02_Creditworthiness_Data_Analysis-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Creditworthiness (Part2)\n",
    "\n",
    "### by Sooyeon Won\n",
    "### Keywords\n",
    "- Analytical Framework\n",
    "- Predictive Classification Models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Model, AdaBoost) \n",
    "- Model Training and Validation\n",
    "- Model Comparisions\n",
    "- Model Selection "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#Recall\">1. Reviews of Part1</a></li>\n",
    "<li><a href=\"#Analysis\">2. Data Analysis</a></li>\n",
    "<li><a href=\"#Selection\">3. Model Selection</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Recall'></a>\n",
    "## 1. Reviews of Part1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the first section of the project Part2, I will briefly review the cleaned dataset from the project Part1 (check,  01_Creditworthiness_Data_Cleaning_Exploration.ipynb). Then for the logistic regression analysis, I create the dummy variables for each categorical variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_Application_Result</th>\n",
       "      <th>Account_Balance</th>\n",
       "      <th>Duration_of_Credit_Month</th>\n",
       "      <th>Payment_Status_of_Previous_Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit_Amount</th>\n",
       "      <th>Value_Savings_Stocks</th>\n",
       "      <th>Length_of_current_employment</th>\n",
       "      <th>Instalment_per_cent</th>\n",
       "      <th>Most_valuable_available_asset</th>\n",
       "      <th>Age_years</th>\n",
       "      <th>Type_of_apartment</th>\n",
       "      <th>No_of_Credits_at_this_Bank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank) or Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank) or Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank) or Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>longer than 1 yrs employment</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank) or Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>longer than 1 yrs employment</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank) or Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  Credit_Application_Result Account_Balance  Duration_of_Credit_Month  \\\n",
       "0              Creditworthy    Some Balance                         4   \n",
       "1              Creditworthy    Some Balance                         4   \n",
       "2              Creditworthy    Some Balance                         4   \n",
       "3              Creditworthy    Some Balance                         4   \n",
       "4              Creditworthy      No Account                         6   \n",
       "\n",
       "       Payment_Status_of_Previous_Credit       Purpose  Credit_Amount  \\\n",
       "0  No Problems (in this bank) or Paid Up         Other           1494   \n",
       "1  No Problems (in this bank) or Paid Up  Home Related           1494   \n",
       "2  No Problems (in this bank) or Paid Up  Home Related           1544   \n",
       "3  No Problems (in this bank) or Paid Up  Home Related           3380   \n",
       "4  No Problems (in this bank) or Paid Up  Home Related            343   \n",
       "\n",
       "  Value_Savings_Stocks  Length_of_current_employment  Instalment_per_cent  \\\n",
       "0           £100-£1000                         < 1yr                    1   \n",
       "1           £100-£1000                         < 1yr                    1   \n",
       "2                 None  longer than 1 yrs employment                    2   \n",
       "3                 None  longer than 1 yrs employment                    1   \n",
       "4                 None                         < 1yr                    4   \n",
       "\n",
       "   Most_valuable_available_asset  Age_years  Type_of_apartment  \\\n",
       "0                              1         33                  2   \n",
       "1                              1         29                  2   \n",
       "2                              1         42                  2   \n",
       "3                              1         37                  2   \n",
       "4                              1         27                  2   \n",
       "\n",
       "  No_of_Credits_at_this_Bank  \n",
       "0                          1  \n",
       "1                          1  \n",
       "2                More than 1  \n",
       "3                          1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data \n",
    "raw_data = pd.read_excel('credit-data-training-cleaned.xlsx')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                                                   Non-Null Count  Dtype\n",
      "---  ------                                                                   --------------  -----\n",
      " 0   Duration_of_Credit_Month                                                 500 non-null    int64\n",
      " 1   Credit_Amount                                                            500 non-null    int64\n",
      " 2   Instalment_per_cent                                                      500 non-null    int64\n",
      " 3   Most_valuable_available_asset                                            500 non-null    int64\n",
      " 4   Age_years                                                                500 non-null    int64\n",
      " 5   Type_of_apartment                                                        500 non-null    int64\n",
      " 6   Credit_Application_Result_Creditworthy                                   500 non-null    uint8\n",
      " 7   Credit_Application_Result_Non-Creditworthy                               500 non-null    uint8\n",
      " 8   Account_Balance_No Account                                               500 non-null    uint8\n",
      " 9   Account_Balance_Some Balance                                             500 non-null    uint8\n",
      " 10  Payment_Status_of_Previous_Credit_No Problems (in this bank) or Paid Up  500 non-null    uint8\n",
      " 11  Payment_Status_of_Previous_Credit_Some Problems                          500 non-null    uint8\n",
      " 12  Purpose_Home Related                                                     500 non-null    uint8\n",
      " 13  Purpose_New car                                                          500 non-null    uint8\n",
      " 14  Purpose_Other                                                            500 non-null    uint8\n",
      " 15  Purpose_Used car                                                         500 non-null    uint8\n",
      " 16  Value_Savings_Stocks_< £100                                              500 non-null    uint8\n",
      " 17  Value_Savings_Stocks_None                                                500 non-null    uint8\n",
      " 18  Value_Savings_Stocks_£100-£1000                                          500 non-null    uint8\n",
      " 19  Length_of_current_employment_< 1yr                                       500 non-null    uint8\n",
      " 20  Length_of_current_employment_longer than 1 yrs employment                500 non-null    uint8\n",
      " 21  No_of_Credits_at_this_Bank_1                                             500 non-null    uint8\n",
      " 22  No_of_Credits_at_this_Bank_More than 1                                   500 non-null    uint8\n",
      "dtypes: int64(6), uint8(17)\n",
      "memory usage: 31.9 KB\n"
     ]
    }
   ],
   "source": [
    "data=pd.get_dummies(raw_data)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One dummy variable is removed from each categorical variables. \n",
    "data.drop(['Credit_Application_Result_Non-Creditworthy', 'Account_Balance_No Account', \n",
    "           'Payment_Status_of_Previous_Credit_No Problems (in this bank) or Paid Up',\n",
    "          'Purpose_Other', 'Value_Savings_Stocks_None', 'Length_of_current_employment_< 1yr',\n",
    "            'No_of_Credits_at_this_Bank_1' ], axis =1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Duration_of_Credit_Month</th>\n",
       "      <th>Credit_Amount</th>\n",
       "      <th>Instalment_per_cent</th>\n",
       "      <th>Most_valuable_available_asset</th>\n",
       "      <th>Age_years</th>\n",
       "      <th>Type_of_apartment</th>\n",
       "      <th>Credit_Application_Result_Creditworthy</th>\n",
       "      <th>Account_Balance_Some Balance</th>\n",
       "      <th>Payment_Status_of_Previous_Credit_Some Problems</th>\n",
       "      <th>Purpose_Home Related</th>\n",
       "      <th>Purpose_New car</th>\n",
       "      <th>Purpose_Used car</th>\n",
       "      <th>Value_Savings_Stocks_&lt; £100</th>\n",
       "      <th>Value_Savings_Stocks_£100-£1000</th>\n",
       "      <th>Length_of_current_employment_longer than 1 yrs employment</th>\n",
       "      <th>No_of_Credits_at_this_Bank_More than 1</th>\n",
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       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1494</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1544</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Duration_of_Credit_Month  Credit_Amount  Instalment_per_cent  \\\n",
       "0                         4           1494                    1   \n",
       "1                         4           1494                    1   \n",
       "2                         4           1544                    2   \n",
       "3                         4           3380                    1   \n",
       "4                         6            343                    4   \n",
       "\n",
       "   Most_valuable_available_asset  Age_years  Type_of_apartment  \\\n",
       "0                              1         33                  2   \n",
       "1                              1         29                  2   \n",
       "2                              1         42                  2   \n",
       "3                              1         37                  2   \n",
       "4                              1         27                  2   \n",
       "\n",
       "   Credit_Application_Result_Creditworthy  Account_Balance_Some Balance  \\\n",
       "0                                       1                             1   \n",
       "1                                       1                             1   \n",
       "2                                       1                             1   \n",
       "3                                       1                             1   \n",
       "4                                       1                             0   \n",
       "\n",
       "   Payment_Status_of_Previous_Credit_Some Problems  Purpose_Home Related  \\\n",
       "0                                                0                     0   \n",
       "1                                                0                     1   \n",
       "2                                                0                     1   \n",
       "3                                                0                     1   \n",
       "4                                                0                     1   \n",
       "\n",
       "   Purpose_New car  Purpose_Used car  Value_Savings_Stocks_< £100  \\\n",
       "0                0                 0                            0   \n",
       "1                0                 0                            0   \n",
       "2                0                 0                            0   \n",
       "3                0                 0                            0   \n",
       "4                0                 0                            0   \n",
       "\n",
       "   Value_Savings_Stocks_£100-£1000  \\\n",
       "0                                1   \n",
       "1                                1   \n",
       "2                                0   \n",
       "3                                0   \n",
       "4                                0   \n",
       "\n",
       "   Length_of_current_employment_longer than 1 yrs employment  \\\n",
       "0                                                  0           \n",
       "1                                                  0           \n",
       "2                                                  1           \n",
       "3                                                  1           \n",
       "4                                                  0           \n",
       "\n",
       "   No_of_Credits_at_this_Bank_More than 1  \n",
       "0                                       0  \n",
       "1                                       0  \n",
       "2                                       1  \n",
       "3                                       0  \n",
       "4                                       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() # Data is now ready for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 16 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype\n",
      "---  ------                                                     --------------  -----\n",
      " 0   Duration_of_Credit_Month                                   500 non-null    int64\n",
      " 1   Credit_Amount                                              500 non-null    int64\n",
      " 2   Instalment_per_cent                                        500 non-null    int64\n",
      " 3   Most_valuable_available_asset                              500 non-null    int64\n",
      " 4   Age_years                                                  500 non-null    int64\n",
      " 5   Type_of_apartment                                          500 non-null    int64\n",
      " 6   Credit_Application_Result_Creditworthy                     500 non-null    uint8\n",
      " 7   Account_Balance_Some Balance                               500 non-null    uint8\n",
      " 8   Payment_Status_of_Previous_Credit_Some Problems            500 non-null    uint8\n",
      " 9   Purpose_Home Related                                       500 non-null    uint8\n",
      " 10  Purpose_New car                                            500 non-null    uint8\n",
      " 11  Purpose_Used car                                           500 non-null    uint8\n",
      " 12  Value_Savings_Stocks_< £100                                500 non-null    uint8\n",
      " 13  Value_Savings_Stocks_£100-£1000                            500 non-null    uint8\n",
      " 14  Length_of_current_employment_longer than 1 yrs employment  500 non-null    uint8\n",
      " 15  No_of_Credits_at_this_Bank_More than 1                     500 non-null    uint8\n",
      "dtypes: int64(6), uint8(10)\n",
      "memory usage: 28.4 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that the datatype of the column \"Type_of_apartment\" is 'int64', rather than 'string'. For the purpose of the analysis, I will preserve the datatype as 'int64'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Analysis'></a>\n",
    "## 2. Data Analysis\n",
    "<br>\n",
    "\n",
    "Remember that the target variable \"Credit_Application_Result_Creditworthy\" is **imbalanced**. According to the visualization (1. Credit Application Result) in Part 1 analysis, the ratio of \"Creditworthy\" to \"Non-Creditworthy\" shows is **72:28**. Unlike a balanced (also called naive) model, it is quite hard for an imbalanced classification model to correctly learn all the characteristics of the minority class given its smaller sample size and skewed class distribution. Therefore, I analyse the **Imbalanced Classifications** with different methods as follows. [Reference](https://towardsdatascience.com/how-to-effectively-predict-imbalanced-classes-in-python-e8cd3b5720c4)  <br><br>\n",
    "\n",
    "<li><a href=\"#Modeling\">i. Model Fitting & Evaluations Metrics</a></li>\n",
    "<li><a href=\"#Logistic\"> - Logistic Regression Model </a></li>\n",
    "<li><a href=\"#Decision\"> - Decision Tree Model </a></li>\n",
    "<li><a href=\"#Random\"> - Random Forest Model </a></li>\n",
    "<li><a href=\"#Gradient\"> - Gradient Boosting Model </a></li>\n",
    "<li><a href=\"#AdaBoost\"> - AdaBoost Model </a></li>\n",
    "\n",
    "> There are many kinds of Classification Model Evaluation Metrics. Among them, I conducted the model comparision using: \n",
    "- **Confusion Matrix**, **Precision**, **Recall**, **F-Score**, \n",
    "- Area Under Receiver Operating Characteristic curve (**AUROC**), \n",
    "- Area Under the Precision-Recall curve (**PR AUC**) <br>\n",
    "\n",
    ">Each model is evaluated with **Confusion Matrix**, **Precision**, **Recall**, **F-Score**, directly after model fitting. Only **AUROC** and **PR AUC** are simultaneously compared at the end with proper visualizations.\n",
    "\n",
    "\n",
    "<li><a href=\"#Cross\">ii. Cross Validation</a></li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Fitting'></a>\n",
    "### i. Model Fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I create \"Estimation\" and \"Validation\" samples where 70% of the dataset should go to the \"Estimation\" and 30% of the entire dataset should be reserved for \"Validation\". I set the random state to the number 42. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the required libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "# Evaluations\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "# Load dataset\n",
    "inputs = data.loc[:, data.columns != 'Credit_Application_Result_Creditworthy']\n",
    "target = data.loc[:, data.columns == 'Credit_Application_Result_Creditworthy']\n",
    "\n",
    "# Split into train/test sets\n",
    "train_X, test_X, train_y, test_y = train_test_split(inputs, target, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Logistic'></a>\n",
    "### i-1. Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Define and fit a model\n",
    "model_logreg = LogisticRegression(class_weight='balanced')\n",
    "model_logreg.fit(train_X, train_y)\n",
    "\n",
    "# Predict the test set results\n",
    "ypred_logreg = model_logreg.predict(test_X) # Predict class labels for samples in X.\n",
    "ypred_prob_logreg = model_logreg.predict_proba(test_X) # Probability estimates.\n",
    "\n",
    "# Retrieve the probabilities for the positive class\n",
    "yhat_positive_logreg = ypred_prob_logreg[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature Importance: Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Duration_of_Credit_Month, Score: -0.010046\n",
      "Feature: Credit_Amount, Score: -0.000094\n",
      "Feature: Instalment_per_cent, Score: -0.140528\n",
      "Feature: Most_valuable_available_asset, Score: -0.307572\n",
      "Feature: Age_years, Score: 0.013399\n",
      "Feature: Type_of_apartment, Score: 0.199608\n",
      "Feature: Account_Balance_Some Balance, Score: 1.324701\n",
      "Feature: Payment_Status_of_Previous_Credit_Some Problems, Score: -0.439987\n",
      "Feature: Purpose_Home Related, Score: -0.217495\n",
      "Feature: Purpose_New car, Score: 0.323926\n",
      "Feature: Purpose_Used car, Score: 0.035562\n",
      "Feature: Value_Savings_Stocks_< £100, Score: 0.135068\n",
      "Feature: Value_Savings_Stocks_£100-£1000, Score: 0.685272\n",
      "Feature: Length_of_current_employment_longer than 1 yrs employment, Score: 0.273252\n",
      "Feature: No_of_Credits_at_this_Bank_More than 1, Score: 0.242357\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get importance\n",
    "importance_logreg = model_logreg.coef_[0]\n",
    "\n",
    "features_logreg = dict(zip(train_X.columns, importance_logreg))\n",
    "features_sorted_logreg = sorted(features_logreg, key=features_logreg.get, reverse=True)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in zip(train_X.columns, importance_logreg):\n",
    "    print('Feature: {}, Score: {:5f}' .format(i,v))\n",
    "# plot feature importance\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.barplot(y = [x for x in train_X.columns ], x= importance_logreg , color = base_color, order = features_sorted_logreg)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the logistic regression analysis, the most important 7 predictor variables are: \n",
    "1. ‘Account.BalanceSome Balance’\n",
    "2. 'Value_Savings_Stocks_£100-£1000'\n",
    "3. 'Purpose_New car'\n",
    "4. 'Length_of_current_employment_longer than 1 yrs employment'\n",
    "5. 'No_of_Credits_at_this_Bank_More than 1'\n",
    "6. 'Type_of_apartment'\n",
    "7. 'Value_Savings_Stocks_< £100'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Logistic Regression Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[26 12]\n",
      " [36 76]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "cm_logreg = confusion_matrix(test_y, ypred_logreg)\n",
    "print(cm_logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix from Logistic Model tells that we have 26 + 76 = 102  correct predictions and 12 + 36 = 48 incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print('The Confusion Matrix from Logistic Model tells that we have {} + {} = {}  correct predictions and {} + {} = {} incorrect predictions.' \n",
    "      .format(cm_logreg[0,0], cm_logreg[1,1],cm_logreg[0,0]+ cm_logreg[1,1], \n",
    "              cm_logreg[0,1], cm_logreg[1,0],cm_logreg[0,1]+ cm_logreg[1,0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.68      0.52        38\n",
      "           1       0.86      0.68      0.76       112\n",
      "\n",
      "    accuracy                           0.68       150\n",
      "   macro avg       0.64      0.68      0.64       150\n",
      "weighted avg       0.75      0.68      0.70       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics: \"Precision\", Recall, F-Score\n",
    "print('Logistic Regression Model: ') \n",
    "print(classification_report(test_y, ypred_logreg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Precision and Recall is highly used for imbalanced dataset because in an highly imbalanced dataset, a high accuracy can be meaningless (Accuracy Score Paradox). **Precision** gives us an answer of \"how many are correctly classified among that class?\". The precision is intuitively the ability of the classifier to not label a sample as positive if it is negative.<br> **Recall** means \"how many of this class you find over the whole number of element of this class?\". The recall is intuitively the ability of the classifier to find all the positive samples.<br> The **f1-score** is the harmonic mean between precision and recall scores. when f1-score reaches its best value at 1 and worst score at 0.<br> Finally, the **support** is the number of occurence of the given class in your dataset. So we have 38 of class 0 (non-creditworty) and 112 of class 1 (creditworty), which is a quite \"imbalanced\" dataset.\n",
    "\n",
    "> Logistic Regression Model for \"creditiworthy\" classification achieved  achieved : \n",
    " - 0.86 for the precision score\n",
    " - 0.68 for the recall score \n",
    " - 0.76 for the f1-score \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Decision'></a>\n",
    "### i-2. Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define and fit a model\n",
    "model_dt = DecisionTreeClassifier(class_weight='balanced')\n",
    "model_dt.fit(train_X, train_y)\n",
    "\n",
    "# Predict the test set results\n",
    "ypred_dt = model_dt.predict(test_X) # Predict class labels for samples in X.\n",
    "ypred_prob_dt = model_dt.predict_proba(test_X) # Probability estimates.\n",
    "\n",
    "# Retrieve the probabilities for the positive class\n",
    "yhat_positive_dt = ypred_prob_dt[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature Importance: Decision Tree Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Duration_of_Credit_Month, Score: 0.141246\n",
      "Feature: Credit_Amount, Score: 0.239726\n",
      "Feature: Instalment_per_cent, Score: 0.076647\n",
      "Feature: Most_valuable_available_asset, Score: 0.037353\n",
      "Feature: Age_years, Score: 0.150507\n",
      "Feature: Type_of_apartment, Score: 0.046898\n",
      "Feature: Account_Balance_Some Balance, Score: 0.134973\n",
      "Feature: Payment_Status_of_Previous_Credit_Some Problems, Score: 0.029354\n",
      "Feature: Purpose_Home Related, Score: 0.014340\n",
      "Feature: Purpose_New car, Score: 0.006070\n",
      "Feature: Purpose_Used car, Score: 0.013538\n",
      "Feature: Value_Savings_Stocks_< £100, Score: 0.012393\n",
      "Feature: Value_Savings_Stocks_£100-£1000, Score: 0.038364\n",
      "Feature: Length_of_current_employment_longer than 1 yrs employment, Score: 0.033447\n",
      "Feature: No_of_Credits_at_this_Bank_More than 1, Score: 0.025145\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get importance\n",
    "importance_dt = model_dt.feature_importances_\n",
    "\n",
    "features_dt = dict(zip(train_X.columns, importance_dt))\n",
    "features_sorted_dt = sorted(features_dt, key=features_dt.get, reverse=True)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in zip(train_X.columns, importance_dt):\n",
    "    print('Feature: {}, Score: {:5f}' .format(i,v))\n",
    "# plot feature importance\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.barplot(y = [x for x in train_X.columns ], x= importance_dt , color = base_color, order = features_sorted_dt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the Decision Tree analysis, the most important 7 predictor variables are: \n",
    "1. 'Credit_Amount'\n",
    "2. 'Age_years'\n",
    "3. 'Account_Balance_Some_Balance'\n",
    "4. 'Duration_of_Credit_Month'\n",
    "5. 'Instalment_per_cent'\n",
    "6. 'Value_Savings_Stocks_£100-£1000'\n",
    "7. 'Most_valuable_available_asset' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Decision Tree Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18 20]\n",
      " [23 89]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix for Decision Tree Model \n",
    "cm_dt = confusion_matrix(test_y, ypred_dt)\n",
    "print(cm_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix from Decision Tree Model tells that we have 18 + 89 = 107 correct predictions and 20 + 23 = 43 incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print('The Confusion Matrix from Decision Tree Model tells that we have {} + {} = {} correct predictions and {} + {} = {} incorrect predictions.' \n",
    "      .format(cm_dt[0,0], cm_dt[1,1],cm_dt[0,0]+ cm_dt[1,1], \n",
    "              cm_dt[0,1], cm_dt[1,0],cm_dt[0,1]+ cm_dt[1,0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Model: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.47      0.46        38\n",
      "           1       0.82      0.79      0.81       112\n",
      "\n",
      "    accuracy                           0.71       150\n",
      "   macro avg       0.63      0.63      0.63       150\n",
      "weighted avg       0.72      0.71      0.72       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics: \"Precision\", Recall, F-Score\n",
    "print('Decision Tree Model: ') \n",
    "print(classification_report(test_y, ypred_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Decision Tree Model for \"creditiworthy\" classification achieved : \n",
    " - 0.82 for the precision score\n",
    " - 0.79 for the recall score \n",
    " - 0.81 for the f1-score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Random'></a>\n",
    "### i-3. Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Define and fit a model\n",
    "model_rf=  RandomForestClassifier(max_depth=10, random_state=42, class_weight='balanced')\n",
    "model_rf.fit(train_X, train_y)\n",
    "\n",
    "# Predict the test set results\n",
    "ypred_rf = model_rf.predict(test_X) # Predict class labels for samples in X.\n",
    "ypred_prob_rf = model_rf.predict_proba(test_X) # Probability estimates.\n",
    "\n",
    "# Retrieve the probabilities for the positive classra\n",
    "yhat_positive_rf = ypred_prob_rf[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature Importance: Random Forest Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Duration_of_Credit_Month, Score: 0.139387\n",
      "Feature: Credit_Amount, Score: 0.209224\n",
      "Feature: Instalment_per_cent, Score: 0.071692\n",
      "Feature: Most_valuable_available_asset, Score: 0.072726\n",
      "Feature: Age_years, Score: 0.144957\n",
      "Feature: Type_of_apartment, Score: 0.036071\n",
      "Feature: Account_Balance_Some Balance, Score: 0.108069\n",
      "Feature: Payment_Status_of_Previous_Credit_Some Problems, Score: 0.034129\n",
      "Feature: Purpose_Home Related, Score: 0.020307\n",
      "Feature: Purpose_New car, Score: 0.016113\n",
      "Feature: Purpose_Used car, Score: 0.015253\n",
      "Feature: Value_Savings_Stocks_< £100, Score: 0.019733\n",
      "Feature: Value_Savings_Stocks_£100-£1000, Score: 0.047033\n",
      "Feature: Length_of_current_employment_longer than 1 yrs employment, Score: 0.030826\n",
      "Feature: No_of_Credits_at_this_Bank_More than 1, Score: 0.034478\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get importance\n",
    "importance_rf = model_rf.feature_importances_\n",
    "\n",
    "features_rf = dict(zip(train_X.columns, importance_rf))\n",
    "features_sorted_rf = sorted(features_rf, key=features_rf.get, reverse=True)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in zip(train_X.columns, importance_rf):\n",
    "    print('Feature: {}, Score: {:5f}' .format(i,v))\n",
    "# plot feature importance\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.barplot(y = [x for x in train_X.columns ], x= importance_rf , color = base_color, order = features_sorted_rf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the Random Forest analysis, the most important 7 predictor variables are: \n",
    "1. 'Credit_Amount'\n",
    "2. 'Age_years'\n",
    "3. 'Duration_of_Credit_Month'\n",
    "4. 'Account_Balance_Some_Balance'\n",
    "5. 'Most_valuable_available_asset'\n",
    "6. 'Instalment_per_cent'\n",
    "7. 'Value_Savings_Stocks_£100-£1000'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Random Forest Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 13  25]\n",
      " [  7 105]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix for Random Forest Model\n",
    "cm_rf = confusion_matrix(test_y, ypred_rf)\n",
    "print(cm_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix from Random Forest Model tells that we have 13 + 105 = 118 correct predictions and 25 + 7 = 32 incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print('The Confusion Matrix from Random Forest Model tells that we have {} + {} = {} correct predictions and {} + {} = {} incorrect predictions.' \n",
    "      .format(cm_rf[0,0], cm_rf[1,1],cm_rf[0,0]+ cm_rf[1,1], \n",
    "              cm_rf[0,1], cm_rf[1,0],cm_rf[0,1]+ cm_rf[1,0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Model: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.34      0.45        38\n",
      "           1       0.81      0.94      0.87       112\n",
      "\n",
      "    accuracy                           0.79       150\n",
      "   macro avg       0.73      0.64      0.66       150\n",
      "weighted avg       0.77      0.79      0.76       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics: \"Precision\", Recall, F-Score\n",
    "print('Random Forest Model: ') \n",
    "print(classification_report(test_y, ypred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Random Forest Model for \"creditiworthy\" classification achieved : \n",
    " - 0.81 for the precision score\n",
    " - 0.94 for the recall score \n",
    " - 0.87 for the f1-score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Gradient'></a>\n",
    "### i-4. Gradient Boosting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Define and fit a model\n",
    "model_gbm = GradientBoostingClassifier(random_state=42)\n",
    "model_gbm.fit(train_X, train_y)\n",
    "\n",
    "# Predict the test set results\n",
    "ypred_gbm = model_gbm.predict(test_X) # Predict class labels for samples in X.\n",
    "ypred_prob_gbm = model_gbm.predict_proba(test_X) # Probability estimates.\n",
    "\n",
    "# Retrieve the probabilities for the positive class\n",
    "yhat_positive_gbm = ypred_prob_gbm[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature Importance: Gradient Boosting Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Duration_of_Credit_Month, Score: 0.159965\n",
      "Feature: Credit_Amount, Score: 0.289643\n",
      "Feature: Instalment_per_cent, Score: 0.059160\n",
      "Feature: Most_valuable_available_asset, Score: 0.042260\n",
      "Feature: Age_years, Score: 0.101492\n",
      "Feature: Type_of_apartment, Score: 0.012878\n",
      "Feature: Account_Balance_Some Balance, Score: 0.167910\n",
      "Feature: Payment_Status_of_Previous_Credit_Some Problems, Score: 0.063931\n",
      "Feature: Purpose_Home Related, Score: 0.010658\n",
      "Feature: Purpose_New car, Score: 0.008386\n",
      "Feature: Purpose_Used car, Score: 0.002483\n",
      "Feature: Value_Savings_Stocks_< £100, Score: 0.016653\n",
      "Feature: Value_Savings_Stocks_£100-£1000, Score: 0.043693\n",
      "Feature: Length_of_current_employment_longer than 1 yrs employment, Score: 0.009039\n",
      "Feature: No_of_Credits_at_this_Bank_More than 1, Score: 0.011849\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get importance\n",
    "importance_gbm = model_gbm.feature_importances_\n",
    "\n",
    "features_gbm = dict(zip(train_X.columns, importance_gbm))\n",
    "features_sorted_gbm = sorted(features_gbm, key=features_gbm.get, reverse=True)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in zip(train_X.columns, importance_gbm):\n",
    "    print('Feature: {}, Score: {:5f}' .format(i,v))\n",
    "# plot feature importance\n",
    "sns.barplot(y = [x for x in train_X.columns ], x= importance_gbm , color = base_color, order = features_sorted_gbm)\n",
    "plt.title('Variance Important - Gradient Boosting Model ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the Gradient Boosting analysis, the most important 7 predictor variables are: \n",
    "1. 'Credit_Amount'\n",
    "2. 'Account_Balance_Some_Balance'\n",
    "3. 'Duration_of_Credit_Month'\n",
    "4. 'Age_years'\n",
    "5. 'Payment_Status_of_Previous_Credit_Some Problems'\n",
    "6. 'Instalment_per_cent'\n",
    "7. 'Value_Savings_Stocks_£100-£1000'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Gradient Boosting Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 15  23]\n",
      " [  7 105]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix for Gradient Boosting Model\n",
    "cm_gbm = confusion_matrix(test_y, ypred_gbm)\n",
    "print(cm_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix from Gradient Boosting Model tells that we have 15+105 = 120 correct predictions and 23+7 = 30 incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print('The Confusion Matrix from Gradient Boosting Model tells that we have {}+{} = {} correct predictions and {}+{} = {} incorrect predictions.' \n",
    "      .format(cm_gbm[0,0], cm_gbm[1,1],cm_gbm[0,0]+ cm_gbm[1,1], \n",
    "              cm_gbm[0,1], cm_gbm[1,0],cm_gbm[0,1]+ cm_gbm[1,0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoosting Model: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.39      0.50        38\n",
      "           1       0.82      0.94      0.88       112\n",
      "\n",
      "    accuracy                           0.80       150\n",
      "   macro avg       0.75      0.67      0.69       150\n",
      "weighted avg       0.79      0.80      0.78       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics: \"Precision\", Recall, F-Score\n",
    "print('GradientBoosting Model: ') \n",
    "print(classification_report(test_y, ypred_gbm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Gradient Boosting Model for \"creditiworthy\" classification achieved : \n",
    " - 0.82 for the precision score\n",
    " - 0.94 for the recall score \n",
    " - 0.88 for the f1-score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='AdaBoost'></a>\n",
    "### i-5.  AdaBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Define and fit a model\n",
    "model_ab = AdaBoostClassifier(random_state=42)\n",
    "model_ab.fit(train_X, train_y)\n",
    "\n",
    "# Predict the test set results\n",
    "ypred_ab = model_ab.predict(test_X) # Predict class labels for samples in X.\n",
    "ypred_prob_ab = model_ab.predict_proba(test_X) # Probability estimates.\n",
    "\n",
    "# Retrieve the probabilities for the positive class\n",
    "yhat_positive_ab = ypred_prob_ab[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature Importance: AdaBoost Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Duration_of_Credit_Month, Score: 0.100000\n",
      "Feature: Credit_Amount, Score: 0.540000\n",
      "Feature: Instalment_per_cent, Score: 0.040000\n",
      "Feature: Most_valuable_available_asset, Score: 0.020000\n",
      "Feature: Age_years, Score: 0.100000\n",
      "Feature: Type_of_apartment, Score: 0.020000\n",
      "Feature: Account_Balance_Some Balance, Score: 0.020000\n",
      "Feature: Payment_Status_of_Previous_Credit_Some Problems, Score: 0.040000\n",
      "Feature: Purpose_Home Related, Score: 0.020000\n",
      "Feature: Purpose_New car, Score: 0.020000\n",
      "Feature: Purpose_Used car, Score: 0.000000\n",
      "Feature: Value_Savings_Stocks_< £100, Score: 0.020000\n",
      "Feature: Value_Savings_Stocks_£100-£1000, Score: 0.040000\n",
      "Feature: Length_of_current_employment_longer than 1 yrs employment, Score: 0.020000\n",
      "Feature: No_of_Credits_at_this_Bank_More than 1, Score: 0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get importance\n",
    "importance_ab = model_ab.feature_importances_\n",
    "\n",
    "features_ab = dict(zip(train_X.columns, importance_ab))\n",
    "features_sorted_ab = sorted(features_ab, key=features_ab.get, reverse=True)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in zip(train_X.columns, importance_ab):\n",
    "    print('Feature: {}, Score: {:5f}' .format(i,v))\n",
    "# plot feature importance\n",
    "sns.barplot(y = [x for x in train_X.columns ], x= importance_ab , color = base_color, order = features_sorted_ab)\n",
    "plt.title('Variance Important - AdaBoost Model ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the AdaBoost analysis, the most important 7 predictor variables are: \n",
    "1. 'Credit_Amount'\n",
    "2. 'Duration_of_Credit_Month'   \n",
    "3. 'Age_years'\n",
    "4. 'Instalment_per_cent'\n",
    "5. 'Payment_Status_of_Previous_Credit_Some Problems'\n",
    "6. 'Value_Savings_Stocks_£100-£1000'\n",
    "7. Most_valuable_available_asset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **AdaBoost Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 23]\n",
      " [15 97]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix for AdaBoost Model \n",
    "cm_ab = confusion_matrix(test_y, ypred_ab)\n",
    "print(cm_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix from AdaBoost Model tells that we have 15+97 = 112 correct predictions and 23+15 = 38 incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print('The Confusion Matrix from AdaBoost Model tells that we have {}+{} = {} correct predictions and {}+{} = {} incorrect predictions.' \n",
    "      .format(cm_ab[0,0], cm_ab[1,1],cm_ab[0,0]+ cm_ab[1,1], \n",
    "              cm_ab[0,1], cm_ab[1,0],cm_ab[0,1]+ cm_ab[1,0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost Model: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.39      0.44        38\n",
      "           1       0.81      0.87      0.84       112\n",
      "\n",
      "    accuracy                           0.75       150\n",
      "   macro avg       0.65      0.63      0.64       150\n",
      "weighted avg       0.73      0.75      0.74       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation Metrics: \"Precision\", Recall, F-Score\n",
    "print('AdaBoost Model: ') \n",
    "print(classification_report(test_y, ypred_ab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Gradient Boosting Model for \"creditiworthy\" classification achieved : \n",
    " - 0.81 for the precision score\n",
    " - 0.87 for the recall score \n",
    " - 0.84 for the f1-score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are many kinds of Classification Model Evaluation Metrics. Among them, I conducted the model comparision using: \n",
    "- **Confusion Matrix**, **\"Precision\"**, **Recall**, **F-Score**, \n",
    "- **Area Under Receiver Operating Characteristic curve (AUROC)**, \n",
    "- **Area Under the Precision-Recall curve (PR AUC)** <br>\n",
    "\n",
    "> Note that **AUROC** and **PR AUC** are simultaneously compared with proper visualizations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve / Area Under Receiver Operating Characteristic curve (AUROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC from Logistic Regression Model : 0.733\n",
      "AUROC from Decision Tree Model : 0.634\n",
      "AUROC from Random Forest Model : 0.765\n",
      "AUROC from GradientBoosting Model : 0.749\n",
      "AUROC from AdaBoost Model : 0.754\n"
     ]
    }
   ],
   "source": [
    "# plot no skill roc curve - the diagonal line\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', label='No Skill')\n",
    "\n",
    "# calculate inputs for the roc curve\n",
    "fpr, tpr, thresholds = roc_curve(test_y, yhat_positive_logreg)\n",
    "fpr2, tpr2, thresholds2 = roc_curve(test_y, yhat_positive_dt)\n",
    "fpr3, tpr3, thresholds3 = roc_curve(test_y, yhat_positive_rf)\n",
    "fpr4, tpr4, thresholds4 = roc_curve(test_y, yhat_positive_gbm)\n",
    "fpr5, tpr5, thresholds5 = roc_curve(test_y, yhat_positive_ab)\n",
    "\n",
    "\n",
    "# plot roc curve\n",
    "plt.plot(fpr, tpr, marker='.', label='Logistic Regression')\n",
    "plt.plot(fpr2, tpr2, marker='.', label='Decision Tree')\n",
    "plt.plot(fpr3, tpr3, marker='.', label='Random Forest')\n",
    "plt.plot(fpr4, tpr4, marker='.', label='Gradient Boosting')\n",
    "plt.plot(fpr5, tpr5, marker='.', label='AdaBoost')\n",
    "\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "\n",
    "# show the legend\n",
    "plt.legend( bbox_to_anchor = (1, 0.5), loc = 6)\n",
    "plt.show()\n",
    "\n",
    "# calculate and print AUROC\n",
    "roc_auc_logreg = roc_auc_score(test_y, yhat_positive_logreg)\n",
    "print('AUROC from Logistic Regression Model : %.3f' % roc_auc_logreg)\n",
    "roc_auc_dt = roc_auc_score(test_y, yhat_positive_dt)\n",
    "print('AUROC from Decision Tree Model : %.3f' % roc_auc_dt)\n",
    "roc_auc_rf = roc_auc_score(test_y, yhat_positive_rf)\n",
    "print('AUROC from Random Forest Model : %.3f' % roc_auc_rf)\n",
    "roc_auc_gbm = roc_auc_score(test_y, yhat_positive_gbm)\n",
    "print('AUROC from GradientBoosting Model : %.3f' % roc_auc_gbm)\n",
    "roc_auc_ab = roc_auc_score(test_y, yhat_positive_ab)\n",
    "print('AUROC from AdaBoost Model : %.3f' % roc_auc_ab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The receiver operating characteristic (ROC) curve is another common tool used with binary classifiers. The dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from the \"No Skill\" line as possible (toward the top-left corner). According to the model comparison based on AUROC, **Random Forest Model** seems the most appropriate model for this Creditworthiness. <br> <br>\n",
    "The Forest model is the highest line along the graph for most of the chart, and it rises the fastest of all models meaning that we are getting a higher rate of true positive rates vs. false positives. We want a high rate of true positive vs. true negative rates because we do not want to extend loans to people who are not creditworthy. The ideal ROC curve hugs the top left corner, indicating a high true positive rate and a low false positive rate. <br> <br>\n",
    "ROC curves should be used when there are roughly equal numbers of observations for each class.\n",
    "Precision-Recall curves should be used when there is a moderate to large class imbalance.\n",
    "The reason for this recommendation is that ROC curves present an optimistic picture of the model on datasets with a class imbalance. Thus, I also compare the Precision-Recall curve between the models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under the Precision-Recall curve (PR AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC PR from Logistic Regression Model : 0.886\n",
      "AUC PR from Decision Tree Model : 0.882\n",
      "AUC PR from Random Forest Model : 0.897\n",
      "AUC PR from Gradient Boosting Model : 0.882\n",
      "AUC PR from AdaBoost Model : 0.885\n"
     ]
    }
   ],
   "source": [
    "# calculate the no skill line as the proportion of the positive class\n",
    "no_skill = len(target[target==1]) / len(target)\n",
    "# plot the no skill precision-recall curve\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n",
    "\n",
    "# calculate inputs for the PR curve\n",
    "precision, recall, thresholds = precision_recall_curve(test_y, yhat_positive_logreg)\n",
    "precision_dt, recall_dt, thresholds_dt = precision_recall_curve(test_y, yhat_positive_dt)\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(test_y, yhat_positive_rf)\n",
    "precision_gbm, recall_gbm, thresholds_gbm = precision_recall_curve(test_y, yhat_positive_gbm)\n",
    "precision_ab, recall_ab, thresholds_ab = precision_recall_curve(test_y, yhat_positive_ab)\n",
    "\n",
    "# plot PR curve\n",
    "plt.plot(recall, precision, marker='.', label='Logistic Regression')\n",
    "plt.plot(precision_dt, recall_dt, marker='.', label='Decision Tree')\n",
    "plt.plot(precision_rf, recall_rf, marker='.', label='Random Forest')\n",
    "plt.plot(precision_gbm, recall_gbm, marker='.', label='Gradient Boosting')\n",
    "plt.plot(precision_ab, recall_ab, marker='.', label='AdaBoost')\n",
    "\n",
    "\n",
    "# axis labels\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "# show the legend\n",
    "plt.legend( bbox_to_anchor = (1, 0.5), loc = 6)\n",
    "plt.show()\n",
    "\n",
    "# calculate and print PR AUC\n",
    "auc_pr = auc(recall, precision)\n",
    "print('AUC PR from Logistic Regression Model : %.3f' % auc_pr)\n",
    "auc_pr_dt = auc(recall_dt, precision_dt)\n",
    "print('AUC PR from Decision Tree Model : %.3f' % auc_pr_dt)\n",
    "auc_pr_rf = auc(recall_rf, precision_rf)\n",
    "print('AUC PR from Random Forest Model : %.3f' % auc_pr_rf)\n",
    "auc_pr_gbm = auc(recall_gbm, precision_gbm)\n",
    "print('AUC PR from Gradient Boosting Model : %.3f' % auc_pr_gbm)\n",
    "auc_pr_ab = auc(recall_ab, precision_ab)\n",
    "print('AUC PR from AdaBoost Model : %.3f' % auc_pr_ab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the model comparison based on AUC PR, **Random Forest Model** shows also the best performance for the Creditworthiness classification issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Cross'></a>\n",
    "### iii. Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-Validation for Imbalanced Datasets\n",
    "> Additionally, I validate the classification models using the method of Cross-Validation. This was not a project requirement, but I personally think that is quite useful for the most analysis with small or imbalanced dataset.The k-fold cross-validation technique is quite efficient and effective in assessing a classifier for balanced or even \"slightly\" skewed datasets. However, it fails in case of imbalanced datasets since the training data is usually split into folds assuming a uniform probability distribution. In the case of imbalanced data, this might mislead model evaluations. <br>\n",
    "> For imbalanced classification problems, a modified k-fold cross-validation, called **stratified k-fold cross-validation**, is more suitable. **Stratified k-fold** (or Repeated Stratified k-fold) maintains the imbalanced class in each fold. Either of these k-fold techniques can then be applied in line with cross_val_score to predict and evaluate each fold. [Reference](https://towardsdatascience.com/how-to-effectively-predict-imbalanced-classes-in-python-e8cd3b5720c4)<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the required libraries\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean AUROC from Logistic Regression Model: 0.752\n",
      "Mean AUROC from Decision Tree Model: 0.613\n",
      "Mean AUROC from Random Forest Model: 0.748\n",
      "Mean AUROC from GradientBoosting Model: 0.727\n",
      "Mean AUROC from AdaBoost Model: 0.715\n"
     ]
    }
   ],
   "source": [
    "# define cross-validation\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
    "\n",
    "# Evaluate the models\n",
    "# 1. Cost-Sensitive Logistic Regression\n",
    "scores_logreg = cross_val_score(model_logreg, inputs, target, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "# 2. Cost-Sensitive Decision Tree\n",
    "scores_dt = cross_val_score(model_dt, inputs, target, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "# 3. Cost-Sensitive Random Forest\n",
    "scores_rf = cross_val_score(model_rf, inputs, target, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "# 4. Cost-Sensitive GradientBoosting Model\n",
    "scores_gbm = cross_val_score(model_gbm, inputs, target, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "# 5. Cost-Sensitive AdaBoost Model \n",
    "scores_ab = cross_val_score(model_ab, inputs, target, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "\n",
    "\n",
    "# Summarize performance\n",
    "print('Mean AUROC from Logistic Regression Model: %.3f' % np.mean(scores_logreg))\n",
    "print('Mean AUROC from Decision Tree Model: %.3f' % np.mean(scores_dt))\n",
    "print('Mean AUROC from Random Forest Model: %.3f' % np.mean(scores_rf))\n",
    "print('Mean AUROC from GradientBoosting Model: %.3f' % np.mean(scores_gbm))\n",
    "print('Mean AUROC from AdaBoost Model: %.3f' % np.mean(scores_ab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the \"modified\" k-fold cross validation, the random forest model still shows the best performance. The mean value of AUROC from Random Forest Model achieved the highest score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Selection'></a>\n",
    "\n",
    "## 3.  Model Selection "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the four classification models, I determine to select the result from **Random Forest Model**. The analysis under the Random Forest Classifier achieved very high precision score (0.81), recall score (0.94), and also f1-score (0.87). When comparing binary\n",
    "model performance, an F1 score looks for the bias in \"Creditworthy\" and \"Non-Creditworthy\" and calculates a single score: the higher the score, the better.The fact that Random Forest model is the best choice for this analysis, can be checked through the ROC curve and PR Curve plot, too. Since we aim to cover the best true positive rate(96.16%), we should choose the red-color line in both plots, which represents Forest model, since it gives the lowest false positive rate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model_rf, open('model_rf.pickle', 'wb'))"
   ]
  }
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