Project_Group3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>\n",
    "    \n",
    "<p>\n",
    "    \n",
    "<center>\n",
    "<font size=\"6\">\n",
    "Machine Learning Final project\n",
    "</font>\n",
    "</center>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "<center>\n",
    "<font size=\"5\">\n",
    "Home credit default risk\n",
    "</font>\n",
    "</center>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "<center>\n",
    "<font size=\"3\">\n",
    "Data Science, Columbian College of Arts & Sciences, George Washington University\n",
    "</font>\n",
    "</center>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "<center>\n",
    "<font size=\"3\">\n",
    "Author: Danlei Qian, Hung-chun Lin, Chen Chen\n",
    "</font>\n",
    "</center>\n",
    "    \n",
    "</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\">\n",
    "    <li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li>\n",
    "    <li><span><a href=\"#Data-preprocessing\" data-toc-modified-id=\"Data-preprocessing-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data preprocessing</a></span></li>\n",
    "    <li><span><a href=\"#Model-Training\" data-toc-modified-id=\"Model-Training-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Model Training</a></span><li><span><a href=\"#Model-Selection\" data-toc-modified-id=\"Model Selection-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Model Selection</a></span><li><span><a href=\"#Feature-Importance\" data-toc-modified-id=\"Feature-Importance-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Feature Importance</a></span><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Conclusion</a></span></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Home Credit B.V. is an international non-bank financial institution founded in 1997 in the Czech Republic and operates in 14 countries and focuses on lending primarily to people with little or no credit history. Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to predict the repayment\n",
    "ability of their customers, Home Credit held a Kaggle competition to see what sort of models the machine learning community can develop to help them in this task.\n",
    "\n",
    "The objective of this project is to use historical loan application data to predict whether or not an applicant will be able to repay a loan. In this project, we will explore several models including Classification Models and Regression Models to predict the TARGET variable and Grid Search with combination of Pipeline for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data source: https://www.kaggle.com/c/home-credit-default-risk\n",
    "\n",
    "Since the raw dataset is too complex, we only use a part of the whole dataset - the application_train data(the main data with information about each loan application at Home Credit.) And every loan has its own row and is identified by the feature SK_ID_CURR(which is useless and we will drop it later). \n",
    "\n",
    "The data comes with the TARGET indicating 0: the loan was repaid or 1: the loan was not repaid.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#data preprocessing packages\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "#model training packages\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "#Model selection packages\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has 307511 observations (each one a separate loan) and 122 features (variables) including the TARGET (the label we want to predict)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 122)\n",
      "(48744, 121)\n"
     ]
    }
   ],
   "source": [
    "application = pd.read_csv(\"application_train.csv\", header = 0)\n",
    "application_predict = pd.read_csv(\"application_test.csv\", header = 0)\n",
    "\n",
    "print(application.shape)\n",
    "print(application_predict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
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       "<p>5 rows × 122 columns</p>\n",
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      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "      <td>0</td>\n",
       "      <td>100001</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>20560.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>100005</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100013</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>100028</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>49018.5</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>100038</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>32067.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      100001         Cash loans           F            N               Y   \n",
       "1      100005         Cash loans           M            N               Y   \n",
       "2      100013         Cash loans           M            Y               Y   \n",
       "3      100028         Cash loans           F            N               Y   \n",
       "4      100038         Cash loans           M            Y               N   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          135000.0    568800.0      20560.5         450000.0   \n",
       "1             0           99000.0    222768.0      17370.0         180000.0   \n",
       "2             0          202500.0    663264.0      69777.0         630000.0   \n",
       "3             2          315000.0   1575000.0      49018.5        1575000.0   \n",
       "4             1          180000.0    625500.0      32067.0         625500.0   \n",
       "\n",
       "   ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                0                0                0                0   \n",
       "1  ...                0                0                0                0   \n",
       "2  ...                0                0                0                0   \n",
       "3  ...                0                0                0                0   \n",
       "4  ...                0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         0.0  \n",
       "1                        0.0                         3.0  \n",
       "2                        1.0                         4.0  \n",
       "3                        0.0                         3.0  \n",
       "4                        NaN                         NaN  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_predict.head()\n",
    "#there is no target value in this data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove feature 'ID' from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we drop the useless feature SK_ID_CURR(ID of loan in our sample)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 121)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application = application.drop(columns=[\"SK_ID_CURR\"])\n",
    "application.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with the missing value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can look at the number and percentage of missing values in each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The missing value checker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_value_checker(df):\n",
    "    \"\"\"\n",
    "    The missing value checker\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : dataframe\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    The variables with missing value and their proportion of missing value\n",
    "    \"\"\"\n",
    "    \n",
    "    variable_proportion = [[variable, df[variable].isna().sum() / df.shape[0]] \n",
    "                           for variable in df.columns \n",
    "                           if df[variable].isna().sum() > 0]\n",
    "\n",
    "    print('%-30s' % 'Variable with missing values', 'Proportion of missing values')\n",
    "    for variable, proportion in sorted(variable_proportion, key=lambda x : x[1]):\n",
    "        print('%-30s' % variable, proportion)\n",
    "        \n",
    "    return variable_proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify variables with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable with missing values   Proportion of missing values\n",
      "DAYS_LAST_PHONE_CHANGE         3.2519161916159095e-06\n",
      "CNT_FAM_MEMBERS                6.503832383231819e-06\n",
      "AMT_ANNUITY                    3.9022994299390916e-05\n",
      "AMT_GOODS_PRICE                0.0009040327012692228\n",
      "EXT_SOURCE_2                   0.0021462646864665006\n",
      "OBS_30_CNT_SOCIAL_CIRCLE       0.0033202064316398437\n",
      "DEF_30_CNT_SOCIAL_CIRCLE       0.0033202064316398437\n",
      "OBS_60_CNT_SOCIAL_CIRCLE       0.0033202064316398437\n",
      "DEF_60_CNT_SOCIAL_CIRCLE       0.0033202064316398437\n",
      "NAME_TYPE_SUITE                0.004201475719567756\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR     0.13501630835970097\n",
      "AMT_REQ_CREDIT_BUREAU_DAY      0.13501630835970097\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK     0.13501630835970097\n",
      "AMT_REQ_CREDIT_BUREAU_MON      0.13501630835970097\n",
      "AMT_REQ_CREDIT_BUREAU_QRT      0.13501630835970097\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR     0.13501630835970097\n",
      "EXT_SOURCE_3                   0.19825307062186392\n",
      "OCCUPATION_TYPE                0.31345545362604915\n",
      "EMERGENCYSTATE_MODE            0.47398304450897694\n",
      "TOTALAREA_MODE                 0.4826851722377411\n",
      "YEARS_BEGINEXPLUATATION_AVG    0.48781019215572774\n",
      "YEARS_BEGINEXPLUATATION_MODE   0.48781019215572774\n",
      "YEARS_BEGINEXPLUATATION_MEDI   0.48781019215572774\n",
      "FLOORSMAX_AVG                  0.4976082156410665\n",
      "FLOORSMAX_MODE                 0.4976082156410665\n",
      "FLOORSMAX_MEDI                 0.4976082156410665\n",
      "HOUSETYPE_MODE                 0.50176091261776\n",
      "LIVINGAREA_AVG                 0.5019332641759157\n",
      "LIVINGAREA_MODE                0.5019332641759157\n",
      "LIVINGAREA_MEDI                0.5019332641759157\n",
      "ENTRANCES_AVG                  0.503487680115508\n",
      "ENTRANCES_MODE                 0.503487680115508\n",
      "ENTRANCES_MEDI                 0.503487680115508\n",
      "APARTMENTS_AVG                 0.5074972927797705\n",
      "APARTMENTS_MODE                0.5074972927797705\n",
      "APARTMENTS_MEDI                0.5074972927797705\n",
      "WALLSMATERIAL_MODE             0.508407829313423\n",
      "ELEVATORS_AVG                  0.532959796560123\n",
      "ELEVATORS_MODE                 0.532959796560123\n",
      "ELEVATORS_MEDI                 0.532959796560123\n",
      "NONLIVINGAREA_AVG              0.5517916432257708\n",
      "NONLIVINGAREA_MODE             0.5517916432257708\n",
      "NONLIVINGAREA_MEDI             0.5517916432257708\n",
      "EXT_SOURCE_1                   0.5638107254699832\n",
      "BASEMENTAREA_AVG               0.5851595552679416\n",
      "BASEMENTAREA_MODE              0.5851595552679416\n",
      "BASEMENTAREA_MEDI              0.5851595552679416\n",
      "LANDAREA_AVG                   0.5937673774271489\n",
      "LANDAREA_MODE                  0.5937673774271489\n",
      "LANDAREA_MEDI                  0.5937673774271489\n",
      "OWN_CAR_AGE                    0.6599081008484249\n",
      "YEARS_BUILD_AVG                0.6649778381911542\n",
      "YEARS_BUILD_MODE               0.6649778381911542\n",
      "YEARS_BUILD_MEDI               0.6649778381911542\n",
      "FLOORSMIN_AVG                  0.6784862980511266\n",
      "FLOORSMIN_MODE                 0.6784862980511266\n",
      "FLOORSMIN_MEDI                 0.6784862980511266\n",
      "LIVINGAPARTMENTS_AVG           0.6835495315614726\n",
      "LIVINGAPARTMENTS_MODE          0.6835495315614726\n",
      "LIVINGAPARTMENTS_MEDI          0.6835495315614726\n",
      "FONDKAPREMONT_MODE             0.6838617155158677\n",
      "NONLIVINGAPARTMENTS_AVG        0.6943296337366793\n",
      "NONLIVINGAPARTMENTS_MODE       0.6943296337366793\n",
      "NONLIVINGAPARTMENTS_MEDI       0.6943296337366793\n",
      "COMMONAREA_AVG                 0.6987229725115525\n",
      "COMMONAREA_MODE                0.6987229725115525\n",
      "COMMONAREA_MEDI                0.6987229725115525\n"
     ]
    }
   ],
   "source": [
    "variable_proportion = pd.DataFrame(missing_value_checker(application))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OWN_CAR_AGE',\n",
       " 'EXT_SOURCE_1',\n",
       " 'APARTMENTS_AVG',\n",
       " 'BASEMENTAREA_AVG',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'ELEVATORS_AVG',\n",
       " 'ENTRANCES_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LANDAREA_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'LIVINGAREA_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAREA_AVG',\n",
       " 'APARTMENTS_MODE',\n",
       " 'BASEMENTAREA_MODE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE',\n",
       " 'ENTRANCES_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LANDAREA_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'NONLIVINGAREA_MODE',\n",
       " 'APARTMENTS_MEDI',\n",
       " 'BASEMENTAREA_MEDI',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'ELEVATORS_MEDI',\n",
       " 'ENTRANCES_MEDI',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'LANDAREA_MEDI',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'LIVINGAREA_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'HOUSETYPE_MODE',\n",
       " 'WALLSMATERIAL_MODE']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the column name with missing value over 50%\n",
    "variable_proportion= variable_proportion.rename(columns={0:'Features',1:'Proportion'})\n",
    "drop_colomns = variable_proportion.loc[variable_proportion.Proportion > 0.5].iloc[:,0].tolist()\n",
    "drop_colomns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the columns which missing proportion > 50%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we drop the features with over 50% missing values and then we have 80 columns left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns before removing columns with missing values over 50%: 121\n",
      "Number of columns after removing columns with missing values over 50%: 80\n"
     ]
    }
   ],
   "source": [
    "print('Number of columns before removing columns with missing values over 50%:', str(application.shape[1]))\n",
    "#drop the column with missing value over 50%\n",
    "application_new = pd.DataFrame(application.drop(columns = drop_colomns, axis = 1))\n",
    "print('Number of columns after removing columns with missing values over 50%:', str(application_new.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the rows with missing value less than 50%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with the left missing values, here we simply drop the rows with missing values. Now, the data has 84575 observations (each one a separate loan) and 80 features (variables) including the TARGET (the label we want to predict)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before removing rows with missing values less than 50%: 307511\n",
      "Number of rows after removing rows with missing values less than 50%: 84575\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows before removing rows with missing values less than 50%:', str(application_new.shape[0]))\n",
    "\n",
    "# Remove rows with np.NaN\n",
    "application_new = application_new.dropna()\n",
    "\n",
    "print('Number of rows after removing rows with missing values less than 50%:', str(application_new.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify variables with missing values again\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we don’t have any missing values any more in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable with missing values   Proportion of missing values\n"
     ]
    }
   ],
   "source": [
    "variable_proportion = missing_value_checker(pd.DataFrame(application_new))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = application_new.drop(columns = [\"TARGET\"])\n",
    "y = application_new[\"TARGET\"]\n",
    "\n",
    "feature_value_names = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling categorical features and target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the machine learning models we will use below unfortunately cannot deal with categorical variables, we need to find a way to encode (represent) these variables as numbers before handing them off to the model. Here we use one-hot-encoding method.\n",
    "\n",
    "Let's now look at the number of unique value in each of the categorical columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The categorical feature checker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorical_feature_checker(df, dtype):\n",
    "    \"\"\"\n",
    "    The categorical feature checker\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : dataframe\n",
    "    target : the target\n",
    "    dtype : the type of the feature\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    The categorical features and their number of unique value\n",
    "    \"\"\"\n",
    "    \n",
    "    feature_number = [[feature, df[feature].nunique()] \n",
    "                      for feature in df.columns \n",
    "                      if df[feature].dtype.name == dtype]\n",
    "    \n",
    "    print('%-30s' % 'Categorical feature', 'Number of unique value')\n",
    "    for feature, number in sorted(feature_number, key=lambda x : x[1]):\n",
    "        print('%-30s' % feature, number)\n",
    "    \n",
    "    return feature_number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical feature            Number of unique value\n",
      "NAME_CONTRACT_TYPE             2\n",
      "FLAG_OWN_CAR                   2\n",
      "FLAG_OWN_REALTY                2\n",
      "EMERGENCYSTATE_MODE            2\n",
      "CODE_GENDER                    3\n",
      "NAME_INCOME_TYPE               5\n",
      "NAME_EDUCATION_TYPE            5\n",
      "NAME_FAMILY_STATUS             5\n",
      "NAME_HOUSING_TYPE              6\n",
      "NAME_TYPE_SUITE                7\n",
      "WEEKDAY_APPR_PROCESS_START     7\n",
      "OCCUPATION_TYPE                18\n",
      "ORGANIZATION_TYPE              57\n"
     ]
    }
   ],
   "source": [
    "feature_number = categorical_feature_checker(X,\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The categorical features\n",
    "categorical_features = [feature for feature, number in feature_number]\n",
    "\n",
    "# Encode the categorical features using One-hot-encoding\n",
    "X = pd.get_dummies(data=X, columns=categorical_features)\n",
    "\n",
    "feature_value_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>...</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 5</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 6</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 7</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 4</th>\n",
       "      <th>ORGANIZATION_TYPE_University</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>-3648.0</td>\n",
       "      <td>-2120</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>80865.0</td>\n",
       "      <td>5881.5</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>0.031329</td>\n",
       "      <td>-13439</td>\n",
       "      <td>-2717</td>\n",
       "      <td>-311.0</td>\n",
       "      <td>-3227</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>918468.0</td>\n",
       "      <td>28966.5</td>\n",
       "      <td>697500.0</td>\n",
       "      <td>0.016612</td>\n",
       "      <td>-14086</td>\n",
       "      <td>-3028</td>\n",
       "      <td>-643.0</td>\n",
       "      <td>-4911</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>7875.0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>0.046220</td>\n",
       "      <td>-17718</td>\n",
       "      <td>-7804</td>\n",
       "      <td>-8751.0</td>\n",
       "      <td>-1259</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>497520.0</td>\n",
       "      <td>32521.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>0.020713</td>\n",
       "      <td>-11146</td>\n",
       "      <td>-4306</td>\n",
       "      <td>-114.0</td>\n",
       "      <td>-2518</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 187 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0              0          202500.0    406597.5      24700.5         351000.0   \n",
       "12             0           67500.0     80865.0       5881.5          67500.0   \n",
       "13             1          225000.0    918468.0      28966.5         697500.0   \n",
       "18             0          112500.0    157500.0       7875.0         157500.0   \n",
       "22             1          450000.0    497520.0      32521.5         450000.0   \n",
       "\n",
       "    REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  \\\n",
       "0                     0.018801       -9461           -637            -3648.0   \n",
       "12                    0.031329      -13439          -2717             -311.0   \n",
       "13                    0.016612      -14086          -3028             -643.0   \n",
       "18                    0.046220      -17718          -7804            -8751.0   \n",
       "22                    0.020713      -11146          -4306             -114.0   \n",
       "\n",
       "    DAYS_ID_PUBLISH  ...  ORGANIZATION_TYPE_Trade: type 5  \\\n",
       "0             -2120  ...                                0   \n",
       "12            -3227  ...                                0   \n",
       "13            -4911  ...                                0   \n",
       "18            -1259  ...                                0   \n",
       "22            -2518  ...                                0   \n",
       "\n",
       "    ORGANIZATION_TYPE_Trade: type 6  ORGANIZATION_TYPE_Trade: type 7  \\\n",
       "0                                 0                                0   \n",
       "12                                0                                0   \n",
       "13                                0                                0   \n",
       "18                                0                                0   \n",
       "22                                0                                0   \n",
       "\n",
       "    ORGANIZATION_TYPE_Transport: type 1  ORGANIZATION_TYPE_Transport: type 2  \\\n",
       "0                                     0                                    0   \n",
       "12                                    0                                    0   \n",
       "13                                    0                                    0   \n",
       "18                                    0                                    0   \n",
       "22                                    0                                    0   \n",
       "\n",
       "    ORGANIZATION_TYPE_Transport: type 3  ORGANIZATION_TYPE_Transport: type 4  \\\n",
       "0                                     0                                    0   \n",
       "12                                    0                                    0   \n",
       "13                                    0                                    0   \n",
       "18                                    0                                    0   \n",
       "22                                    0                                    0   \n",
       "\n",
       "    ORGANIZATION_TYPE_University  EMERGENCYSTATE_MODE_No  \\\n",
       "0                              0                       1   \n",
       "12                             0                       1   \n",
       "13                             0                       1   \n",
       "18                             0                       1   \n",
       "22                             0                       1   \n",
       "\n",
       "    EMERGENCYSTATE_MODE_Yes  \n",
       "0                         0  \n",
       "12                        0  \n",
       "13                        0  \n",
       "18                        0  \n",
       "22                        0  \n",
       "\n",
       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    78299\n",
       "1     6276\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into Train and Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can split our data into traing with percentage of 80 and testing with percentage of 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#80% training set, 20% testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check categorical target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the bar chart shows, we get 62639 target'0' and 5021 target'1',the two classes are quitely imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    62639\n",
       "1     5021\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the unique value and their number for the target\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Over sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most machine learning algorithms work best when the number of samples in each class are about equal, we need to deal with the imbalanced problem. Here we use over sampling method.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwgBVHXVU9sqr+TVX9VlXdWFXfNY0/t6oee+SWCADAw1m7kklTwL0zyclJPpDk+5KcMG3+gSTPTrLpSCwQAICHt9IjdVck+UySs5L8YJKa2/aHmUUeAABLsqIjdUn+aZIf6e5dVbVmj22fSXLa4V0WAAAHYqVH6r6U5FH72PatST53eJYDAMDBWGnUbUny0qr6prmxrqpHJnlxkncc9pUBALBiKz39+p+S/EmSbUluStJJfjbJ45N8Q5IfOSKrAwBgRVZ0pK67707yxCRXJ/nuJH+Z2UUTNyR5SnfvOFILBADg4a30SF26+6+TvPQIrgUAgIPkFyUAAAaw0i8fvjezz9HtU3d/62FZEQAAB2ylp1+vyt5R9+gkz0jy9UmuOZyLAgDgwKwo6rr7ZYvGq+oRSf53ki8ezkUBAHBgDukzdd391ST/I8m/OzzLAQDgYByOCyW+Pclxh2E/AAAcpJVeKLF5wfBxSR6X5OIkv384FwUAwIFZ6YUSv7lg7KEkn87s9OvPH7YVAQBwwFYadY/cc6C7v3KY1wIAwEFa6dWvAg4A4Ci2z6irqvMOZEfd/c5DXw4AAAdjf0fq3pHZFw7XCvbTSdYclhUBAHDA9hd161dtFQAAHJJ9Rl13/8VqLgQAgIO30qtfk/z/nwU7I8nX7bmtu//8cC0KAIADs9IvH16b5DVJXpjk+H1M85k6AIAlWenPhL0syb9I8qLMLpx4SZLNSf4wyaeSPOdILA4AgJVZadQ9P8kvJHnD9PiW7r6qu5+R5JYkzzoSiwMAYGVWGnVnJvn49CXEDyY5cW7btUmed7gXBgDAyq006j6T5Jun+59K8k/mtn1HVvZddgAAHCErvfr1vUm+L8nbklyV5FVV9Z1JvpTkx5Jcf0RWBwDAiuzvZ8Ie2d1/Pz18WZJ10/1fyewI33MzuxL2N5NcfiQXCQDA/u3vSN1nqurNSd6Y5L3dvSNJuruT/PJ0AwDgKLC/z9S9Mcmzk7wryaer6jVVdc7qLAsAgAOxz6jr7hcnOT3J+UluTPKCJLdU1V1V9YtV9YRVWiMAAA9jv1e/dvdXu/td3f2TSf5RkguT/HGSn0ryZ1X10ap6aVV9x0perKq+rqr+tKr+rKpur6pfmMYfU1Xvq6o7q+r3quq4afxR0+Nt0/az5vb10mn8E1V1/tz4xmlsW1VddoD/HgAAx6SVfqVJuvuh7n5bd78gySmZfTfdx5O8IslKf/f1S0me0d1PTPI9STZW1blJXp3kNd29Psn9SS6Z5l+S5P7u/q7Mfqbs1UlSVWcnuSjJ45NsTPIbVbWmqtYkeW2SC5KcneT501wAgKGtOOr28KQk35/ke6d93L2SJ/XMF6aHj5xuneQZSd40jV+Tf/jZsQunx5m2P7Oqahq/rru/1N2fTLItyTnTbVt339XdX05y3TQXAGBoK466qnpSVb26qj6Z2SnYH80stJ7e3Ss6/TrtZ01VfSjJfUm2JPmLJJ/r7oemKdsz+yxfpr/3JLMjhUkeSPIt8+N7PGdf44vWsbmqtlbV1p07d650+QAAR6X9fvlwVT0us9OcP5pkfWZR9ZbMrox9d3d/9UBfcPqpse+pqhOnfT1u0bTdS9jHtn2NL4rUXjCW7r4yyZVJsmHDhoVzAACOFfv78uEPZ/aZtb/L7JckfibJ26fTmoesuz9XVe9Ncm6SE6tq7XQ07owkO6Zp2zP73dntVbU2s58q2zU3vtv8c/Y1DgAwrP2dfv3LzL7G5NTuvqi733qoQVdV66YjdKmq45P8YJI7krwns1+oSJJNSd463b9hepxp+7unLz++IclF09Wxj8nsKOKfJrktyfrpatrjMjvKeMOhrBkA4FiwzyN13f3sI/B6pyW5ZrpK9RFJru/ut1XVx5JcV1W/mOSDmf2+bKa/v1NV2zI7QnfRtLbbq+r6JB9L8lCSS6fTuqmqFye5KcmaJFd39+1H4H0AABxV9vuZusOtuz+c2ZWze47fldmVq3uOP5jZV6cs2tcrk7xywfiNmX1ZMgDA14yD/UoTAACOIqIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACiDgBgAKIOAGAAog4AYACrGnVVdWZVvaeq7qiq26vqJdP4o6tqS1XdOf09aRqvqrqiqrZV1Yer6slz+9o0zb+zqjbNjT+lqj4yPeeKqqrVfI8AAMuw2kfqHkryH7r7cUnOTXJpVZ2d5LIkN3f3+iQ3T4+T5IIk66fb5iSvS2YRmOTyJE9Nck6Sy3eH4DRn89zzNq7C+wIAWKpVjbruvre7PzDd/3ySO5KcnuTCJNdM065J8pzp/oVJru2ZW5OcWFWnJTk/yZbu3tXd9yfZkmTjtO2E7r6luzvJtXP7AgAY1tI+U1dVZyV5UpL3JTm1u+9NZuGX5JRp2ulJ7pl72vZpbH/j2xeMAwAMbSlRV1XfmOTNSX66u/9mf1MXjPVBjC9aw+aq2lpVW3fu3PlwSwYAOKqtetRV1SMzC7rf7e7fn4Y/O506zfT3vml8e5Iz555+RpIdDzN+xoLxvXT3ld29obs3rFu37tDeFADAkq321a+V5Kokd3T3r8xtuiHJ7itYNyV569z4xdNVsOcmeWA6PXtTkvOq6qTpAonzktw0bft8VZ07vdbFc/sCABjW2lV+vacneUGSj1TVh6ax/5LkVUmur6pLktyd5HnTthuTPCvJtiRfTPLCJOnuXVX1iiS3TfNe3t27pvsvSvL6JMcneft0AwAY2qpGXXf/3yz+3FuSPHPB/E5y6T72dXWSqxeMb03yhENYJgDAMccvSgAADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxA1AEADEDUAQAMQNQBAAxgVaOuqq6uqvuq6qNzY4+uqi1Vdef096RpvKrqiqraVlUfrqonzz1n0zT/zqraNDf+lKr6yPScK6qqVvP9AQAsy2ofqXt9ko17jF2W5ObuXp/k5ulxklyQZP1025zkdcksApNcnuSpSc5JcvnuEJzmbJ573p6vBQAwpFWNuu7+oyS79hi+MMk10/1rkjxnbvzanrk1yYlVdVqS85Ns6e5d3X1/ki1JNk7bTujuW7q7k1w7ty8AgKEdDZ+pO7W7702S6e8p0/jpSe6Zm7d9Gtvf+PYF4wAAwzsaom5fFn0erg9ifPHOqzZX1daq2rpz586DXCIAwNHhaIi6z06nTjP9vW8a357kzLl5ZyTZ8TDjZywYX6i7r+zuDd29Yd26dYf8JgAAluloiLobkuy+gnVTkrfOjV88XQV7bpIHptOzNyU5r6pOmi6QOC/JTdO2z1fVudNVrxfP7QsAYGhrV/PFquqNSf5ZkpOrantmV7G+Ksn1VXVJkruTPG+afmOSZyXZluSLSV6YJN29q6pekeS2ad7Lu3v3xRcvyuwK2+OTvH26AQAMb1Wjrrufv49Nz1wwt5Ncuo/9XJ3k6gXjW5M84VDWCABwLDoaTr8CAHCIRB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAARB0AwABEHQDAAEQdAMAA1i57AQB87fj9T9y77CVwDPmXjz1t2Us4pjhSBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwAFEHADAAUQcAMABRBwAwgCGjrqo2VtUnqmpbVV227PUAABxpw0VdVa1J8tokFyQ5O8nzq+rs5a4KAODIGi7qkpyTZFt339XdX05yXZILl7wmAIAjasSoOz3JPXOPt09jAADDWrvsBRwBtWCs95pUtTnJ5unhF6rqE0d0VYzi5CR/texFHG3q55e9Ajjm+b+FA/HtiwZHjLrtSc6ce3xGkh17TuruK5NcuVqLYgxVtbW7Nyx7HcBY/N/C4TDi6dfbkqyvqsdU1XFJLkpyw5LXBABwRA13pK67H6qqFye5KcmaJFd39+1LXhYAwBE1XNQlSXffmOTGZa+DITllDxwJ/m/hkFX3XtcQAABwjBnxM3UAAF9zRB2skJ+fAw63qrq6qu6rqo8uey0c+0QdrICfnwOOkNcn2bjsRTAGUQcr4+fngMOuu/8oya5lr4MxiDpYGT8/B8BRTdTByqzo5+cAYFlEHazMin5+DgCWRdTByvj5OQCOaqIOVqC7H0qy++fn7khyvZ+fAw5VVb0xyS1JHltV26vqkmWviWOXX5QAABiAI3UAAAMQdQAAAxB1AAADEHUAAAMQdQAAAxB1AAADEHUAAAMQdQAAA/h/3OV4rBFrci4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#application_new.ix[:,'TARGET'].value_counts().plot(kind='bar',figsize=(10 ,8),width=0.5,colors=('steelblue','lightblue'))\n",
    "y_train.value_counts().plot(kind='bar',figsize=(10 ,8),width=0.5,colors=('steelblue','lightblue'))\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.title('Target',fontsize=18)\n",
    "plt.ylabel(\"Value\",fontsize=15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    62639\n",
       "0    62639\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomOverSampler (with random_state=0)\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_train, y_train = ros.fit_sample(X_train, y_train)\n",
    "\n",
    "pd.DataFrame(data=y_train, columns=[\"TARGET\"])[\"TARGET\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125278, 187)\n",
      "(125278,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we first use the combination of Pipeline and GridSearchCV to fine tune the hyperparameters of 6 classifiers. For our training result, the scoring we choose to evaluate the model is f1 score, f1 score can convey the balance between the precision and the recall.\n",
    "\n",
    "\n",
    "\\begin{equation*}\n",
    "F1 = \n",
    "\\frac{2(Recall)(Precision)}{(Recall)+(Precision)}\\end{equation*}\n",
    "\n",
    "\n",
    "In the training procedure, we also use cross-validation in the model training, the data is trained in 10 folds, so that we can get a more accurate result. After finished all the steps, we use confusion matrix, AUC and ROC to test the model to make sure the model could be used without low predicting power or overfitting ... etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For logistic regression, the hyperparameters we fine tune are: <font color=\"grey\">*multi_class, solver and C.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_range = [10 ** i for i in range(-4, 5)]\n",
    "\n",
    "param_grid = [{'Logisticregression__multi_class': ['ovr'],\n",
    "               'Logisticregression__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "               'Logisticregression__C': C_range},\n",
    "              \n",
    "              {'Logisticregression__multi_class': ['multinomial'],\n",
    "               'Logisticregression__solver': ['newton-cg', 'lbfgs', 'sag', 'saga'],\n",
    "               'Logisticregression__C': C_range}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.6907899722968173\n",
      "std:            0.0037082992431903125\n",
      "best_params:    {'Logisticregression__C': 0.01, 'Logisticregression__multi_class': 'multinomial', 'Logisticregression__solver': 'newton-cg'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr_pipe = Pipeline([('StandardScaler', StandardScaler()), \n",
    "                    ('Logisticregression', LogisticRegression())])\n",
    "\n",
    "lr_gs = GridSearchCV(estimator=lr_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "lr_fitted = lr_gs.fit(X_train, y_train)\n",
    "    \n",
    "\n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', lr_fitted.best_score_)\n",
    "print('%-15s' % 'std:', lr_fitted.cv_results_[\"std_test_score\"][lr_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), lr_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = lr_fitted.best_estimator_.predict(X_test)\n",
    "#make the confusion matrix for test data\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred,\n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.6822388606493567\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "lr_probas = lr_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, lr_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilayer perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For multilayer perceptron, the hyperparameters we fine tune are: <font color=\"darkblue\">*hidden_layer_sizes and activation.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'MLPClassifier__hidden_layer_sizes': [10, 50, 100],\n",
    "               'MLPClassifier__activation': ['identity', 'logistic', 'tanh', 'relu']}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.9606790554970879\n",
      "std:            0.0015194402708749969\n",
      "best_params:    {'MLPClassifier__activation': 'logistic', 'MLPClassifier__hidden_layer_sizes': 100}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mlp_pipe = Pipeline([('StandardScaler', StandardScaler()),\n",
    "                      (\"MLPClassifier\",MLPClassifier())])\n",
    "\n",
    "mlp_gs = GridSearchCV(estimator=mlp_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "mlp_fitted = mlp_gs.fit(X_train, y_train)\n",
    "    \n",
    "\n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', mlp_fitted.best_score_)\n",
    "print('%-15s' % 'std:', mlp_fitted.cv_results_[\"std_test_score\"][mlp_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), mlp_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = mlp_fitted.best_estimator_.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.5415891987605136\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "mlp_probas = mlp_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, mlp_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Decision tree, the hyperparameters we fine tune are: <font color=\"grey\">*max_depth and min_samples_split.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth = range(10,91,20)\n",
    "min_samples_split = range(10,91,20)\n",
    "\n",
    "# Implement me\n",
    "# Hint: you should use the above eta_range and C_range\n",
    "param_grid = [{'DecisionTreeClassifier__max_depth':max_depth, \n",
    "               'DecisionTreeClassifier__min_samples_split':min_samples_split}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.9584954795763723\n",
      "std:            0.0019912512014958693\n",
      "best_params:    {'DecisionTreeClassifier__max_depth': 70, 'DecisionTreeClassifier__min_samples_split': 10}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tree_pipe = Pipeline([('StandardScaler', StandardScaler()),\n",
    "                      (\"DecisionTreeClassifier\", DecisionTreeClassifier(criterion = \"gini\"))])\n",
    "\n",
    "tree_gs = GridSearchCV(estimator = tree_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "tree_fitted = tree_gs.fit(X_train, y_train)\n",
    "    \n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', tree_fitted.best_score_)\n",
    "print('%-15s' % 'std:', tree_fitted.cv_results_[\"std_test_score\"][tree_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), tree_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = tree_fitted.best_estimator_.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred,\n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.5433119374354434\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "tree_probas = tree_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test,tree_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Random forest, the hyperparameters we fine tune are: <font color=\"grey\">*n_estimators, max_depth and max_leaf_nodes.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Param for Random Forest\n",
    "n_estimators = range(50,111,20)\n",
    "max_depth = range(50,131,20)\n",
    "max_leaf_nodes = range(50, 131, 20)\n",
    "\n",
    "# Implement me\n",
    "# Hint: you should use the above eta_range and C_range\n",
    "param_grid = [{\"RandomForestClassifier__n_estimators\":n_estimators,\n",
    "               \"RandomForestClassifier__max_depth\":max_depth, \n",
    "               \"RandomForestClassifier__max_leaf_nodes\":max_leaf_nodes}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.7466884505962368\n",
      "std:            0.004639937736989228\n",
      "best_params:    {'RandomForestClassifier__max_depth': 50, 'RandomForestClassifier__max_leaf_nodes': 130, 'RandomForestClassifier__n_estimators': 110}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_pipe = Pipeline([('StandardScaler', StandardScaler()),\n",
    "                      (\"RandomForestClassifier\", RandomForestClassifier(criterion = \"gini\"))])\n",
    "\n",
    "rf_gs = GridSearchCV(estimator = rf_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "rf_fitted = rf_gs.fit(X_train, y_train)\n",
    "\n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', rf_fitted.best_score_)\n",
    "print('%-15s' % 'std:', rf_fitted.cv_results_[\"std_test_score\"][rf_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), rf_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = rf_fitted.best_estimator_.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.6721457719568724\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "rf_probas = rf_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, rf_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For XGboost, the hyperparameters we fine tune are: <font color=\"grey\">*eta, gamma and lambda.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'XGBClassifier__eta': [10 ** i for i in range(-4, 1)],\n",
    "               'XGBClassifier__gamma': [0, 10, 100],\n",
    "               'XGBClassifier__lambda': [10 ** i for i in range(-4, 5)]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.7034882821082507\n",
      "std:            0.0034843677632812853\n",
      "best_params:    {'XGBClassifier__eta': 0.0001, 'XGBClassifier__gamma': 10, 'XGBClassifier__lambda': 0.0001}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xg_pipe = Pipeline([('StandardScaler', StandardScaler()),\n",
    "                      (\"XGBClassifier\",XGBClassifier())])\n",
    "\n",
    "xg_gs = GridSearchCV(estimator = xg_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "xg_fitted = xg_gs.fit(X_train, y_train)\n",
    "\n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', xg_fitted.best_score_)\n",
    "print('%-15s' % 'std:', xg_fitted.cv_results_[\"std_test_score\"][xg_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), xg_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = xg_fitted.best_estimator_.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.6854457012308366\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "xg_probas = xg_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, xg_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For KNN, the hyperparameters we fine tune is: <font color=\"grey\">*n_neighbors.*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'KNeighborsClassifier__n_neighbors': list(range(1, 11))}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score:     0.9640380974647565\n",
      "std:            0.0022469094759983727\n",
      "best_params:    {'KNeighborsClassifier__n_neighbors': 1}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn_pipe = Pipeline([('StandardScaler', StandardScaler()),\n",
    "                      (\"KNeighborsClassifier\",KNeighborsClassifier())])\n",
    "\n",
    "knn_gs = GridSearchCV(estimator = knn_pipe,\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='f1',\n",
    "                  n_jobs=-1,\n",
    "                  cv=StratifiedKFold(n_splits=10,shuffle=True,random_state=0))\n",
    "\n",
    "knn_fitted = knn_gs.fit(X_train, y_train)\n",
    "\n",
    "# Print the best settings\n",
    "print('%-15s' % 'best_score:', knn_fitted.best_score_)\n",
    "print('%-15s' % 'std:', knn_fitted.cv_results_[\"std_test_score\"][knn_fitted.best_index_])\n",
    "print('%-15s' % 'best_params:'.format(20), knn_fitted.best_params_, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#predict the target use test features\n",
    "y_pred = knn_fitted.best_estimator_.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    normalize = True,\n",
    "    text_fontsize = 20,\n",
    "    title_fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score: 0.5209762482636504\n"
     ]
    },
    {
     "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": [
    "#compute the auc\n",
    "print(\"AUC score:\" , roc_auc_score(y_test, y_pred))\n",
    "#make the ROC curves\n",
    "knn_probas = knn_fitted.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, knn_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='rank.png',width=600, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " In the analysis of the model training, we get a very high training score over 90% in the model multilayer perceptron, decision tree and KNN. However, when we use test data set to check the model it shows very low score, only 50%. So it is obvouis that these three models are not good.\n",
    " \n",
    " Compare the other models, we can see that Logistic regression have the lowest false negative score which means the applicant not make the repayment but predicted as make the payment. As a loan issuer, they definitely want to lower this score to avoid default risk. So we can conclude that the best model for our data is <font color=\"red\"> Logistic regression</font>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, we use the random forest to generate the important features of our model. As the plot shows below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the importances into one-dimensional 1darray with corresponding df column names as axis labels\n",
    "f_importances = pd.Series(rf_fitted.best_estimator_.named_steps['RandomForestClassifier'].feature_importances_, feature_value_names)\n",
    "\n",
    "# Sort the array in descending order of the importances\n",
    "f_importances = f_importances.sort_values(ascending=False)[:10]\n",
    "\n",
    "# Draw the bar Plot from f_importances \n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "f_importances.plot(x='Features', y='Importance', kind='barh', rot=0, fontsize=20, color = \"darkorange\")\n",
    "ax.invert_yaxis()\n",
    "plt.title('Feature Importance', fontsize=35)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='models.png',width=1000, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use the Random forest classifier to find out the most important features for our model. \n",
    "From the plot, we can see that, the top 2 important features are external_sources_3 and external_sources_2, these two features are far more ahead of other features, but the data provider does not give us any infortmation about these two features.\n",
    "\n",
    "Look at the other features, we can see, employment status can prove whether a loan customer have income and the ability to repay the loan. Age is an important factor. Old and young people's ability to repay the loan may be lower than middle-aged people's ability, due to income levels. The loan amount that an applicant applies for is also important. If the goods value is high or credit amount of the loan is high, then that applicant may be likely to default. The frequncy of changing phone and IDs can also indicate the possibility of loan fraud (Loan fraud refers to the providing of false information when applying for or receiving a loan.). Loan annuity is also a kind of proof that you have enough money to make the repayment.\n",
    "\n",
    "In a word, whether an applicant can repay a loan is mainly based on whether he has enough money or whether he has possible to be a fraudulent customer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, we trained 6 models to select the best one to predict whether a applicant can make the repayment of the loan. And we find Logistic regression is the best model to make the prediction. \n",
    "Using Random Forest we find the top important features. Age, income, loan amount applied and loan fraud are key factors to predict whether an applicant can make the loan repayment. \n",
    "For further improvement, we think more precise variable selection before training the model may help with our result. "
   ]
  }
 ],
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