Predicting Loans Default.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brief Introduction of Data Set and Data Modeling Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Goal: Based on the attributes’ of the data, we can generate some sort of credit rating and predict if the customer will default payment next month.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attribute Information:\n",
    "This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explanation of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Limit Balance:</b> <br>\n",
    "Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. <br>\n",
    "\n",
    "<b>Sex:</b> <br>\n",
    "Gender of Credit Customer <br>\n",
    "1 = male <br>\n",
    "2 = female <br>\n",
    "\n",
    "<b>Education:</b><br>\n",
    "One of the 4 Levels of Education of Customer <br>\n",
    "1 = graduate school <br>\n",
    "2 = university<br>\n",
    "3 = high school<br>\n",
    "4 = others\n",
    "\n",
    "<b>Marriage:</b><br>\n",
    "Marital status <br>\n",
    "1 = married<br>\n",
    "2 = single<br>\n",
    "3 = others<br>\n",
    "\n",
    "<b>Age:</b><br>\n",
    "Age - continuous variable (number of years)<br>\n",
    "\n",
    "<b>PAY_0, PAY_2 - PAY_6:</b><br>\n",
    "History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: <br>\n",
    "X6 = the repayment status in September, 2005<br>\n",
    "X7 = the repayment status in August, 2005<br>\n",
    "X8 = the repayment status in July, 2005<br>\n",
    "X9 = the repayment status in June, 2005<br>\n",
    "X10 = the repayment status in May, 2005<br>\n",
    "X11 = the repayment status in April, 2005<br>\n",
    "\n",
    "The measurement scale for the repayment status is: <br>\n",
    "1 = payment delay for one month<br>\n",
    "2 = payment delay for two months<br>\n",
    "3 = payment delay for three months<br>\n",
    "4 = payment delay for four months<br>\n",
    "5 = payment delay for five months<br>\n",
    "6 = payment delay for six months<br>\n",
    "7 = payment delay for seven months <br>\n",
    "8 = payment delay for eight months<br>\n",
    "9 = payment delay for nine months and above. <br>\n",
    "-2 = Balance paid in full and no transactions this period (we may refer to this credit card account as having been 'inactive' this period)<br>\n",
    "-1 = Balance paid in full, but account has a positive balance at end of period due to recent transactions for which payment has not yet come due<br>\n",
    "0 = Customer paid the minimum due amount, but not the entire balance. I.e., the customer paid enough for their account to remain in good standing, but did revolve a balance* <br>\n",
    "\n",
    "\n",
    "<b>BILL_AMT1 - BILL_AMT6:</b><br>\n",
    "Amount of bill statement (NT dollar). <br>\n",
    "X12 = amount of bill statement in September, 2005 <br>\n",
    "X13 = amount of bill statement in August, 2005<br>\n",
    "X14 = amount of bill statement in July, 2005<br>\n",
    "X15 = amount of bill statement in June, 2005<br>\n",
    "X16 = amount of bill statement in May, 2005<br>\n",
    "X17 = amount of bill statement in April, 2005 <br>\n",
    "\n",
    "\n",
    "<b>PAY_AMT1 - PAY_AMT6:</b><br>\n",
    "Amount of previous payment (NT dollar)<br>\n",
    "X18 = amount paid in September, 2005<br>\n",
    "X19 = amount paid in August, 2005<br>\n",
    "X20 = amount paid in July, 2005<br>\n",
    "X21 = amount paid in June, 2005<br>\n",
    "X22 = amount paid in May, 2005<br>\n",
    "X23 = amount paid in April, 2005<br>\n",
    "\n",
    "\n",
    "<b>Default Payment Next Month:</b><br>\n",
    "Target Variable<br>\n",
    "\n",
    "*(Source: http://inseaddataanalytics.github.io/INSEADAnalytics/CourseSessions/ClassificationProcessCreditCardDefault.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\valli\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef,classification_report,roc_curve\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/valli/Downloads/card.csv',  skiprows = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>LIMIT_BAL</th>\n",
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       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
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       "      <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",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
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       "      <td>...</td>\n",
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       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
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       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</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",
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       "      <td>0</td>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0                           1  \n",
       "1      1000         0      2000                           1  \n",
       "2      1000      1000      5000                           0  \n",
       "3      1100      1069      1000                           0  \n",
       "4      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 25)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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",
      "ID                            30000 non-null int64\n",
      "LIMIT_BAL                     30000 non-null int64\n",
      "SEX                           30000 non-null int64\n",
      "EDUCATION                     30000 non-null int64\n",
      "MARRIAGE                      30000 non-null int64\n",
      "AGE                           30000 non-null int64\n",
      "PAY_0                         30000 non-null int64\n",
      "PAY_2                         30000 non-null int64\n",
      "PAY_3                         30000 non-null int64\n",
      "PAY_4                         30000 non-null int64\n",
      "PAY_5                         30000 non-null int64\n",
      "PAY_6                         30000 non-null int64\n",
      "BILL_AMT1                     30000 non-null int64\n",
      "BILL_AMT2                     30000 non-null int64\n",
      "BILL_AMT3                     30000 non-null int64\n",
      "BILL_AMT4                     30000 non-null int64\n",
      "BILL_AMT5                     30000 non-null int64\n",
      "BILL_AMT6                     30000 non-null int64\n",
      "PAY_AMT1                      30000 non-null int64\n",
      "PAY_AMT2                      30000 non-null int64\n",
      "PAY_AMT3                      30000 non-null int64\n",
      "PAY_AMT4                      30000 non-null int64\n",
      "PAY_AMT5                      30000 non-null int64\n",
      "PAY_AMT6                      30000 non-null int64\n",
      "default payment next month    30000 non-null int64\n",
      "dtypes: int64(25)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data has only integer values, no variable columns has null or missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
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       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
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       "      <td>38871.760400</td>\n",
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       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
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       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
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       "    <tr>\n",
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       "      <td>10000.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
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       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</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>25%</th>\n",
       "      <td>7500.750000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22500.250000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       LIMIT_BAL           SEX     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.853133      1.551867   \n",
       "std     8660.398374   129747.661567      0.489129      0.790349      0.521970   \n",
       "min        1.000000    10000.000000      1.000000      0.000000      0.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE         PAY_0         PAY_2         PAY_3         PAY_4  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6  default payment next month  \n",
       "count   30000.000000                30000.000000  \n",
       "mean     5215.502567                    0.221200  \n",
       "std     17777.465775                    0.415062  \n",
       "min         0.000000                    0.000000  \n",
       "25%       117.750000                    0.000000  \n",
       "50%      1500.000000                    0.000000  \n",
       "75%      4000.000000                    0.000000  \n",
       "max    528666.000000                    1.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For LIMIT_BAL and all the BILL_AMTs and PAY_AMTs, there is a significantly large difference between the 75% and the max value, therefore there are extreme outliers in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SEX.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4, 6, 0])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EDUCATION.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MARRIAGE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2, -1,  0, -2,  1,  3,  4,  8,  7,  5,  6])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_0.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  0, -1, -2,  3,  5,  7,  4,  1,  6,  8])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_2.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  2, -2,  3,  4,  6,  7,  1,  5,  8])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_3.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0, -2,  2,  3,  4,  5,  7,  6,  1,  8])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_4.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2,  0, -1,  2,  3,  5,  4,  7,  8,  6])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_5.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2,  2,  0, -1,  3,  6,  4,  7,  8,  5])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_6.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For education, there are rows with 0, 5 and 6 which belong to unknown categories. For marriage, there are rows with 0 which belong to unknown cateogory. For PAY_0 to PAY_6, there are rows with -2 and 0 which belong to unknown categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "5      280\n",
       "4      123\n",
       "6       51\n",
       "0       14\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EDUCATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      323\n",
       "0       54\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MARRIAGE.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For rows with education = 0, 5 or 6, we convert it into type 4, which categorises the unknown categories into \"others\".\n",
    "For rows with marriage = 0, we convert it into type 3, which categorises the unknown category into \"others\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "4      468\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fil = (df.EDUCATION == 5) | (df.EDUCATION == 6) | (df.EDUCATION == 0)\n",
    "df.loc[fil, 'EDUCATION'] = 4\n",
    "df.EDUCATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      377\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.MARRIAGE == 0, 'MARRIAGE'] = 3\n",
    "df.MARRIAGE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes : 22.12 %\n",
      "no : 77.88000000000001 %\n"
     ]
    }
   ],
   "source": [
    "All = df.shape[0]\n",
    "default = df[df['default payment next month'] == 1]\n",
    "nonDefault = df[df['default payment next month'] == 0]\n",
    "x = len(default)/All\n",
    "y = len(nonDefault)/All\n",
    "print('yes :',x*100,'%')\n",
    "print('no :',y*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['no','yes']\n",
    "classes = pd.value_counts(df['default payment next month'], sort = True)\n",
    "classes.plot(kind = 'bar', rot=0)\n",
    "plt.title(\"Default payment distribution\")\n",
    "plt.xticks(range(2), labels)\n",
    "plt.xlabel(\"Default\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.loc[:,[\"LIMIT_BAL\", \"AGE\", \"BILL_AMT1\", \"BILL_AMT2\", \"BILL_AMT3\", \"BILL_AMT4\", \n",
    "                               \"BILL_AMT5\", \"BILL_AMT6\", \"PAY_AMT1\",  \"PAY_AMT2\",  \"PAY_AMT3\",  \"PAY_AMT4\", \n",
    "                               \"PAY_AMT5\",  \"PAY_AMT6\"]].corr()\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "sns.heatmap(correlation_matrix,vmax=0.8,square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find if there are any dependencies between the attributes, we used a correlation matrix.\n",
    "Attributes BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6 are strongly positively correlated, meaning that if the customers spent a large amount in September, they are likely to spend a large amount in August, July, June, May, April. \n",
    "AGE is not linearly correlated to any BILL_AMTs and PAY_AMTs as the correlation coefficients are very close to 0. This implies that the age of the customers do not affect the amount they spent and the amount they pay each month.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x8064 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "anomalous_features = df.iloc[:,12:18].columns\n",
    "\n",
    "plt.figure(figsize=(12,28*4))\n",
    "\n",
    "gs = gridspec.GridSpec(28, 1)\n",
    "for i, cn in enumerate(df[anomalous_features]):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[cn][df['default payment next month'] == 1], bins=20, color = 'blue')\n",
    "    sns.distplot(df[cn][df['default payment next month'] == 0], bins=20, color = 'red')\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('histogram of feature: ' + str(cn))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms plotted show the distribution of BILL_AMT attributes. All the histograms are positively skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x8064 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomalous_features2 = df.iloc[:,18:24].columns\n",
    "\n",
    "plt.figure(figsize=(20,28*4))\n",
    "\n",
    "gs = gridspec.GridSpec(28, 1)\n",
    "for i, cn in enumerate(df[anomalous_features2]):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[cn][df['default payment next month'] == 1], bins=100, color = 'blue')\n",
    "    sns.distplot(df[cn][df['default payment next month'] == 0], bins=100, color = 'red') \n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('histogram of feature: ' + str(cn))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We removed the rows with PAY_AMT values higher than the 75th percentile, therefore we have a clearer view of the region where most data points are concentrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x8064 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amt = [5006,5.000000e+03,4505,4013.250000,4031.500000,4000.000000]\n",
    "anomalous_features2 = df.iloc[:,18:24].columns\n",
    "\n",
    "plt.figure(figsize=(20,28*4))\n",
    "\n",
    "gs = gridspec.GridSpec(28, 1)\n",
    "for i, cn in enumerate(df[anomalous_features2]):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[cn][df[cn] <= amt[i]][df['default payment next month'] == 1], bins=50, kde = False, color = 'blue')\n",
    "    sns.distplot(df[cn][df[cn] <= amt[i]][df['default payment next month'] == 0], bins=50, kde = False, color = 'red')\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('histogram of feature: ' + str(cn))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are many more data points concentrated near 0. The number of data points that have default payment next month = 0 is consistently higher than the that of default payment next month = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT3</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</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>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>689</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2682</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13559</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49291</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35835</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29995</td>\n",
       "      <td>220000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</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>208365</td>\n",
       "      <td>88004</td>\n",
       "      <td>31237</td>\n",
       "      <td>15980</td>\n",
       "      <td>8500</td>\n",
       "      <td>20000</td>\n",
       "      <td>5003</td>\n",
       "      <td>3047</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29996</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3502</td>\n",
       "      <td>8979</td>\n",
       "      <td>5190</td>\n",
       "      <td>0</td>\n",
       "      <td>1837</td>\n",
       "      <td>3526</td>\n",
       "      <td>8998</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29997</td>\n",
       "      <td>30000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2758</td>\n",
       "      <td>20878</td>\n",
       "      <td>20582</td>\n",
       "      <td>19357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22000</td>\n",
       "      <td>4200</td>\n",
       "      <td>2000</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29998</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76304</td>\n",
       "      <td>52774</td>\n",
       "      <td>11855</td>\n",
       "      <td>48944</td>\n",
       "      <td>85900</td>\n",
       "      <td>3409</td>\n",
       "      <td>1178</td>\n",
       "      <td>1926</td>\n",
       "      <td>52964</td>\n",
       "      <td>1804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29999</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49764</td>\n",
       "      <td>36535</td>\n",
       "      <td>32428</td>\n",
       "      <td>15313</td>\n",
       "      <td>2078</td>\n",
       "      <td>1800</td>\n",
       "      <td>1430</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0          20000    2          2         1   24      2      2     -1     -1   \n",
       "1         120000    2          2         2   26     -1      2      0      0   \n",
       "2          90000    2          2         2   34      0      0      0      0   \n",
       "3          50000    2          2         1   37      0      0      0      0   \n",
       "4          50000    1          2         1   57     -1      0     -1      0   \n",
       "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
       "29995     220000    1          3         1   39      0      0      0      0   \n",
       "29996     150000    1          3         2   43     -1     -1     -1     -1   \n",
       "29997      30000    1          2         2   37      4      3      2     -1   \n",
       "29998      80000    1          3         1   41      1     -1      0      0   \n",
       "29999      50000    1          2         1   46      0      0      0      0   \n",
       "\n",
       "       PAY_5  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "0         -2  ...        689          0          0          0         0   \n",
       "1          0  ...       2682       3272       3455       3261         0   \n",
       "2          0  ...      13559      14331      14948      15549      1518   \n",
       "3          0  ...      49291      28314      28959      29547      2000   \n",
       "4          0  ...      35835      20940      19146      19131      2000   \n",
       "...      ...  ...        ...        ...        ...        ...       ...   \n",
       "29995      0  ...     208365      88004      31237      15980      8500   \n",
       "29996      0  ...       3502       8979       5190          0      1837   \n",
       "29997      0  ...       2758      20878      20582      19357         0   \n",
       "29998      0  ...      76304      52774      11855      48944     85900   \n",
       "29999      0  ...      49764      36535      32428      15313      2078   \n",
       "\n",
       "       PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
       "0           689         0         0         0         0  \n",
       "1          1000      1000      1000         0      2000  \n",
       "2          1500      1000      1000      1000      5000  \n",
       "3          2019      1200      1100      1069      1000  \n",
       "4         36681     10000      9000       689       679  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "29995     20000      5003      3047      5000      1000  \n",
       "29996      3526      8998       129         0         0  \n",
       "29997         0     22000      4200      2000      3100  \n",
       "29998      3409      1178      1926     52964      1804  \n",
       "29999      1800      1430      1000      1000      1000  \n",
       "\n",
       "[30000 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,1:24]\n",
    "y = df.iloc[:,-1]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimum number of features: 12\n",
      "Score with 12 features: 0.126327\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression\n",
    "nof_list=np.arange(1,13)            \n",
    "high_score=0\n",
    "#Variable to store the optimum features\n",
    "nof=0           \n",
    "score_list =[]\n",
    "for n in range(len(nof_list)):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.333, random_state = 0)\n",
    "    model = LinearRegression()\n",
    "    rfe = RFE(model,nof_list[n])\n",
    "    X_train_rfe = rfe.fit_transform(X_train,y_train)\n",
    "    X_test_rfe = rfe.transform(X_test)\n",
    "    model.fit(X_train_rfe,y_train)\n",
    "    score = model.score(X_test_rfe,y_test)\n",
    "    score_list.append(score)\n",
    "    if(score>high_score):\n",
    "        high_score = score\n",
    "        nof = nof_list[n]\n",
    "print(\"Optimum number of features: %d\" %nof)\n",
    "print(\"Score with %d features: %f\" % (nof, high_score)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3',\n",
      "       'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'PAY_AMT1'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cols = list(X.columns)\n",
    "model = LinearRegression()\n",
    "#Initializing RFE model\n",
    "rfe = RFE(model, 12)             \n",
    "#Transforming data using RFE\n",
    "X_rfe = rfe.fit_transform(X,y)  \n",
    "#Fitting the data to model\n",
    "model.fit(X_rfe,y)              \n",
    "temp = pd.Series(rfe.support_,index = cols)\n",
    "selected_features_rfe = temp[temp==True].index\n",
    "print(selected_features_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection\n",
    "<font color = \"blue\"><p style = \"font-size:20px\">One Hot Encoding for Featured Categorical Attributes</p></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "featured_df = df.loc[:, [\"SEX\",\"EDUCATION\", \"MARRIAGE\", \"AGE\", \"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\", \"BILL_AMT1\", \"PAY_AMT1\", \"default payment next month\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SEX</th>\n",
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       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>default payment next month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>1</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>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>29239</td>\n",
       "      <td>1518</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>8617</td>\n",
       "      <td>2000</td>\n",
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       "      <td>29995</td>\n",
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       "      <td>29996</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  \\\n",
       "0        2          2         1   24      2      2     -1     -1     -2   \n",
       "1        2          2         2   26     -1      2      0      0      0   \n",
       "2        2          2         2   34      0      0      0      0      0   \n",
       "3        2          2         1   37      0      0      0      0      0   \n",
       "4        1          2         1   57     -1      0     -1      0      0   \n",
       "...    ...        ...       ...  ...    ...    ...    ...    ...    ...   \n",
       "29995    1          3         1   39      0      0      0      0      0   \n",
       "29996    1          3         2   43     -1     -1     -1     -1      0   \n",
       "29997    1          2         2   37      4      3      2     -1      0   \n",
       "29998    1          3         1   41      1     -1      0      0      0   \n",
       "29999    1          2         1   46      0      0      0      0      0   \n",
       "\n",
       "       PAY_6  BILL_AMT1  PAY_AMT1  default payment next month  \n",
       "0         -2       3913         0                           1  \n",
       "1          2       2682         0                           1  \n",
       "2          0      29239      1518                           0  \n",
       "3          0      46990      2000                           0  \n",
       "4          0       8617      2000                           0  \n",
       "...      ...        ...       ...                         ...  \n",
       "29995      0     188948      8500                           0  \n",
       "29996      0       1683      1837                           0  \n",
       "29997      0       3565         0                           1  \n",
       "29998     -1      -1645     85900                           1  \n",
       "29999      0      47929      2078                           1  \n",
       "\n",
       "[30000 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featured_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>EDU_GRAD</th>\n",
       "      <th>EDU_UNI</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29995</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>29996</td>\n",
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       "    <tr>\n",
       "      <td>29997</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29998</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       F  M  EDU_GRAD  EDU_UNI  EDU_HIGH  EDU_OTHERS  MARRIAGE_MARRIED  \\\n",
       "0      0  1         0        1         0           0                 1   \n",
       "1      0  1         0        1         0           0                 0   \n",
       "2      0  1         0        1         0           0                 0   \n",
       "3      0  1         0        1         0           0                 1   \n",
       "4      1  0         0        1         0           0                 1   \n",
       "...   .. ..       ...      ...       ...         ...               ...   \n",
       "29995  1  0         0        0         1           0                 1   \n",
       "29996  1  0         0        0         1           0                 0   \n",
       "29997  1  0         0        1         0           0                 0   \n",
       "29998  1  0         0        0         1           0                 1   \n",
       "29999  1  0         0        1         0           0                 1   \n",
       "\n",
       "       MARRIAGE_SINGLE  MARRIAGE_OTHERS  \n",
       "0                    0                0  \n",
       "1                    1                0  \n",
       "2                    1                0  \n",
       "3                    0                0  \n",
       "4                    0                0  \n",
       "...                ...              ...  \n",
       "29995                0                0  \n",
       "29996                1                0  \n",
       "29997                1                0  \n",
       "29998                0                0  \n",
       "29999                0                0  \n",
       "\n",
       "[30000 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehot = OneHotEncoder(dtype = np.int, sparse = True)\n",
    "nominals = pd.DataFrame(onehot.fit_transform(featured_df[['SEX', 'EDUCATION', 'MARRIAGE']]).toarray(),\n",
    "                                columns = ['F','M', 'EDU_GRAD', 'EDU_UNI', 'EDU_HIGH', 'EDU_OTHERS', 'MARRIAGE_MARRIED', 'MARRIAGE_SINGLE', 'MARRIAGE_OTHERS'\n",
    "                                         ])\n",
    "nominals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>F</th>\n",
       "      <th>M</th>\n",
       "      <th>EDU_GRAD</th>\n",
       "      <th>EDU_UNI</th>\n",
       "      <th>EDU_HIGH</th>\n",
       "      <th>EDU_OTHERS</th>\n",
       "      <th>MARRIAGE_MARRIED</th>\n",
       "      <th>MARRIAGE_SINGLE</th>\n",
       "      <th>MARRIAGE_OTHERS</th>\n",
       "      <th>PAY_0_0</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_6_5</th>\n",
       "      <th>PAY_6_6</th>\n",
       "      <th>PAY_6_7</th>\n",
       "      <th>PAY_6_8</th>\n",
       "      <th>PAY_6_9</th>\n",
       "      <th>PAY_6_10</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
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       "      <td>34</td>\n",
       "      <td>29239</td>\n",
       "      <td>1518</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-1645</td>\n",
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       "    <tr>\n",
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       "      <td>47929</td>\n",
       "      <td>2078</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       F  M  EDU_GRAD  EDU_UNI  EDU_HIGH  EDU_OTHERS  MARRIAGE_MARRIED  \\\n",
       "0      0  1         0        1         0           0                 1   \n",
       "1      0  1         0        1         0           0                 0   \n",
       "2      0  1         0        1         0           0                 0   \n",
       "3      0  1         0        1         0           0                 1   \n",
       "4      1  0         0        1         0           0                 1   \n",
       "...   .. ..       ...      ...       ...         ...               ...   \n",
       "29995  1  0         0        0         1           0                 1   \n",
       "29996  1  0         0        0         1           0                 0   \n",
       "29997  1  0         0        1         0           0                 0   \n",
       "29998  1  0         0        0         1           0                 1   \n",
       "29999  1  0         0        1         0           0                 1   \n",
       "\n",
       "       MARRIAGE_SINGLE  MARRIAGE_OTHERS  PAY_0_0  ...  PAY_6_5  PAY_6_6  \\\n",
       "0                    0                0        0  ...        0        0   \n",
       "1                    1                0        0  ...        0        0   \n",
       "2                    1                0        0  ...        0        0   \n",
       "3                    0                0        0  ...        0        0   \n",
       "4                    0                0        0  ...        0        0   \n",
       "...                ...              ...      ...  ...      ...      ...   \n",
       "29995                0                0        0  ...        0        0   \n",
       "29996                1                0        0  ...        0        0   \n",
       "29997                1                0        0  ...        0        0   \n",
       "29998                0                0        0  ...        0        0   \n",
       "29999                0                0        0  ...        0        0   \n",
       "\n",
       "       PAY_6_7  PAY_6_8  PAY_6_9  PAY_6_10  AGE  BILL_AMT1  PAY_AMT1  \\\n",
       "0            0        0        0         0   24       3913         0   \n",
       "1            0        0        0         0   26       2682         0   \n",
       "2            0        0        0         0   34      29239      1518   \n",
       "3            0        0        0         0   37      46990      2000   \n",
       "4            0        0        0         0   57       8617      2000   \n",
       "...        ...      ...      ...       ...  ...        ...       ...   \n",
       "29995        0        0        0         0   39     188948      8500   \n",
       "29996        0        0        0         0   43       1683      1837   \n",
       "29997        0        0        0         0   37       3565         0   \n",
       "29998        0        0        0         0   41      -1645     85900   \n",
       "29999        0        0        0         0   46      47929      2078   \n",
       "\n",
       "       default payment next month  \n",
       "0                               1  \n",
       "1                               1  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  \n",
       "...                           ...  \n",
       "29995                           0  \n",
       "29996                           0  \n",
       "29997                           1  \n",
       "29998                           1  \n",
       "29999                           1  \n",
       "\n",
       "[30000 rows x 77 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_df = featured_df.iloc[:,4:10]\n",
    "pay_col = pay_df.columns\n",
    "for i in range(len(pay_col)):\n",
    "    pay_df[pay_col[i]] = pay_df[pay_col[i]].apply(lambda x : x+2)\n",
    "\n",
    "nominals_pay0 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_0']]).toarray(),\n",
    "                                columns = ['PAY_0_0', 'PAY_0_1', 'PAY_0_2', 'PAY_0_3', 'PAY_0_4', 'PAY_0_5',\n",
    "                                          'PAY_0_6', 'PAY_0_7','PAY_0_8', 'PAY_0_9', 'PAY_0_10'])\n",
    "nominals_pay2 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_2']]).toarray(),\n",
    "                                columns = ['PAY_2_0', 'PAY_2_1', 'PAY_2_2', 'PAY_2_3', 'PAY_2_4', 'PAY_2_5',\n",
    "                                          'PAY_2_6', 'PAY_2_7','PAY_2_8', 'PAY_2_9', 'PAY_2_10'])\n",
    "nominals_pay3 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_3']]).toarray(),\n",
    "                                columns = ['PAY_3_0', 'PAY_3_1', 'PAY_3_2', 'PAY_3_3', 'PAY_3_4', 'PAY_3_5',\n",
    "                                          'PAY_3_6', 'PAY_3_7','PAY_3_8', 'PAY_3_9', 'PAY_3_10'])\n",
    "nominals_pay4 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_4']]).toarray(),\n",
    "                                columns = ['PAY_4_0', 'PAY_4_1', 'PAY_4_2', 'PAY_4_3', 'PAY_4_4', 'PAY_4_5',\n",
    "                                          'PAY_4_6', 'PAY_4_7','PAY_4_8', 'PAY_4_9', 'PAY_4_10'])\n",
    "nominals_pay5 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_5']]).toarray(),\n",
    "                                columns = ['PAY_5_0', 'PAY_5_1', 'PAY_5_2', 'PAY_5_4', 'PAY_5_5',\n",
    "                                          'PAY_5_6', 'PAY_5_7','PAY_5_8', 'PAY_5_9', 'PAY_5_10'])\n",
    "nominals_pay6 = pd.DataFrame(onehot.fit_transform(pay_df[['PAY_6']]).toarray(),\n",
    "                                columns = ['PAY_6_0', 'PAY_6_1', 'PAY_6_2', 'PAY_6_4', 'PAY_6_5',\n",
    "                                          'PAY_6_6', 'PAY_6_7','PAY_6_8', 'PAY_6_9', 'PAY_6_10'])\n",
    "nominals = pd.concat([nominals, nominals_pay0, nominals_pay2, nominals_pay3, nominals_pay4, nominals_pay5, nominals_pay6], axis = 1)\n",
    "\n",
    "nominals_cont = featured_df.loc[:, ['AGE', 'BILL_AMT1', 'PAY_AMT1', 'default payment next month']]\n",
    "nominals = pd.concat([nominals, nominals_cont], axis = 1)\n",
    "nominals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"><p style = \"font-size:20px\">SVM</p></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_featured = nominals.iloc[:,0:76]\n",
    "y_featured = nominals.iloc[:,-1]\n",
    "\n",
    "X_train_featured,X_test_featured,y_train_featured,y_test_featured = train_test_split(X_featured,y_featured, test_size = 0.333, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>F</th>\n",
       "      <th>M</th>\n",
       "      <th>EDU_GRAD</th>\n",
       "      <th>EDU_UNI</th>\n",
       "      <th>EDU_HIGH</th>\n",
       "      <th>EDU_OTHERS</th>\n",
       "      <th>MARRIAGE_MARRIED</th>\n",
       "      <th>MARRIAGE_SINGLE</th>\n",
       "      <th>MARRIAGE_OTHERS</th>\n",
       "      <th>PAY_0_0</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_6_4</th>\n",
       "      <th>PAY_6_5</th>\n",
       "      <th>PAY_6_6</th>\n",
       "      <th>PAY_6_7</th>\n",
       "      <th>PAY_6_8</th>\n",
       "      <th>PAY_6_9</th>\n",
       "      <th>PAY_6_10</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2682</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>29239</td>\n",
       "      <td>1518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>46990</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8617</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>29995</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
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       "      <td>29996</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1683</td>\n",
       "      <td>1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29997</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>3565</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29998</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>-1645</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>29999</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>47929</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       F  M  EDU_GRAD  EDU_UNI  EDU_HIGH  EDU_OTHERS  MARRIAGE_MARRIED  \\\n",
       "0      0  1         0        1         0           0                 1   \n",
       "1      0  1         0        1         0           0                 0   \n",
       "2      0  1         0        1         0           0                 0   \n",
       "3      0  1         0        1         0           0                 1   \n",
       "4      1  0         0        1         0           0                 1   \n",
       "...   .. ..       ...      ...       ...         ...               ...   \n",
       "29995  1  0         0        0         1           0                 1   \n",
       "29996  1  0         0        0         1           0                 0   \n",
       "29997  1  0         0        1         0           0                 0   \n",
       "29998  1  0         0        0         1           0                 1   \n",
       "29999  1  0         0        1         0           0                 1   \n",
       "\n",
       "       MARRIAGE_SINGLE  MARRIAGE_OTHERS  PAY_0_0  ...  PAY_6_4  PAY_6_5  \\\n",
       "0                    0                0        0  ...        0        0   \n",
       "1                    1                0        0  ...        1        0   \n",
       "2                    1                0        0  ...        0        0   \n",
       "3                    0                0        0  ...        0        0   \n",
       "4                    0                0        0  ...        0        0   \n",
       "...                ...              ...      ...  ...      ...      ...   \n",
       "29995                0                0        0  ...        0        0   \n",
       "29996                1                0        0  ...        0        0   \n",
       "29997                1                0        0  ...        0        0   \n",
       "29998                0                0        0  ...        0        0   \n",
       "29999                0                0        0  ...        0        0   \n",
       "\n",
       "       PAY_6_6  PAY_6_7  PAY_6_8  PAY_6_9  PAY_6_10  AGE  BILL_AMT1  PAY_AMT1  \n",
       "0            0        0        0        0         0   24       3913         0  \n",
       "1            0        0        0        0         0   26       2682         0  \n",
       "2            0        0        0        0         0   34      29239      1518  \n",
       "3            0        0        0        0         0   37      46990      2000  \n",
       "4            0        0        0        0         0   57       8617      2000  \n",
       "...        ...      ...      ...      ...       ...  ...        ...       ...  \n",
       "29995        0        0        0        0         0   39     188948      8500  \n",
       "29996        0        0        0        0         0   43       1683      1837  \n",
       "29997        0        0        0        0         0   37       3565         0  \n",
       "29998        0        0        0        0         0   41      -1645     85900  \n",
       "29999        0        0        0        0         0   46      47929      2078  \n",
       "\n",
       "[30000 rows x 76 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_featured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ff63d0a48>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "scv_featured = svm.SVC(kernel = 'rbf', random_state = 0, gamma = 'auto')\n",
    "scv_featured.fit(X_train_featured, y_train_featured)\n",
    "y_pred_featured = scv_featured.predict(X_test_featured)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test_featured, y_pred_featured)\n",
    "sns.heatmap(cm,annot= True,linewidths=1,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7717,   39],\n",
       "       [2193,   41]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "values = precision_recall_fscore_support(y_test_featured, y_pred_featured,average='weighted',warn_for=tuple())\n",
    "roc=roc_auc_score(y_test_featured, y_pred_featured)\n",
    "acc = accuracy_score(y_test_featured, y_pred_featured)\n",
    "precision = values[0]\n",
    "recall = values[1]\n",
    "f1score = values[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.68621</td>\n",
       "      <td>0.506662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model  Accuracy  Precision    Recall  F1 Score       ROC\n",
       "0  SVM Tuned  0.776577   0.719178  0.776577   0.68621  0.506662"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_results = pd.DataFrame([['SVM Tuned', acc, precision, recall, f1score, roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OneHotEncoder previously assumed that the input features take on values in the range [0, max(values)).\n",
    "This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels.\n",
    "\n",
    "We based our research on this documentation which states that One Hot Encoding is preferable for SVM:\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.506662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.351051</td>\n",
       "      <td>0.237483</td>\n",
       "      <td>0.910316</td>\n",
       "      <td>0.376695</td>\n",
       "      <td>0.553908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model  Accuracy  Precision    Recall  F1 Score       ROC\n",
       "0             SVM Tuned  0.776577   0.719178  0.776577  0.686210  0.506662\n",
       "1  Gaussian Naive Bayes  0.351051   0.237483  0.910316  0.376695  0.553908"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,1:24]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.333, random_state = 0)\n",
    "\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "naive_bayes = GaussianNB()\n",
    "naive_bayes.fit(X_train,y_train)\n",
    "\n",
    "y_pred = naive_bayes.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "results_nb = pd.DataFrame([['Gaussian Naive Bayes', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_nb, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.351051</td>\n",
       "      <td>0.237483</td>\n",
       "      <td>0.910316</td>\n",
       "      <td>0.376695</td>\n",
       "      <td>0.553908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaussian Naive Bayes Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.794063</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.791575</td>\n",
       "      <td>0.650849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.615573</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.689878</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.615573</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.689878</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gaussian Naive Bayes Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0                   SVM Tuned  0.776076   0.719178  0.776577  0.686210   \n",
       "1        Gaussian Naive Bayes  0.351051   0.237483  0.910316  0.376695   \n",
       "2  Gaussian Naive Bayes Tuned  0.776076   0.451613  0.006267  0.012362   \n",
       "3    Random Forest Classifier  0.812913   0.794063  0.812913  0.791575   \n",
       "4         Logistic Regression  0.776376   0.615573  0.784585  0.689878   \n",
       "5         Logistic Regression  0.784585   0.615573  0.784585  0.689878   \n",
       "6  Gaussian Naive Bayes Tuned  0.776076   0.451613  0.006267  0.012362   \n",
       "\n",
       "        ROC  \n",
       "0  0.502037  \n",
       "1  0.553908  \n",
       "2  0.502037  \n",
       "3  0.650849  \n",
       "4  0.500000  \n",
       "5  0.500000  \n",
       "6  0.502037  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "naive_bayes = GaussianNB()\n",
    "naive_bayes.fit(X_train_featured,y_train_featured)\n",
    "\n",
    "y_pred = naive_bayes.predict(X_test_featured)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test_featured, y_pred)\n",
    "cm\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test_featured, y_pred)\n",
    "acc = accuracy_score(y_test_featured, y_pred)\n",
    "prec = precision_score(y_test_featured, y_pred)\n",
    "rec = recall_score(y_test_featured, y_pred)\n",
    "f1 = f1_score(y_test_featured, y_pred)\n",
    "\n",
    "results_nbtuned = pd.DataFrame([['Gaussian Naive Bayes Tuned', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_nbtuned, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"><p style = \"font-size:20px\">Logistic Regression</p></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.351051</td>\n",
       "      <td>0.237483</td>\n",
       "      <td>0.910316</td>\n",
       "      <td>0.376695</td>\n",
       "      <td>0.553908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaussian Naive Bayes Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.794063</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.791575</td>\n",
       "      <td>0.650849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.615573</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.689878</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.615573</td>\n",
       "      <td>0.784585</td>\n",
       "      <td>0.689878</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0                   SVM Tuned  0.776076   0.719178  0.776577  0.686210   \n",
       "1        Gaussian Naive Bayes  0.351051   0.237483  0.910316  0.376695   \n",
       "2  Gaussian Naive Bayes Tuned  0.776076   0.451613  0.006267  0.012362   \n",
       "3    Random Forest Classifier  0.812913   0.794063  0.812913  0.791575   \n",
       "4         Logistic Regression  0.776376   0.615573  0.784585  0.689878   \n",
       "5         Logistic Regression  0.784585   0.615573  0.784585  0.689878   \n",
       "\n",
       "        ROC  \n",
       "0  0.502037  \n",
       "1  0.553908  \n",
       "2  0.502037  \n",
       "3  0.650849  \n",
       "4  0.500000  \n",
       "5  0.500000  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X = df.iloc[:,1:24].values\n",
    "y = df.iloc[:,-1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.333, random_state = 0)\n",
    "\n",
    "logmodel = LogisticRegression(random_state=1, solver = 'liblinear')\n",
    "logmodel.fit(X_train,y_train)\n",
    "y_pred = logmodel.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "values = precision_recall_fscore_support(y_test, y_pred,average='weighted',warn_for=tuple())\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "precision = values[0]\n",
    "recall = values[1]\n",
    "f1score = values[2]\n",
    "\n",
    "results_lr = pd.DataFrame([['Logistic Regression', acc,precision,recall, f1score,roc]], columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_lr, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Original</td>\n",
       "      <td>0.773473</td>\n",
       "      <td>0.663331</td>\n",
       "      <td>0.773473</td>\n",
       "      <td>0.680329</td>\n",
       "      <td>0.500839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.506662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.602760</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.678640</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression Tuned</td>\n",
       "      <td>0.804905</td>\n",
       "      <td>0.790130</td>\n",
       "      <td>0.804905</td>\n",
       "      <td>0.760602</td>\n",
       "      <td>0.593266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0               SVM Original  0.773473   0.663331  0.773473  0.680329   \n",
       "1                  SVM Tuned  0.776577   0.719178  0.776577  0.686210   \n",
       "2        Logistic Regression  0.776376   0.602760  0.776376  0.678640   \n",
       "3  Logistic Regression Tuned  0.804905   0.790130  0.804905  0.760602   \n",
       "\n",
       "        ROC  \n",
       "0  0.500839  \n",
       "1  0.506662  \n",
       "2  0.500000  \n",
       "3  0.593266  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "sel_ = SelectFromModel(LogisticRegression(solver = 'liblinear'))\n",
    "sel_.fit(X_train, y_train)\n",
    "sel_.get_support()\n",
    "X_train_selected = sel_.transform(X_train)\n",
    "X_test_selected = sel_.transform(X_test)\n",
    "\n",
    "logmodel = LogisticRegression(random_state=1, solver = 'liblinear')\n",
    "logmodel.fit(X_train_selected,y_train)\n",
    "y_pred_selected = logmodel.predict(X_test_selected)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred_selected)\n",
    "cm\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "values = precision_recall_fscore_support(y_test, y_pred_selected,average='weighted',warn_for=tuple())\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_selected)\n",
    "acc = accuracy_score(y_test, y_pred_selected)\n",
    "precision = values[0]\n",
    "recall = values[1]\n",
    "f1score = values[2]\n",
    "\n",
    "results_lrtuned = pd.DataFrame([['Logistic Regression Tuned', acc,precision,recall, f1score,roc]], columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_lrtuned, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"><p style = \"font-size:20px\">Random Forest</p></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.351051</td>\n",
       "      <td>0.237483</td>\n",
       "      <td>0.910316</td>\n",
       "      <td>0.376695</td>\n",
       "      <td>0.553908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaussian Naive Bayes Tuned</td>\n",
       "      <td>0.776076</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.502037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.794063</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.791575</td>\n",
       "      <td>0.650849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0                   SVM Tuned  0.776076   0.719178  0.776577  0.686210   \n",
       "1        Gaussian Naive Bayes  0.351051   0.237483  0.910316  0.376695   \n",
       "2  Gaussian Naive Bayes Tuned  0.776076   0.451613  0.006267  0.012362   \n",
       "3    Random Forest Classifier  0.812913   0.794063  0.812913  0.791575   \n",
       "\n",
       "        ROC  \n",
       "0  0.502037  \n",
       "1  0.553908  \n",
       "2  0.502037  \n",
       "3  0.650849  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X = df.iloc[:,1:24].values\n",
    "y = df.iloc[:,-1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.333, random_state = 0)\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = 100,criterion = 'entropy',random_state = 0)\n",
    "rfc.fit(X_train,y_train)\n",
    "y_pred = rfc.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "values = precision_recall_fscore_support(y_test, y_pred,average='weighted',warn_for=tuple())\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test_original, y_pred)\n",
    "acc = accuracy_score(y_test_original, y_pred)\n",
    "precision = values[0]\n",
    "recall = values[1]\n",
    "f1score = values[2]\n",
    "\n",
    "results_rf = pd.DataFrame([['Random Forest Classifier', acc,precision,recall, f1score,roc]], columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_rf, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "Index(['LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'BILL_AMT1', 'BILL_AMT2',\n",
      "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
      "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT5', 'PAY_AMT6'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Original</td>\n",
       "      <td>0.773473</td>\n",
       "      <td>0.663331</td>\n",
       "      <td>0.773473</td>\n",
       "      <td>0.680329</td>\n",
       "      <td>0.500839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM Tuned</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.719178</td>\n",
       "      <td>0.776577</td>\n",
       "      <td>0.686210</td>\n",
       "      <td>0.506662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.602760</td>\n",
       "      <td>0.776376</td>\n",
       "      <td>0.678640</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression Tuned</td>\n",
       "      <td>0.804905</td>\n",
       "      <td>0.790130</td>\n",
       "      <td>0.804905</td>\n",
       "      <td>0.760602</td>\n",
       "      <td>0.593266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.794063</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.791575</td>\n",
       "      <td>0.650849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Random Forest Classifier Tuned</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.796608</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.793196</td>\n",
       "      <td>0.652233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0                    SVM Original  0.773473   0.663331  0.773473  0.680329   \n",
       "1                       SVM Tuned  0.776577   0.719178  0.776577  0.686210   \n",
       "2             Logistic Regression  0.776376   0.602760  0.776376  0.678640   \n",
       "3       Logistic Regression Tuned  0.804905   0.790130  0.804905  0.760602   \n",
       "4        Random Forest Classifier  0.812913   0.794063  0.812913  0.791575   \n",
       "5  Random Forest Classifier Tuned  0.814815   0.796608  0.814815  0.793196   \n",
       "\n",
       "        ROC  \n",
       "0  0.500839  \n",
       "1  0.506662  \n",
       "2  0.500000  \n",
       "3  0.593266  \n",
       "4  0.650849  \n",
       "5  0.652233  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = SelectFromModel(RandomForestClassifier(n_estimators = 100))\n",
    "sel.fit(X_train, y_train)\n",
    "sel.get_support()\n",
    "print(sel.get_support().sum())\n",
    "print(X_train.columns[(sel.get_support())])\n",
    "rfc_important = RandomForestClassifier(n_estimators = 100,criterion = 'entropy',random_state = 0)\n",
    "\n",
    "X_important_train = sel.transform(X_train)\n",
    "X_important_test = sel.transform(X_test)\n",
    "\n",
    "rfc_important.fit(X_important_train, y_train)\n",
    "\n",
    "y_important_pred = rfc_important.predict(X_important_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_important_pred)\n",
    "cm\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "values = precision_recall_fscore_support(y_test, y_important_pred,average='weighted',warn_for=tuple())\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_important_pred)\n",
    "acc = accuracy_score(y_test, y_important_pred)\n",
    "precision = values[0]\n",
    "recall = values[1]\n",
    "f1score = values[2]\n",
    "\n",
    "results_rftuned = pd.DataFrame([['Random Forest Classifier Tuned', acc,precision,recall, f1score,roc]], columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "model_results = model_results.append(results_rftuned, ignore_index = True)\n",
    "model_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the performance of the models, we decided to use Accuracy and F1 score since in this business problem about credit default, identifying false positives and false negatives is important. This is to ensure we do not incorrectly identify a customer as having a default payment in the following month when they are not supposed to or incorrectly predict that they do not have default payment next month when they are supposed to. Both these scenarios could lead to losses acquired by both the customers and the company itself. Accuracy is a ratio of correctly predicted observation to the total observations, while F1 score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account and would be a good measure of the model.\n",
    "\n",
    "The accuracy generated using the Random Forest model is the highest, followed by Logistic Regression and SVM. \n",
    "\n",
    "This is consistent with the F1 score and ROC as well. Hence, we can conclude that Random Forest is the best suited model for this business problem as it results in the largest accuracy and F1 score among all the models.\n",
    "\n",
    "We see that there is an improvement in the accuracy of the models when only the important features are selected, so it is possible that with fewer attributes, it makes the model more generalisable and thus has higher accuracy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion for Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>1) Chi-square test could have used for feature selection:</b><br />\n",
    "We can conduct feature selection by using Chi-Square for categorical variables and correlation matrix for continuous variables. From these features, we can conduct another round of model selection and see if the the features selected varies and would produce a better result.\n",
    "\n",
    "<b>2) More models for model selection could have incorporated  -  kNN Models, Stochastic Gradient, Decision trees:</b><br />\n",
    "This would have given us a wider variety of models to choose from for the purpose of simplifying and evaluating this business problem. This could have allowed us to choose a better model with higher performance level.\n",
    "\n",
    "<b>3) KS statistic could have been used to evaluate the models:</b><br />\n",
    "We decided to use ROC, F1 score and Accuracy due to simplicity and effectiveness. Moreover, ROC can clearly illustrate the diagnostic ability of a binary classifier system. However, KS statistic could have served as a good measure for evaluation as well to show how well the model discriminates between the two classes. \n",
    "\n",
    "<b>4) Penalties for each wrongly classified data could have been introduced using a cost/slack variable for SVM :</b><br />\n",
    "The cost parameter for SVM would be useful in the case that incorrect predictions into one target class have more serious consequences than incorrect predictions into the other class, as in our case with the credit defaults. Therefore, the two types of incorrect predictions (incorrectly classifying as having a default payment in the following month and incorrectly classifying not having a default payment in the following month) should be weighted differently. \n",
    "In this prediction model, the cost of  false negative and a false positive is the same. However, in reality a false negative (wrongly predicting as not default but actually default) has a greater cost than a false positive (wrongly predicting as default but actually not default) and hence the cost parameter given to the false negative prediction will be higher.\n"
   ]
  }
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