      <td>60705.944083</td>\n",
       "      <td>59432.541657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-157264.000000</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3580.000000</td>\n",
       "      <td>2999.750000</td>\n",
       "      <td>2627.250000</td>\n",
       "      <td>2341.750000</td>\n",
       "      <td>1745.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22361.000000</td>\n",
       "      <td>21150.000000</td>\n",
       "      <td>20079.500000</td>\n",
       "      <td>19037.000000</td>\n",
       "      <td>18066.000000</td>\n",
       "      <td>17005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67649.750000</td>\n",
       "      <td>64395.500000</td>\n",
       "      <td>60360.000000</td>\n",
       "      <td>54727.500000</td>\n",
       "      <td>50290.500000</td>\n",
       "      <td>49253.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>746814.000000</td>\n",
       "      <td>671563.000000</td>\n",
       "      <td>855086.000000</td>\n",
       "      <td>706864.000000</td>\n",
       "      <td>823540.000000</td>\n",
       "      <td>699944.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BILL_AMT1      BILL_AMT2      BILL_AMT3      BILL_AMT4  \\\n",
       "count   26664.000000   26664.000000   26664.000000   26664.000000   \n",
       "mean    51405.730723   49300.001500   47026.340047   43338.894539   \n",
       "std     73633.687106   70934.549534   68705.359524   64275.250740   \n",
       "min   -165580.000000  -69777.000000 -157264.000000 -170000.000000   \n",
       "25%      3580.000000    2999.750000    2627.250000    2341.750000   \n",
       "50%     22361.000000   21150.000000   20079.500000   19037.000000   \n",
       "75%     67649.750000   64395.500000   60360.000000   54727.500000   \n",
       "max    746814.000000  671563.000000  855086.000000  706864.000000   \n",
       "\n",
       "           BILL_AMT5      BILL_AMT6  \n",
       "count   26664.000000   26664.000000  \n",
       "mean    40338.136701   38889.872337  \n",
       "std     60705.944083   59432.541657  \n",
       "min    -81334.000000 -339603.000000  \n",
       "25%      1745.000000    1256.000000  \n",
       "50%     18066.000000   17005.000000  \n",
       "75%     50290.500000   49253.750000  \n",
       "max    823540.000000  699944.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bill_feat].describe()\n",
    "# Care for the negative values -> admitted, OK\n",
    "\n",
    "# Seems all to make sense \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20519"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"BILL_AMT1\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'BILL_AMT1'}>,\n",
       "        <AxesSubplot:title={'center':'BILL_AMT2'}>,\n",
       "        <AxesSubplot:title={'center':'BILL_AMT3'}>],\n",
       "       [<AxesSubplot:title={'center':'BILL_AMT4'}>,\n",
       "        <AxesSubplot:title={'center':'BILL_AMT5'}>,\n",
       "        <AxesSubplot:title={'center':'BILL_AMT6'}>]], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''bill_bins = np.array(range(1,21)) - 0.5 # 21 centered bins \n",
    "\n",
    "mpl.rcParams['font.size'] = 4 \n",
    "df[bill_feat].hist(bins = bill_bins, layout=(2,3))'''\n",
    "\n",
    "mpl.rcParams['font.size'] = 4 \n",
    "df[bill_feat].hist(layout=(2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6\n",
       "0          0          0          0          0          0            774\n",
       "390        390        390        390        390        390           33\n",
       "2400       2400       2400       2400       2400       2400          27\n",
       "2500       0          0          0          0          0             23\n",
       "1050       1050       1050       1050       1050       1050          18\n",
       "                                                                   ... \n",
       "10083      11211      13669      316        120961     123355         1\n",
       "           11091      12117      13125      13407      13692          1\n",
       "10078      8886       8815       9600       0          0              1\n",
       "10072      7728       2344       9536       9043       0              1\n",
       "746814     374028     351588     86927      66111      38491          1\n",
       "Length: 25541, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bill_count = df[bill_feat].value_counts()\n",
    "bill_count\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIMIT_BAL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # plotting package\n",
    "# Plot direct under the cells\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl # additional plotting functionality\n",
    "mpl.rcParams['figure.dpi'] = 400 # high resolution figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g8ZlNH4qInR3D1TS243hghuOBvfxuZt19gOd3CZJS+k/AczObXjbH7jW1k7WVYW2NhxNAJI3ZMeRfx4bcGrFt/42BcbX1bGTWDe1/0AymznOsWdtzcbrK/T65Y82zfRm1uY32QZTaXidy29fVZ5YipXRP8oMqH46Iy+cIsZFZt4g2WlRfvj4iLllCLkfPuCIsl0sp1tBc2o6360q5IxedS0RcDNzQJZeW7evqMwuVUtojpfQUmueoTr/WfRN4xpyhNjLrrCvrqk1ue811dSq3vvXrz0dEbtBmUTYy66wr66pNbnvNdbXLz9E8xqI0hng74CnAa4Evp5T+OKV0x57H2siss66sqza57TXVVe5W4Tt3/SOl9KzJ/18JPAjYO/Pze9E8BvDFwGdSSm9NKXX9AmIjs87asrba5LbXVFt9PBnYL7P+DT1ibWTWOR7oeGCb3PaqxgMjYgfwusymV6SUXjDPHalSSj8OvJv/ODEf4IyIeMccaWxk1llb1lab3PaqamtdnAAiacwOL6z/1sC4pf1Lx9PuK9cnhvY/aL7Um+dYY8olgG8vIZfS8WZtW9frRE3naeEmV2C/jvx7zN+bM0xNbZTbPrQfl3LZAzi0Yy4wvG361NVtyZ/jRZynXIwtWVcppbumlI6fWh6YUjo5pfSclNJrga8ApwNHTO1+AXBiRHxpzsPV1EbWVZ511dPkS7Dfmlp9RkScPSTuHGpqI+sqz7pavf2A5wDnp5Se02P/mtrIusqzrro5NrPuypTSXimltwH/h6ZN57UH8FTgvJTSyR32q6mdrK08a2v5npVZ913gnT1i1dQ+tefieGB5e4019Qvcuib2AF5OMwnxl1NK900pHTp5bNnBk7GNZ07uZHUmsH1q//cCT5/z+DW1U+25WFvl7TXW1srNeu6cJNWs9CFunivMiyLi5pTS1cABU5u6fCjXFpdS2h/IPfN6UP+buCKzblb/y9XDsnLZL6W0X0Rc2yGXqyPi5iXkAu1tU83rRIV9Zhn+O3B8Zv17IuIDc8ZYZV+uqa6gyaf04W0pfXlGLiXLygXqP0+LrKvXAA/uuM9VwKuB3+54Z4Oa2si6Wm0uUP95GlpXfwLsv+n/l9HcEWTZamoj62q1uUD952ndn133B/44pfRA4OkdHhlQUxtZV6vNBeo/T53qanLnidxjka4C3gY8oUdeuxwMvCul9OyIePMcP19TO1lbq80F6j9PS/+blVK6O3DfzKY/i4jrOsaqbWzH8cD5cwHHA1tFxE0ppVOAF9A8TnPfTZvvAvxBh3DXAC8Fficibpn1wxW2k7U1fy5gbVXHO4BIGrMDC+uvWkDs3Jcq0398tHurrf/l8llWLtCeT+25wHrOU025LFxK6ZE0Hw6nXQ78fIdQq+w/Y68rGJ5PTblA/edpXe8FrgZeCBwTES/s8ViLmtrIulptLlD/eepdV5Nb5580tfrXFvBc5nnU1EbW1WpzgfrP09C/VzcAO4DfAB5HM+B/GM3jKQ6iuavBQ4AXAee1xPlp4BUdjltTG1lXq80F6j9PXevqEPJj70/g1pM/vkpTb/ejubp0H+Bo4GTg9eQf4bENeF1KKfel9rSa2snaWm0uUP95WsVnrNzdPwDe2CPW7nyuwPFA2OLjgRFxS0T8DnB74GXAFzqG+AzN37SNiHjZPJM/Jmprp9r7c025gLVVHSeASBqzvQrrb1pA7Bsz63LPYtXuq7b+l8tnWblAez615wLrOU815bJQKaW7AmeQf2/53Ij4codwq+w/Y68rGJ5PTblA/edpXe8FDgD+J3B2SukJ8zx7d0pNbWRdrTYXqP889aqrlNL3Ab8/tfrvae4Isgo1tZF1tdpcoP7z1Pfv1UeBZwO3jYiHRMRLIuLMiPh8RHwnIm6MiKsi4isRsSMiXhoR96GZDPKvhZinppSeNufxa2oj62q1uUD956lrXeWuSAU4aur/rwLuPKm3cyPikoi4ISK+FhHvi4ifBe4GfKJwjLenlErH2qWmdrK2VpsL1H+elvoZK6W0N82jk6Z9MiJydTXL7nyuwPFA2H3GA4PmLh5d76ZwJM3k4bt23K+2dqq9P9eUC1hb1XECiKQx21ZYP/TWV6UYPjZLm9XW/3L5LCsXaM+n9lzaYnXR9TzVlMvCpJSOonme5yGZza+MiL/oGHKV/WfsdcUCbvc49rqCuvrMMiWaRyydAXwkpfT9HfatqY2sqw65tMTqovbz1LeuXsV/fL70DcBzOjxqYqia2si66pBLS6wuaj9PveoqIk6IiDdERKer5iJiB3Af4B2FH3lZSmm/OULV1EbWVYdcWmJ1Uft56lpX8/z870TEL0TENW0/FBFfpJlolfuyegN4+ozj1NRO1laHXFpidVH7eVr2Z6zHkH8c0xt6xtudzxU4HliKMZZcZkop7ZNS+l1gJ/ASmrtTdXEb4KeAHSmlc1JKd5xzv9raqfb+XFMubbG62NK1tWpOAJE0ZqWZfIt4Ic7FKM2I1O6ptv6Xy2dZuUB7PrXn0hari67nqaZcFiKldDjw18DtMpv/CvilHmFX2X/GXle7ni1eRS4tsbqo/TwtrK4i4sSISJsXYH+aq2VOAH4R+NvC7icAH00p3WPOw9XURtZVh1xaYnVR+3nqXFcppZ8AHj+1+uUR8dleWfVTUxtZVx1yaYnVRe3naeWfXSfPLP8p4MOZzUcBz5sjTE1tZF11yKUlVhe1n6eudTXr5/+R5jFKc4mIK4CnANdmNr8gpZRadq+pnaytDrm0xOqi9vO07L9Zz8ysux44vWe83flcgeOBpRhjyaVVSulI4J+BXwX2ndp8DfCXNO/pHgM8CPgx4Gdo7sL4jUzIBwGfTCmdPMfha2un2vtzTbm0xepiy9bWOjgBRNKYXV9Yv4hbMeVilI6n3VNt/S+3fVm5lI43llzaYnXR9TzVlMtgKaXb0Ez+yN3S8a+BJ/a8cmqV/WfsddUWa4y5lGLUdJ6WWlcRcW1EfCMiPhoRfxQRDwbuRTMAM+1w4H2TWpylpjayrlabSylGTeepU12llLbT3P1jsy8AL+2ZU181tZF1tdpcSjFqOk9r+ewaETcA/4XmjjzTfmKOEDW1kXW12lxKMWo6T13rKlcHm72o6+eliPg88ObMpu8H2iYG19RO1tZqcynFqOk8Le1v1uSOpY/IbHpnRHy3Z9jd+VyVjjeWXNpidbElxwNTSocCHyL/9+SVwO0i4okR8eqIeFdEfCQizo6IN0fEc2juSPU8mokimx0AvDOl9OAZKdTWTrX355pyaYvVxZasrXVxAoikMbuysP6gBcTOxbhiAXG1ddTW/3L5LCuX0vHWlUtb29R0nmrKZZCU0sHA+2m+mJ52DvDYiOj7pniV/ae2ulpH/6kpl1KMms7Tyt8LRMT5NHf8yN1W/0jgFXOEqamNrKvV5lKKUdN56lpXrwC+d2rdcwf83emrpjayrlabSylGTedpbZ9dI+JC4M8zm+6XUsrdgn+zmtrIulptLqUYNZ2nrnV1OVB6LNlFEfHBjvF2eX1hfdsXazW1k7W12lxKMWo6T8v8m/V08o8I6Pv4F9i9z1XpeOvKZSw1VVMubV4L3HlqXQBPj4jnR8QlbTtHxA0R8WrgAcBlU5v3Ad6aUso9PnqX2trJ2po/l7ZYXWzV2loLJ4BIGrPvFNZvHxI0pbQvzZuSeY+n3dDk6rbcs7G3LyB8Lsas/pfbnovTVS7GFRHRdsuzXC77ppRydTU0l9LxZm0rxZpLn9eJCvtMLymlA4GzyT8D9B+AR01uAd7XKvtyTXVVOt6sbaVY8yrtv45cSjFqOk9reS8QETfR3PL705nNT00pHT0jRE1tZF1121aK1UUuRk3nae66Sik9lOYWw5u9OSJyj5xYtprayLrqtq0Uq4tcjJrO07o/u747s24P4Idm7FdTG1lX3baVYnWRi1HTeepUV5P3b5cXNn+ka0KbnF+Ie3zLPjW1k7XVbVspVhe5GDWdp2X+zZp+3wjwJaDvBKwax3YcD+y2rRRrLlt1PDCldALwhMym342IP+0SKyI+RTN+Me0Y4NSW/WprJ2ur27ZSrLls1dpaJyeASBqzbxbWT18R2FVp/9LxtPvK9Ymh/a8UY1b/qz2XUqyhubQdr7ZcStvXdZ46SykdALyX5m4E0z4GnBwRuTfeXdTURrnth6eUhr6HzuVyPbe+QmJWLqVYQ3NpOx7Ad8nf0npQLimlbTSPNemSS2n7aOpqXpPBgBdkNu0JnDJj95rayLrKs65mSCntz62ver4Y+JWhSfVUUxtZV3nW1fp8vLD+iBn71dRG1lWeddXN1wrrP9kjFgAREcCnMpu+p2W3mtrJ2sqzthZs8riJO2Y2vWlSR0PU1D6151KKNTSXtuPVlktpe0019bzMukvp+ZjNiDgb+EBm08/NeL2vqZ1qz6UUa2gubcerLZfS9ppqa22cACJpzL5O/rlbxw6MW9r/3wbG1daT6xOD+t/kw/mRcx5r1vajJvGGyP0+fXIpxRqay3W0v/mq7XWipj7T9Tj701zN+SOZzZ8AHhkRi7gV3sLbqCVGn/O1DThqCbnsnDEItcq6ajseEXELzVVTi87lKPKfT1ZeVy0x1v1e4G/IX3HwwBn71dRG1lWGdTWX+wF3mFr3NuAHUkrHd1kK8Y8u/PzBhZ+vqY2sqwzraq2+XVjf9gU11NVG1lWGddXZRYX1l/aItVnu/eBtW36+pnaytjKsraV4ZmbdLcCbFhC7prEdxwPzHA+cP04CHpHZdFZEtD2WZJY/y6w7AvjBln1qaidrK8/aGgkngEgarcmHuAszm+40MPQPFNZ/cWBcbT0XZNYN7X8bwF5zHmvW9r0m8YbI1UOfXGA5tfnFtgGdCl8nauozc0sp7QecBZyY2fwp4OERcdmCDpfL+3Yppb37BpzcEjH35r+mvjwrl2+Qv63hMnK5EvjWjP2W0ZdLddXnPK2rzyxVRNxM/orRWR9ua2oj66rMupoRIrPul4CP9lhynlX42fsUfr6mNrKuyqyrusz6XWtqI+uqzLqa3+cK66/pEWuzqzPrDmz5+Zraydoqs7YWJKV0EPm7JH4wIr68gEPUNLbjeGCG44GdHEt+EuHfdU1oSulxZ/dq2aemdrK2Mqyt8XACiKSxOy+z7p4DY947s+6rEXHJwLjaenL97+iU0qEDYub6H8y+RWwuFxhQDymlw4Cju+YSEReTv9XtMmqzNZeJml4nauozc5k8g/GvgIdmNv8L8NCIGHoF22a5NtoG3H1AzHtMYkz75Iz9Pg3clFk/pK5SYf/WXCYfsM5fZC4Tuf5z/hy3xM2dpx+c/H6LzOUmmn7WNZd19ZlVuDiz7jYz9qmpjayrMutqXGpqI+uqzLpaj9KdPmbdLa6mNrKuyqyr+Z1bWF+6u9S8Dsmsa/tMVlM7WVtl1tbiPAnYP7P+DQuKX9PYjuOBZY4Hzif3mCiYPWltltL+h7XsU1M7WVtl1tYIOAFE0tj9U2bdXVNKuQ/D83rAnMeRSv3ihAExc/3vEsq3jt3lQvK3gV10LjBfPeR+pncuKaXtwHELzGVdrxM19ZmZJlfo/D/g4ZnNn6OZ/LHoyXHn0tyWddqi2+hmys+nByAiriP/fO0hudwdOCizvm//Ob7vgOBkvx9eYC4HA3frk8tE7jydHxG5W0tuVk2fWZEDMutyzwvfrJo2sq4652Jd1auaNrKuOudiXS3fDxXWz7rqupo2sq4652Jd5f19Yf2sxyHNktu/7XNZNe1kbXXOxdrq51mZdZcCZy4ofk1jO44HdsvF8cBb27Ow/saOcebdv+176Zraydrqlou1VRkngEgauw9k1m0DHtYnWErpe8jP8MsdR/oX8rOZHzkgZm7fD866UmSy/YMryOWbETHrShHI18y9U0qlWeWzPIz8+5Z5arOm14lq+swsk1u7vgM4ObP588BJEVF6vntvk0fJ5K6UW3QbnTvnY2ty5/WklFLpA3KfXG4EzumZy+GUH08wyw+Rv/Jinr58Dvkr93qdp5TSXsBJfXKpsM8sW+5qkdYrcypsI+sqz7oakQrbyLrKs67W49GF9aUrKYEq28i6yrOu5hQRXyd/p4X7do21y+RzWu4q250teVxGXe1kbeVZWwuQUror+Uk4p88x8WVe1YztOB7YORfHA2+tNIGw7zmbtX/urqa7VNNO1lbnXKytyjgBRNKoRcTnyD9z7Ik9Qz6BWz9fPIB394ynLWzyRz/XN05JKeVua9kqpXR38ld2nDVniHdl1t198uG3ay57kn9eau4YOe+hqZ3N9gB+smsuE0/KrLsgIv511o41vU5U2GdKcfcEzgAeldn8BeAhEfHNIceYIdfPHpFSmvWYi1uZ3PIvdweTIXV1G+ARXXOZyPXlHRFx5Rz77qB5dvS0vn05l8uVk+O0iogryA+o9s3lkcD2zPoh52ldfWZpUkpHkB/w/+wcu9fURtZVhnXVLiJ2RERaxFI4xIsLP7+jJa2a2si6yrCuVi+l9P3AkzObLoiIr8wRoqY2sq4yrKvO3pFZ9+DJRI4+HgTsk1n/4Rn71dRO1laGtbUwzyysX9TjX2oc23E8MMPxwLmVLu4q3dFtXvcrrC9OAKmwnaytDGtrJCLCxcXFZdQLcBrNH4TNyw3AMR3jJJpHGkzH+tC6f0eXQf1jI3NOAzhxQfFPLMR/Uo9Yr8vEuRI4YM79DwCuysR4XY9cnlT4vR7UIcaOzP6fBVLHXI6huXpnOtZvdohRzetETX2mEHMb8PZCjhcARy2idmbkcHua281OH/+FPWK9MBPnFmCjwzn/t0yM9/XI5fhCuz6tQ4w3Z/a/pOs5n7xefCcT640dYjyj8Pv8cI+2eV8mzkVj7DPLXIAXFdr8lDG1kXVlXa27rgptfFqPONW0kXVlXa27riY57Q18qNDec713r6mNrCvrahF1RTMmcXMm7lN7xntnJtbNwKFjaSdry9paRG0VjrMn8M3MsT6+yONMjnVi4Vw5Huh4YPV9JhPvgky8C4FtA2rkbYW6P2Is7WRtWVtDa2udy9oTcHFxcRm6AEcC12demN/SMc4zC38oHr/u39FlUP/YKJzXExcUPwGfzsT/ArB3hzh3nrxJmo7zRx3zeVXhzdedOsTYh/wb//M75nJKoe1/pmOcP83EuA74vg4xqnmdqK3PTMXcAzi98DteCBy9iLqZM5d3Z3L4DnDbDjEOo3nO73Scszrm8iuFNnlIx/N+TibGt4B9O8S5byGXF3f8nX6rEOc+HWLsR3OlyHSMHR1zOamQyy+Ptc8sqSaOA67J5HYZsN/Y2si6sq7WWVeFtjmtZ6xq2si6sq761hXNI//mPn5LO5cmEV/R8ferpo2sK+uqb11Nxf7LTNwvAQd3jPNw8l/Iv31s7WRtWVuLqK3MsR5b+L1/fgnHqmpsB8cDSzEcD5wv5isXcc42xbsbzSOtpuOdN7Z2srasrSG1tc5l7Qm4uLi4LGIBXlv4Q/GTc+5/J5ovUKb3/zSwx7p/P5dBfWOj0DdOXOAxnlg4xqvm3H9/mmerTu9/LXBsx1xuN3nDNh3rn5n/C8LXDKmnTXH2oHkO33Scy4A7DmzbV/c4T9W8TtTUZzbFTMCbCnld1Ddu34XylVxnzdPeNHcyeVdm/1voePUUcBD5QbidwGFzxvi1wu/zqz3a5uxMnBuAE+bc/4HkP9C8p0cupd/rV+bc/3Cawe/p/b8FHDjWPrMp5h/SDEJ2uhojE+dewDcKv99pY2wj68q66ltXi1iG1lKtbWRdWVd96wo4E7iaZoD7eLpfRfhgmqsPc79XAM8baxtZV9ZV37qain9cod3fBewzZ4y7AF8v5Hj3sbWTtWVtLaK2MsfLHetaYPuijzU5XjVjOzge2BbL8cDZcU8o5HUlcK+OsQ4FPl+I94KxtZO1ZW0Nqa11LmtPwMXFxWURC81s8txtFq8Dnjxj33sDXyn8gZj7ygOXOhdWMAFkcpwdheP8AbBny36H0TyrN7fvaT1zeXEh3gdpGUihuVXmHxb23dEzl9JVJ18C7jlj358iP5O409UmU21dzetETX1mEjc3oz1ovgx4Is3Az9Dl3h1zekshpz8H9m/Z7wDgjMK+b+7ZPj9TiHc+M25bS3N1W+5KvU4z0TfFu0uhNr4LnDRj34eR/3B1PXDnHrnsM/k9puPdwoyry2huKZyboR/AM3qep2r6zFSdf5rmtsdzfSDftP8G8Aryt+YMmkGV4u9VextZV9bVupZCTqcNiFdNG1lX1lXPfM6civU14I3A82luqXx74LbAXjSDo0fRTPr4deAThXx2LWeMvY2sK+tqEQvwksIxPgIcN2PfU4CLC/u/ZqztZG1ZW4tcgO8l/7np9EUfa+q4Owq/o+OB+XiOB1bUZ6bi5yZQBXA58MQ5Y9wP+GIhzteYc8JEbe1kbVlbY1zWnoCLi4vLohbKt/kL4L3AjwNH0Mw+304zYPV68jP0A3jFun8nl5nnfKPlnC9i2eiYy3cLcT4DPJvmg/iuQdN7Ar9ZeJMUwD8Ce/Vsl72BfyrEvQR4EfCDNLcW3Qu4A/AcylftXQrcbsB5+l+FuDfQPFfvR4BDJrX5vcDjyD+rdtfymAG5VPM6UVOfmeSzc0Y9LGLZ2TGn7eSfDR00H2xOpbk93z6T5Tjglyh/gLiIAVf+kH/edtA8D/TlNB90D6D5gHcMzQerfyzscz1w3wG5/GIh7i00A3GPoLnqYhvNFzY/SjP4lhskDeD5A3K5P/kPkAH8A/Bkmi+K9gQOBH4Y+H2ayUW5ff7vgFxq6zM7MjEvoLkN+K9P+sijaF4HTwQeDTwL+N+TvlM6X0HzWtD6ZcFI2si6sq5WvhTyOm2rtJF1ZV31yOfMQtyhyxn0/0xTWxtZV9bVoGXSBjsKx7qR5ou3/wr8GM17w8fRfP47r7BPAH9Px8kOtbUT1pa1tbgaK905pXUCzwKOu0ElYzs4HtiWy2Nb4u7W44Gb8roT+cc17VrOo3mdvA/Na+Cek/N3Z5oJfe9t2fcW4CfG2k5YW9bWCJe1J+Di4uKyyAX4by1/cLos7xrzi/vuslDRBJBJPifR3BZs6HEvBI4a2DZH0XyoHprLNQy8E87kTVXuubF9lhcuoN9U8zpRWZ/ZueD6yS07e+R1D8pvxLsslzDnrZFbcjmY5haPQ3O5GXjKAvryHy/ovLxuAbk8dfJ7Dc3lY3R8FnrlfWbHEuooaF7f77ZF2si6sq5WvhRyO22rtJF1ZV31yOXMBZ2jXcvlwDMXcL5qaiPryroavNB88bGIfhSTOEeMvZ2sLWtrgfX1r5njXcTAx3HOeeyaxnYcDyzn43jg7LweODn3i2inzcupY28nrC1ra2TL2hNwcXFxWfRCM/P8pgEv7m9lzmewuqz9XG8s6M1FadnokdOJlG/NOs/yceCYBbXPMcy+JXPb8m3gwQvKZR/gbQNyuZEBV89k8qnmdaKWPkOlE0Amud2DYR+yLmRBg0zAbYC/GZDLVXR8xmdLLgn4vYHn5OUsaECM5lFBpSvO5lnez4KuBKulz7D4CSA30lzZd8BWaSPryrpax1LI77St1EbWlXXVMY/fYNj70V3Ld2geXbaQzzM1tZF1ZV0tsE8fSHM3iSHn7m3Avlulnawta2sBuT2wcMzfWFYtZ3I4kQrGdia5OB5YzudUHA+cldf9aR43O+R1cNdyGfC0rdJO1pa1NaZl7Qm4uLi4LGOhufXhuR1f2L8OPHXdubt0Os8bA/6Qz7Ns9MzrKNpvA5pbrgV+m4GDOJlc9qV51nCXma+3TPI/cgnn7KcntdblPJwL3H8JuVTzOlFDn6HiCSCT/G4DvJZuHyZuAl7Dgm8vS3PbwlPJP+e5bfkbejwDeo58Tib/jOi25QvAjy4hl7vQffD2skl77rHgXNbeZ2hujfoSmg+PXep7evkGzcSPu2y1NrKurKt1LIU8T9tqbWRdWVcd80g0t67+WZpbR38U+Bbtf7+uo7kd9itpvqRc6GeZ2trIurKuFpzjo4HzO7bXOcCJW7GdrC1ra2Beb8wc92ZW/AUeFYztbMrF8cByLo4Hzs5r/0n/+XbHdtqc41sWVYM1tZO1ZW2NZUmThpCkLSml9DCaWyY+jOZFf9plwEeAdwBnRMT1q8tOQ6WU9gHuvcRDnDekT6SUjgOeRfNc2OOAPaZ+5HqaW52eBbwpIi7pe6w5cjmc5nmMjwHuSzNLeLNbgM8BZwNvjIjPLTGXfWgGiE+heSbh9syPfRX4AHB6RHxgWblM8qnmdaKmPlOrlNKxNG30n2me1bjn1I/cRDOQ+h7gDRHx5SXmchDwNJrnXT6A5tnUmwXNFVLvB94SER9bYi7baOr7ScBDgMMzP3Yx8GHgL4CzIuLmJeZzf+AZNM/JvgPNl0qbXU3zxdI7gbdGxJVLzKWKPpNSOoTmSpr70dT3Bs3VI4fQ9J0ArgSuoHke7GdpnrF7LvAPSz5fVbTRJBfrqpyPdbUgKaXTMqt3RMSOBR6jmjayrlrzsa5m57U3zbPDD6R5rvnNNO+PLwcuj4hbVpHHJJdq2si6as3HuuogpfQA4FHA8cCdgNvS3Nr9UprHZVwEfAj464j4zJJyqKadrK3WfKytcj73pPkbtdl1EfHJZR63pKaxHccDW/NxPHB2XnsDjwceSvPl/l1pJuzl7KR55NTfAX8WEd9ZQj7VtJO11ZqPtVUBJ4BI2m2klLYDR9J8eLwOuCQivrHWpLTbSCntCxwLHMT/HzT9yjIHB1py2UbzpeN2mjftV0xyuW7VuUzy+T7gMJoZ1FcDX4uIy9eUy3YqeZ2oqc/UKqW0F00bHTJZdTnw5Yi4cQ25JOBo4FCaAduraM7X1avOZZLPYcARNFdtXAN8a10fYlJKB9K0zYE0t5i8FPhqrOGDSE19plY1tZF11ZqLdTUiNbWRddWai3U1IjW1kXXVmot1NTI1tZO11ZqLtTUSNY3tOB7Ymst2HA+caVLvh9L0oYNo7phwGXBpRFy74lyqaSdrqzWX7Vhba+EEEEmSJEmSJEmSJEmSpJGbvtWJJEmSJEmSJEmSJEmSRsYJIJIkSZIkSZIkSZIkSSPnBBBJkiRJkiRJkiRJkqSRcwKIJEmSJEmSJEmSJEnSyDkBRJIkSZIkSZIkSZIkaeScACJJkiRJkiRJkiRJkjRyTgCRJEmSJEmSJEmSJEkaOSeASJIkSZIkSZIkSZIkjZwTQCRJkiRJkiRJkiRJkkbOCSCSJEmSJEmSJEmSJEkj5wQQSZIkSZIkSZIkSZKkkXMCiCRJkiRJkiRJkiRJ0sg5AUSSJEmSJEmSJEmSJGnknAAiSZIkSZIkSZIkSZI0ck4AkSRJkiRJkiRJkiRJGjkngEiSJEmSJEmSJEmSJI2cE0AkSZIkSZIkSZIkSZJGzgkgkiRJkiRJkiRJkiRJI+cEEEmSJEmSJEmSJEmSpJFzAogkSZIkSZIkSZIkSdLIOQFEkiRJkiRJkiRJkiRp5JwAIkmSJEmSJEmSJEmSNHJOAJEkSZIkSZIkSZIkSRo5J4BIkiRJkiRJkiRJkiSNnBNAJEmSJEmSJEmSJEmSRs4JIJIkSZIkSZIkSZIkSSPnBBBJkiRJkiRJkiRJkqSRcwKIJEmSJEmSJEmSJEnSyDkBRJIkSZIkSZIkSZIkaeScACJJkiRJkiRJkiRJkjRyTgCRJEmSJEmSJEmSJEkaOSeASJIkSZIkSZIkSZIkjZwTQCRJkiRJkiRJkiRJkkbOCSCSJEmSJEmSJEmSJEkj5wQQSZIkSZIkSZIkSZKkkXMCiCRJkiRJkiRJkiRJ0sg5AUSSJEmSJEmSJEmSJGnknAAiSZIkSZIkSZIkSZI0ck4AkSRJkiRJkiRJkiRJGjkngEiSJEmSJEmSJEmSJI2cE0AkSZIkSZIkSZIkSZJGzgkgkiRJkiRJkiRJkiRJI+cEEEmSJEmSJEmSJEmSpJFzAogkSZIkSZIkSZIkSdLIOQFEkiRJkiRJkiRJkiRp5JwAIkmSJEmSJEmSJEmSNHJOAJEkSZIkSZIkSZIkSRo5J4BIkiRJkiRJkiRJkiSN3L8D/9dlXH0r2AoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean_2[\"LIMIT_BAL\"].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     26664.000000\n",
       "mean     167919.054905\n",
       "std      129839.453081\n",
       "min       10000.000000\n",
       "25%       50000.000000\n",
       "50%      140000.000000\n",
       "75%      240000.000000\n",
       "max      800000.000000\n",
       "Name: LIMIT_BAL, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"LIMIT_BAL\"].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'LIMIT_BAL' seems fine since all the all the values are above 0 (min = 10000, no invalid data).\n",
    "\n",
    "No strange patterns in the hist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AGE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean_2[\"AGE\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26664.000000\n",
       "mean        35.505213\n",
       "std          9.227442\n",
       "min         21.000000\n",
       "25%         28.000000\n",
       "50%         34.000000\n",
       "75%         41.000000\n",
       "max         79.000000\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"AGE\"].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'AGE' seems fine (min = 21, no strange patterns in the hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    16080\n",
       "1    10584\n",
       "Name: SEX, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"SEX\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No undocumented, values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDUCATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    12458\n",
       "1     9412\n",
       "3     4380\n",
       "5      245\n",
       "4      115\n",
       "6       43\n",
       "0       11\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"EDUCATION\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0, 5, 6 undocumented values.\n",
    "\n",
    "Since '4 = others' and the counts for '0, 5, 6' are low I merge them with '4 = others'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    12458\n",
       "1     9412\n",
       "3     4380\n",
       "4      414\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[\"EDUCATION\"].replace(to_replace=[0, 5, 6], value=4, inplace=True)\n",
    "df_clean_2[\"EDUCATION\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           default payment next month\n",
      "EDUCATION                            \n",
      "1                            0.193901\n",
      "2                            0.235913\n",
      "3                            0.255479\n",
      "4                            0.074879\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Education level: ordinal encoding')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean_2.groupby('EDUCATION').agg({'default payment next month':'mean'}).plot.bar(legend=False)\n",
    "print(df_clean_2.groupby('EDUCATION').agg({'default payment next month':'mean'}))\n",
    "plt.xlabel('Education level: ordinal encoding')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning algorithms only work with numbers so we need to encode our categorical features.\n",
    "\n",
    "Our aim is to classify a binary response thus, in order to build our prediction, we can look at the average values of the response variable for each different level of  the categorical features and make a guess.\n",
    "\n",
    "Moreover, this can give us an idea about the encoding procedure to apply on categorical features.\n",
    "\n",
    "In this case we can see if an ordinal encoding will work well with a linear model.\n",
    "\n",
    "Looks like that EDUCATION has a non-linear effect on the response.\n",
    "\n",
    "It may be better use more complex algorithms, such as decision tree or random forest instead of a simple logistic regression that assumes linear effect of the features. \n",
    "\n",
    "In case we want to use a simpler and more interpretable linear model such as logistic regression we could avoid an ordinal encoding and use a different way of encoding categorical variables.\n",
    "\n",
    "OHE is another way to encode categorical features that avoids the implied numerical structure of an ordinal encoding. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OHE with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>EDUCATION</th>\n",
       "      <th>EDUCATION_CAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EDUCATION    EDUCATION_CAT\n",
       "0          2       university\n",
       "1          2       university\n",
       "2          2       university\n",
       "3          2       university\n",
       "4          2       university\n",
       "5          1  graduate school\n",
       "6          1  graduate school\n",
       "7          2       university\n",
       "8          3      high school\n",
       "9          3      high school"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column EDUCATION_CAT for the categorical labels \n",
    "cat_mapping = {1: \"graduate school\", 2: \"university\", 3: \"high school\", 4: \"others\"}\n",
    "df_clean_2['EDUCATION_CAT'] = df_clean_2['EDUCATION'].map(cat_mapping)\n",
    "df_clean_2[['EDUCATION', 'EDUCATION_CAT']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>EDUCATION_CAT</th>\n",
       "      <th>graduate school</th>\n",
       "      <th>high school</th>\n",
       "      <th>university</th>\n",
       "      <th>others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>graduate school</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>graduate school</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>high school</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>high school</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EDUCATION_CAT  graduate school  high school  university  others\n",
       "0       university                0            0           1       0\n",
       "1       university                0            0           1       0\n",
       "2       university                0            0           1       0\n",
       "3       university                0            0           1       0\n",
       "4       university                0            0           1       0\n",
       "5  graduate school                1            0           0       0\n",
       "6  graduate school                1            0           0       0\n",
       "7       university                0            0           1       0\n",
       "8      high school                0            1           0       0\n",
       "9      high school                0            1           0       0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OHE\n",
    "edu_ohe = pd.get_dummies(df_clean_2['EDUCATION_CAT'])\n",
    "df_with_ohe = pd.concat([df_clean_2, edu_ohe], axis=1)\n",
    "df_with_ohe[['EDUCATION_CAT', 'graduate school','high school', 'university', 'others']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chekpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_ohe.to_csv('Data/df_clean_3.csv', index=False) # saving\n",
    "\n",
    "# Check point\n",
    "# ID -> removal of duplicate and invalid IDs (observations)\n",
    "# LIMIT_BAL -> fine\n",
    "# SEX -> fine\n",
    "# EDUCATION -> merge undocumented categories('0, 5, 6' to --> '4 = others') + OHE\n",
    "# MARRIAGE -> \n",
    "# AGE -> fine\n",
    "# PAY_1-PAY_6 -> Adjustment Pay_1 type + drop of the missing (\"not avaible\") + To drop from PAY_2 to PAY_6 since invalid data (Related to the drop of the missing)\n",
    "# BILL_AMT1-BILL_AMT6\n",
    "# PAY_AMT1-PAY_AMT6\n",
    "# default payment next month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MARRIAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14158\n",
       "1    12172\n",
       "3      286\n",
       "0       48\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_3 = pd.read_csv('Data/df_clean_3.csv')\n",
    "df_clean_3[\"MARRIAGE\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'0 undocumented value', since low count I merge it with '3 = others'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14158\n",
       "1    12172\n",
       "3      334\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_3[\"MARRIAGE\"].replace(to_replace=0, value=3, inplace=True)\n",
    "df_clean_3[\"MARRIAGE\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_3.to_csv(\"Data/df_clean_4.csv\", index=False)  # saving\n",
    "\n",
    "# Check point\n",
    "# ID -> removal of duplicate and invalid IDs (observations)\n",
    "# LIMIT_BAL -> fine\n",
    "# SEX -> fine\n",
    "# EDUCATION -> merge undocumented categories('0, 5, 6' to --> '4 = others') + OHE\n",
    "# MARRIAGE -> merge undocumented categories('0' to --> '3 = others')\n",
    "# AGE -> fine\n",
    "# PAY_1-PAY_6 -> Adjustment Pay_1 type + drop of the missing (\"not avaible\") + To drop from PAY_2 to PAY_6 since invalid data (Related to the drop of the missing)\n",
    "# BILL_AMT1-BILL_AMT6\n",
    "# PAY_AMT1-PAY_AMT6\n",
    "# default payment next month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>LIMIT_BAL</th>\n",
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       "      <th>PAY_AMT6</th>\n",
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       "      <th>EDUCATION_CAT</th>\n",
       "      <th>graduate school</th>\n",
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       "      <td>5000</td>\n",
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       "      <td>university</td>\n",
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       "      <th>3</th>\n",
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       "      <td>50000</td>\n",
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       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>8998</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>high school</td>\n",
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       "      <td>0</td>\n",
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       "      <td>30000</td>\n",
       "      <td>1</td>\n",
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       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1178</td>\n",
       "      <td>1926</td>\n",
       "      <td>52964</td>\n",
       "      <td>1804</td>\n",
       "      <td>1</td>\n",
       "      <td>high school</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26663</th>\n",
       "      <td>15d69f9f-5ad3</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1430</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>university</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26664 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  \\\n",
       "0      798fc410-45c1      20000    2          2         1   24      2      2   \n",
       "1      8a8c8f3b-8eb4     120000    2          2         2   26     -1      2   \n",
       "2      85698822-43f5      90000    2          2         2   34      0      0   \n",
       "3      0737c11b-be42      50000    2          2         1   37      0      0   \n",
       "4      3b7f77cc-dbc0      50000    1          2         1   57     -1      0   \n",
       "...              ...        ...  ...        ...       ...  ...    ...    ...   \n",
       "26659  ecff42d0-bdc6     220000    1          3         1   39      0      0   \n",
       "26660  99d1fa0e-222b     150000    1          3         2   43     -1     -1   \n",
       "26661  95cdd3e7-4f24      30000    1          2         2   37      4      3   \n",
       "26662  00d03f02-04cd      80000    1          3         1   41      1     -1   \n",
       "26663  15d69f9f-5ad3      50000    1          2         1   46      0      0   \n",
       "\n",
       "       PAY_3  PAY_4  ...  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0         -1     -1  ...         0         0         0         0   \n",
       "1          0      0  ...      1000      1000         0      2000   \n",
       "2          0      0  ...      1000      1000      1000      5000   \n",
       "3          0      0  ...      1200      1100      1069      1000   \n",
       "4         -1      0  ...     10000      9000       689       679   \n",
       "...      ...    ...  ...       ...       ...       ...       ...   \n",
       "26659      0      0  ...      5003      3047      5000      1000   \n",
       "26660     -1     -1  ...      8998       129         0         0   \n",
       "26661      2     -1  ...     22000      4200      2000      3100   \n",
       "26662      0      0  ...      1178      1926     52964      1804   \n",
       "26663      0      0  ...      1430      1000      1000      1000   \n",
       "\n",
       "       default payment next month  EDUCATION_CAT  graduate school  \\\n",
       "0                               1     university                0   \n",
       "1                               1     university                0   \n",
       "2                               0     university                0   \n",
       "3                               0     university                0   \n",
       "4                               0     university                0   \n",
       "...                           ...            ...              ...   \n",
       "26659                           0    high school                0   \n",
       "26660                           0    high school                0   \n",
       "26661                           1     university                0   \n",
       "26662                           1    high school                0   \n",
       "26663                           1     university                0   \n",
       "\n",
       "       high school  others  university  \n",
       "0                0       0           1  \n",
       "1                0       0           1  \n",
       "2                0       0           1  \n",
       "3                0       0           1  \n",
       "4                0       0           1  \n",
       "...            ...     ...         ...  \n",
       "26659            1       0           0  \n",
       "26660            1       0           0  \n",
       "26661            0       0           1  \n",
       "26662            1       0           0  \n",
       "26663            0       0           1  \n",
       "\n",
       "[26664 rows x 30 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_4 = pd.read_csv(\"Data/df_clean_4.csv\")\n",
    "df_clean_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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