AI200_Lending_Club_Capstone_Project_[Yun Hui_Onn].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "890b86cf",
   "metadata": {},
   "source": [
    "<img 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\" 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   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42f3868",
   "metadata": {},
   "source": [
    "***\n",
    "# HEICODERS ACADEMY\n",
    "<b>AI200: Applied Machine Learning</b><br>\n",
    "***\n",
    "<b>Capstone Project:</b> Loan Default Prediction Competition [Student Notebook]\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703919d6",
   "metadata": {},
   "source": [
    "<b>Dataset Used:</b>\n",
    "\n",
    "- LendingClub Loan Default Dataset from Kaggle\n",
    "\n",
    "<b>Note:</b>\n",
    "\n",
    "- While this Data Capstone is a group project, all students are to <b>individually submit your own copy of the Capstone to the eLearn platform</b>. Also, take note the <b>last 5 bonus marks (at the end of this notebook) is to be individually completed</b>.\n",
    "    - <b>Group component</b>: a machine learning model that can generate whether a client with default or not. This includes the data cleaning, feature engineering and model implementation steps, and accounts for 40% of the entire course.<b>[40 marks]</b>\n",
    "    - <b>Individual component</b>: submit your responses to these prompts at the end of this student notebook <b>[5 bonus marks]</b>:\n",
    "        - Your key takeaways and insights from working on this project (2 mark)\n",
    "        - What are some areas in your current workplace where you think the introduction of big data application will be beneficial? (2 mark)\n",
    "        - Walk us through how you will go about implementing one big data application in your workplace (1 mark)\n",
    "- To help you, we have already provided preliminary data preparation steps in this notebook. We hope that this will expose you to one of the many techniques when trying to generate predictions for a variable with more than 2 outcomes.\n",
    "- We highly recommend that you <b>organise your code and annotate them thoroughly with explanations using the markdown cells</b>. In the workplace, we will have to present our findings to our colleagues and superiors, so this is a good practice for that!\n",
    "- Lastly, have fun, and don't hesitate to shout in the class chat if you need help!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d572d1-b5ae-4916-9ada-71618f419567",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Student Score:</b> / 45 mark\n",
    "  \n",
    "<b>Teaching Assistant Comment:</b>\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9e10c2-ec5a-4023-bb8a-b3835f5fdb4c",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<b>Before you submit</b>\n",
    "- <b>Student Name:</b> _Onn Yun Hui_\n",
    "- <b>Group Name:</b> _Fighting Machines_\n",
    "- <b>Name of Teammates:</b> _Marc_\n",
    "- _Rename this Jupyter Notebook in the following format before you submit it_ e.g. `John_Doe_Project.ipynb`\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf53e59-9165-450d-a667-d6205583e2c0",
   "metadata": {},
   "source": [
    "# 🏦 Lending Club Credit Default Prediction Capstone Project [40 Marks]\n",
    "\n",
    "# 📑 Introduction\n",
    "\n",
    "> `LendingClub` is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. `LendingClub` is the world's largest peer-to-peer lending platform.\n",
    "\n",
    "> Solving this case study will give us an idea about how real business problems are solved using EDA and Machine Learning. In this case study, we will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.\n",
    "\n",
    "# 📝 Business Understanding\n",
    "\n",
    "> You work for the `LendingClub` company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:\n",
    "\n",
    "> - If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company\n",
    "> - If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company\n",
    "\n",
    "> The data given contains the information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns which indicate if a person is likely to default, which may be used for takin actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.\n",
    "\n",
    "> When a person applies for a loan, there are two types of decisions that could be taken by the company:\n",
    "> 1. `Loan accepted`: If the company approves the loan, there are 3 possible scenarios described below:\n",
    "    - `Fully paid`: Applicant has fully paid the loan (the principal and the interest rate)\n",
    "    - `Current`: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.\n",
    "    - `Charged-off`: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan\n",
    "> 2. `Loan rejected`: The company had rejected the loan (because the candidate does not meet their requirements etc.). Since the loan was rejected, there is no transactional history of those applicants with the company and so this data is not available with the company (and thus in this dataset)\n",
    "\n",
    "# 🎯 Business Objectives\n",
    "> - `LendingClub` is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fast online interface. \n",
    "> - Like most other lending companies, lending loans to ‘`risky`’ applicants is the largest source of financial loss (called `credit loss`). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who defaultcause the largest amount of loss to the lenders. In this case, the customers labelled as '`charged-off`' are the '`defaulters`'. \n",
    "> - If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA and machine learning is the aim of this case study. \n",
    "> - In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment. \n",
    "> - To develop your understanding of the domain, you are advised to independently research a little about risk analytics (understanding the types of variables and their significance should be enough).\n",
    "\n",
    "# 💾 Data Description\n",
    "\n",
    "----\n",
    "Here is the information on this particular data set:\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>LoanStatNew</th>\n",
    "      <th>Description</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>0</th>\n",
    "      <td>loan_amnt</td>\n",
    "      <td>The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <td>term</td>\n",
    "      <td>The number of payments on the loan. Values are in months and can be either 36 or 60.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <td>int_rate</td>\n",
    "      <td>Interest Rate on the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>3</th>\n",
    "      <td>installment</td>\n",
    "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>4</th>\n",
    "      <td>grade</td>\n",
    "      <td>LC assigned loan grade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>5</th>\n",
    "      <td>sub_grade</td>\n",
    "      <td>LC assigned loan subgrade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>6</th>\n",
    "      <td>emp_title</td>\n",
    "      <td>The job title supplied by the Borrower when applying for the loan.*</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>7</th>\n",
    "      <td>emp_length</td>\n",
    "      <td>Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>8</th>\n",
    "      <td>home_ownership</td>\n",
    "      <td>The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>9</th>\n",
    "      <td>annual_inc</td>\n",
    "      <td>The self-reported annual income provided by the borrower during registration.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>10</th>\n",
    "      <td>verification_status</td>\n",
    "      <td>Indicates if income was verified by LC, not verified, or if the income source was verified</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>11</th>\n",
    "      <td>issue_d</td>\n",
    "      <td>The month which the loan was funded</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>12</th>\n",
    "      <td>loan_status</td>\n",
    "      <td>Current status of the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>13</th>\n",
    "      <td>purpose</td>\n",
    "      <td>A category provided by the borrower for the loan request.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>14</th>\n",
    "      <td>title</td>\n",
    "      <td>The loan title provided by the borrower</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>15</th>\n",
    "      <td>zip_code</td>\n",
    "      <td>The first 3 numbers of the zip code provided by the borrower in the loan application.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>16</th>\n",
    "      <td>addr_state</td>\n",
    "      <td>The state provided by the borrower in the loan application</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>17</th>\n",
    "      <td>dti</td>\n",
    "      <td>A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>18</th>\n",
    "      <td>earliest_cr_line</td>\n",
    "      <td>The month the borrower's earliest reported credit line was opened</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>19</th>\n",
    "      <td>open_acc</td>\n",
    "      <td>The number of open credit lines in the borrower's credit file.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>20</th>\n",
    "      <td>pub_rec</td>\n",
    "      <td>Number of derogatory public records</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>21</th>\n",
    "      <td>revol_bal</td>\n",
    "      <td>Total credit revolving balance</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>22</th>\n",
    "      <td>revol_util</td>\n",
    "      <td>Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>23</th>\n",
    "      <td>total_acc</td>\n",
    "      <td>The total number of credit lines currently in the borrower's credit file</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>24</th>\n",
    "      <td>initial_list_status</td>\n",
    "      <td>The initial listing status of the loan. Possible values are – W, F</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>25</th>\n",
    "      <td>application_type</td>\n",
    "      <td>Indicates whether the loan is an individual application or a joint application with two co-borrowers</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>26</th>\n",
    "      <td>mort_acc</td>\n",
    "      <td>Number of mortgage accounts.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>27</th>\n",
    "      <td>pub_rec_bankruptcies</td>\n",
    "      <td>Number of public record bankruptcies</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f09e6a-a8bd-4cb8-a1dd-7837364ce215",
   "metadata": {},
   "source": [
    "Before we start, we will import all the libraries needed for this Student Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db6595d-746a-4e21-84c7-f645547c2b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de0bea8",
   "metadata": {},
   "source": [
    "# 1.📝Data Preparation\n",
    "---\n",
    "\n",
    "> **OVERALL GOAL:** \n",
    "> - Ingest the data and relabelling the outcome variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da509662",
   "metadata": {},
   "source": [
    "## 1a. Import Dataset\n",
    "\n",
    "We import the dataset using <code>read_csv()</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0507e2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"data/lc_trainingset.csv\")\n",
    "test_df = pd.read_csv(\"data/lc_testset.csv\")\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f43ae67",
   "metadata": {},
   "source": [
    "## 1b. How many rows/columns in each dataset?\n",
    "\n",
    "We understand the dimensionality of the data using <code>.shape</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db57e167",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551b444a-f194-4036-b8af-837cbaba23a9",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Observe that you are have only 26 columns in the <code>test_df</code>, and that is the outcome variable which you are supposed to generate predictions for and submit to Kaggle.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56a9aa",
   "metadata": {},
   "source": [
    "## 1c. Check Out Loan Status\n",
    "\n",
    "We use <code>value_counts()</code> to see the number of instances of each unique status in the loan_status data column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6047610",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83fb426",
   "metadata": {},
   "source": [
    "We visualise the loan status for ease of interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de99adad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.bar(train_df['loan_status'].value_counts(), width=800, height=500)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e895dc03",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "While our descriptive analysis shows that the outcome variable only has 2 different outcomes, according to the business rules, it is possible that the column that we are trying to predict for has other different outcomes. As a good data scientist, we need to deal with this possibility so that if a data with a new outcome occurs the model will be able to deal with it accordingly. So how do we go about this?</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019f4ca4",
   "metadata": {},
   "source": [
    "## 1d. Relabelling the Loan Status\n",
    "\n",
    "From the above visualisation, the two loan status we are interested in are <b>Defaulted</b> (Charged Off) and <b>Not Defaulted</b> (Fully Paid). Hence, we will need to add a new variable which will be binary (0s and 1s).\n",
    "\n",
    "- 0 means Not Defaulted\n",
    "- 1 means Defaulted\n",
    "\n",
    "All those loans, whose status is “Fully Paid”, “Current” will be categorized as Not Defaulted and anything else will be categorized as Defaulted. To achieve this we will introduce new variable defaulted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c83702",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# First we define the function\n",
    "def change_loan_status(loan_status):\n",
    "    if loan_status == 'Fully Paid':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "# Next we apply the function\n",
    "train_df['loan_status'] = train_df['loan_status'].apply(change_loan_status)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d1ca41",
   "metadata": {},
   "source": [
    "Once again, we apply the <code>value_counts()</code> to see the number of instances of each unique status in the loan_status data column. Now we can see that the loan status only has 2 category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e0976c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8044ed4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71a67a90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e5b7b0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "So in the real world, when your column to be predicted has more than 2 outcomes, and you wish you classify them into 2 different outcomes, the above is one of the approach you can take - using logic/domain-knowledge to categorise the classes together.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78d71fe7-0b83-483c-8cdd-4397af4900a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform data cleaning. You may create as many cells as you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5969a56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# For example: convert date columns from string to datetime format\n",
    "# Read about datetime formatting here - https://strftime.org/ to learn more about %b and %Y\n",
    "pd.options.display.max_rows = 316823\n",
    "pd.options.display.max_columns = 999\n",
    "import numpy as np\n",
    "\n",
    "for date_col in ['earliest_cr_line', 'issue_d']:\n",
    "    train_df[date_col] = pd.to_datetime(train_df[date_col], format = \"%b-%Y\")\n",
    "    \n",
    "#dropping columns\n",
    "drop_columns= ['id', 'emp_title', 'purpose', 'title']\n",
    "new_train_df= train_df.drop(columns = drop_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3853df5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------------------\n",
    "#--------------------\n",
    "#--------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057e505e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting term to int, removing months\n",
    "\n",
    "def term(months):\n",
    "    if months == ' 36 months':\n",
    "        return int(36)\n",
    "    else:\n",
    "        return int(60)\n",
    "    \n",
    "new_train_df['term'] = new_train_df['term'].apply(term)\n",
    "\n",
    "#int_rate no feature engineering\n",
    "#installment no feature engineering\n",
    "\n",
    "#changing grades to integer\n",
    "def grade(letter):\n",
    "    if letter == 'A':\n",
    "        return int(6)\n",
    "    elif letter == 'B':\n",
    "        return int(5)\n",
    "    elif letter == 'C':\n",
    "        return int(4)\n",
    "    elif letter == 'D':\n",
    "        return int(3)\n",
    "    elif letter == 'E':\n",
    "        return int(2)\n",
    "    elif letter == 'F':\n",
    "        return int(1)\n",
    "    else:\n",
    "        return int(0)\n",
    "new_train_df['grade'] = new_train_df['grade'].apply(grade)\n",
    "\n",
    "#sub_grade Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "subgrade_label= LabelEncoder()\n",
    "subgrade_label.fit(['A1','A2','A3','A4','A5',\n",
    "                    'B1','B2','B3','B4','B5',\n",
    "                    'C1','C2','C3','C4','C5',\n",
    "                    'D1','D2','D3','D4','D5',\n",
    "                    'E1','E2','E3','E4','E5',\n",
    "                    'F1','F2','F3','F4','F5',\n",
    "                    'G1','G2','G3','G4','G5'])\n",
    "\n",
    "new_train_df['sub_grade']= subgrade_label.transform(new_train_df['sub_grade'])\n",
    "\n",
    "#emp_length- replacing and fillna with 0\n",
    "new_train_df['emp_length'] = new_train_df['emp_length'].map({'10+ years':'10',\n",
    "                                                    '7 years':'7',\n",
    "                                                    '9 years':'9',\n",
    "                                                    '5 years':'5',\n",
    "                                                    '1 year':'1',\n",
    "                                                    '2 years':'2',\n",
    "                                                    '3 years':'3',\n",
    "                                                    '6 years':'6',\n",
    "                                                    '4 years':'4',\n",
    "                                                    '8 years':'8',\n",
    "                                                    '< 1 year':'1'})\n",
    "\n",
    "new_train_df['emp_length'] = new_train_df['emp_length'].apply(pd.to_numeric)\n",
    "new_train_df['emp_length'] = new_train_df['emp_length'].fillna(0)\n",
    "\n",
    "#OHE for home ownership- non nominal categorical variable\n",
    "def home_owner(variable):\n",
    "    if variable == 'ANY':\n",
    "        return 'Other'\n",
    "    elif variable == 'OTHER':\n",
    "        return 'Other'\n",
    "    elif variable == 'NONE':\n",
    "        return 'Other'\n",
    "    elif variable == 'MORTGAGE':\n",
    "        return 'Mortgage'\n",
    "    elif variable == 'RENT':\n",
    "        return 'Rent'\n",
    "    else:\n",
    "        return 'Own'\n",
    "\n",
    "new_train_df['home_ownership'] = new_train_df['home_ownership'].apply(home_owner)\n",
    "home_ownership_OH= pd.get_dummies(new_train_df['home_ownership'])\n",
    "new_train_df = pd.concat([new_train_df, home_ownership_OH],axis= 1)\n",
    "\n",
    "#annual_inc setting upper limit\n",
    "upper_lim = 1350000\n",
    "new_train_df = new_train_df[(new_train_df['annual_inc'] < upper_lim)]\n",
    "new_train_df['annual_inc_log'] = (new_train_df['annual_inc']+1).transform(np.log)\n",
    "\n",
    "#verification_status- OHE\n",
    "verification_status_OH= pd.get_dummies(new_train_df['verification_status'])\n",
    "new_train_df = pd.concat([new_train_df, verification_status_OH],axis= 1)\n",
    "\n",
    "#issue_d\n",
    "new_train_df['issue_d_year'] = new_train_df['issue_d'].dt.year\n",
    "new_train_df['issue_d_month'] = new_train_df['issue_d'].dt.month\n",
    "\n",
    "#dti- remove outliers\n",
    "upper_lim = 60\n",
    "new_train_df = new_train_df[(new_train_df['dti'] < upper_lim)]\n",
    "\n",
    "#extracting year from earliest_cr_line\n",
    "new_train_df['earliest_cr_line_year'] = new_train_df['earliest_cr_line'].dt.year\n",
    "new_train_df['earliest_cr_line_month'] = new_train_df['earliest_cr_line'].dt.month\n",
    "\n",
    "#pub_rec- remove outliers\n",
    "upper_lim = 20\n",
    "new_train_df = new_train_df[(new_train_df['pub_rec'] < upper_lim)]\n",
    "\n",
    "#revol_bal\n",
    "new_train_df['revol_bal_log'] = (new_train_df['revol_bal']+1).transform(np.log)\n",
    "\n",
    "#revol_util- remove outliers\n",
    "new_train_df['revol_util'] = new_train_df['revol_util'].fillna (new_train_df['revol_util'].mean())\n",
    "upper_lim = 200\n",
    "new_train_df = new_train_df[(new_train_df['revol_util'] < upper_lim)]\n",
    "\n",
    "#OHE for initial_list_status\n",
    "initial_list_status_OH = pd.get_dummies(new_train_df['initial_list_status'])\n",
    "new_train_df = pd.concat([new_train_df, initial_list_status_OH],axis= 1)\n",
    "\n",
    "#OHE for application_type\n",
    "application_type_OH= pd.get_dummies(new_train_df['application_type'])\n",
    "new_train_df = pd.concat([new_train_df, application_type_OH],axis= 1)\n",
    "\n",
    "#fillna in mort_acc, mode, can try median.\n",
    "new_train_df['mort_acc'] = new_train_df['mort_acc'].fillna(new_train_df['mort_acc'].mode()[0])\n",
    "\n",
    "#fill na for pub_rec_bankruptcies\n",
    "new_train_df['pub_rec_bankruptcies'] = new_train_df['pub_rec_bankruptcies'].fillna (new_train_df['pub_rec_bankruptcies'].mode()[0])\n",
    "\n",
    "#extract postal code and state, dropping address\n",
    "new_train_df['postcode'] = new_train_df['address'].str.extract('(\\d{5}$)')\n",
    "new_train_df['postcode3'] = new_train_df['postcode'].str[:3]\n",
    "\n",
    "new_train_df['state']= new_train_df['address'].str.extract('([A-Z]{2})')\n",
    "\n",
    "#OHE for state\n",
    "state_OH= pd.get_dummies(new_train_df['state'])\n",
    "new_train_df = pd.concat([new_train_df, state_OH],axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad50c662",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "correlation= new_train_df.corr()\n",
    "print(correlation['loan_status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267c3f00",
   "metadata": {},
   "source": [
    "# 2.🔍Exploratory Data Analysis\n",
    "---\n",
    "\n",
    "> **OVERALL GOAL:** \n",
    "> - Get an understanding for which variables are important, view summary statistics, and visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27167d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform your EDA in the following cells. You may create as many cells as you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ed22c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b7b3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a895cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_train_df = new_train_df.corr()\n",
    "\n",
    "# Create a heat map of all the numerical features.\n",
    "fig = px.imshow(corr_train_df, width=900, height=800,\n",
    "                title=' Heatmap Correlation for Train Data Set')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1278f98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig= px.histogram(train_df, x= 'loan_amnt', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aee9d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'int_rate', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dbce200",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'installment', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f89cb085",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.box(train_df, x= 'emp_length', y= 'annual_inc', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e08843bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.box(train_df, x= 'sub_grade', y='int_rate')\n",
    "fig.update_xaxes(categoryorder= 'category ascending')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e5c02aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'annual_inc', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db524a08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df['annual_inc'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8c30667",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'dti', color = 'loan_status')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b159d12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['dti'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26434328",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'open_acc')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d5dc237",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['open_acc'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc045a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'pub_rec')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d18bdb3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['pub_rec'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b253d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'revol_bal')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b94f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['revol_bal'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b55f71dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'revol_util')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c67d6d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['revol_util'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c82dbb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'total_acc')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b52f500",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['total_acc'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bba14c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'total_acc')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9baf357a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['total_acc'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10ae92dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'mort_acc')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e6b22ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['mort_acc'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b00d1dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.histogram(train_df, x= 'pub_rec_bankruptcies')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bce38881",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['pub_rec_bankruptcies'].value_counts(ascending=True).sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e677ceda",
   "metadata": {},
   "source": [
    "# 3.🔄 Feature Engineering\n",
    "---\n",
    "\n",
    "> **OVERALL GOAL:** \n",
    "> - Select relevant features and enhance them to improve the overall performance of the machine learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657808ac-d935-4f4a-a390-772806270d29",
   "metadata": {},
   "source": [
    "First we convert the text data into useful categorical numerical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "374189f8-73e2-41ed-a488-3ef118e68067",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform your Feature Enginerring in the following cells. You may create as many cells as you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72762b85-b5c2-4922-9724-76db14414508",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2a5d84-777e-4bf9-8659-b72ecfb6d5d8",
   "metadata": {},
   "source": [
    "# 4.🤖 Models Building and Comparing Model Perforamance\n",
    "---\n",
    "\n",
    "> **OVERALL GOAL:** \n",
    "> - Building a machine learning model that is capable of generating predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0bf6aaf-d266-4397-8207-cdb6c280ad55",
   "metadata": {},
   "source": [
    "## 4a. Logistic Regression (baseline model without any feature engineering)\n",
    "\n",
    "Here we start off by creating a baseline model whhich you can use to compare against"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff4a1d9-a37e-46e0-9cb0-c30de3be8094",
   "metadata": {},
   "source": [
    "<b>Selecting the feature columns</b><br>\n",
    "We select the features we want to use in predicting our outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "834951ae-1ee8-422b-abf8-189d4c9ff407",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_train_df[['loan_amnt',\n",
    "                  'term',\n",
    "                  'int_rate',\n",
    "                  'grade',\n",
    "                  'sub_grade',\n",
    "                  'emp_length',\n",
    "                  'Mortgage','Other','Own','Rent',\n",
    "                  'annual_inc_log',\n",
    "                  'Not Verified', 'Source Verified', 'Verified',\n",
    "                  'dti',\n",
    "                  'earliest_cr_line_year',\n",
    "                  'revol_bal_log',\n",
    "                  'total_acc',\n",
    "                  'pub_rec_bankruptcies',\n",
    "                  'postcode3',\n",
    "                  'issue_d_year',\n",
    "                  'issue_d_month',\n",
    "                  'revol_util',\n",
    "                  'AK','AL','AR','AZ','CA','CO','CT','DC','DE','DP','FL','GA','HI','IA','ID','IL','IN','KS','KY','LA','MA','MD','ME','MI','MN','MO','MS','MT','NC','ND','NE','NH','NJ','NM','NV','NY','OH','OK','OR','PA','PS','RI','SC','SD','TN','TX','US','UT','VA','VT','WA','WI','WV','WY'\n",
    "                 ]]\n",
    "\n",
    "# Select the features you want to use to predict the loan_status\n",
    "y = new_train_df['loan_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132a9e91-a29e-4c1a-80cc-abcafba8b8df",
   "metadata": {},
   "source": [
    "<b> Import the models of your choice</b><br>\n",
    "You are highly encouraged to try other models after this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2354ffb4-be66-46a0-aa87-ddd445fe9edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_model = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346df04b-e956-4009-b620-742c9afc3ef7",
   "metadata": {},
   "source": [
    "<b> Train-Test Split</b></br>\n",
    "We split the data to facilitate the evaluation of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56eaa5d1-4fd1-4983-b3fd-0a807f4731a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a96b7c5-945e-48b4-8636-66e4dc4364a0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "You may opt for using k-fold cross validation as well. (Recommended)</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f35c86-99ff-4eaa-b960-cddf1f937cec",
   "metadata": {},
   "source": [
    "<b> Evaluate your model (using only lc_trainingset.csv)</b><br>\n",
    "We generate a logistic regression model by fitting the training data using <code>.fit()</code>, and thereafter generate predictions using <code>.predict()</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4583b9fc-8593-4dc0-abc4-95ca0f2f32ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_model = logreg_model.fit(X_train, y_train)\n",
    "y_pred_proba = logreg_model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda1a2fe-af2c-4847-b6f1-6b98503f365c",
   "metadata": {},
   "source": [
    "<b>We evalute the model's AUC score using <code>metrics.roc_auc_score()</code></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04119771-96b8-4d20-a41f-f01f65babfb9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5779e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reference: AUC: 0.7790788473430068"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ede0f84-df5a-42b5-b42c-314948b902ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AUC with LogReg Model\n",
    "logreg_model = LogisticRegression(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg_model = logreg_model.fit(X_train, y_train)\n",
    "y_pred_proba = logreg_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) \n",
    "\n",
    "#Accuracy with LogReg Model traintest split\n",
    "logreg_model = LogisticRegression(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg_model = logreg_model.fit(X_train, y_train)\n",
    "y_pred= logreg_model.predict(X_test)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_pred)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6529ce7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accuracy with LogReg Model Kfold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statistics\n",
    "\n",
    "kf = KFold(n_splits=10)\n",
    "k_fold_Accuracy = []\n",
    "logreg_model = LogisticRegression(random_state=0)\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    \n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    logreg_model = logreg_model.fit(X_train, y_train)\n",
    "    y_pred= logreg_model.predict(X_test)\n",
    "    Accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "    k_fold_Accuracy.append(Accuracy)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56a30809",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision tree AUC, less than logreg\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statistics\n",
    "\n",
    "Classifier = DecisionTreeClassifier()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "Classifier_model = Classifier.fit(X_train, y_train)\n",
    "y_pred_proba = Classifier_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f33c00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AUC and Accuracy with RFClassifier traintest split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=5)\n",
    "\n",
    "model = RandomForestClassifier(random_state=5)\n",
    "\n",
    "RF_model= model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba = RF_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) \n",
    "\n",
    "y_predict=RF_model.predict(X_test)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_predict)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)\n",
    "\n",
    "\n",
    "#Accuracy with RFClassifier KFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statistics\n",
    "\n",
    "kf = KFold(n_splits=10)\n",
    "k_fold_Accuracy = []\n",
    "model = RandomForestClassifier(random_state=5)\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    \n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    logreg_model = logreg_model.fit(X_train, y_train)\n",
    "    y_pred= logreg_model.predict(X_test)\n",
    "    Accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "    k_fold_Accuracy.append(Accuracy)\n",
    "    \n",
    "print('\\nAccuracy_score on test dataset : ',statistics.mean(k_fold_Accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f49ee9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gradient Booster Classifier AUC\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model = GradientBoostingClassifier(learning_rate=0.01,random_state=5)\n",
    "GB_model= model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba = GB_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) \n",
    "\n",
    "y_predict= GB_model.predict(X_test)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_predict)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)\n",
    "\n",
    "\n",
    "#Gradient Booster Kfold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statistics\n",
    "\n",
    "kf = KFold(n_splits=10)\n",
    "k_fold_Accuracy = []\n",
    "model = GradientBoostingClassifier(learning_rate=0.01,random_state=5)\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    \n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    GB_model= model.fit(X_train, y_train)\n",
    "    y_pred= GB_model.predict(X_test)\n",
    "    Accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "    k_fold_Accuracy.append(Accuracy)\n",
    "    \n",
    "print('\\nAccuracy_score on test dataset : ',statistics.mean(k_fold_Accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f81d77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#xgb traintest split without postal\n",
    "\n",
    "X = new_train_df[['loan_amnt',\n",
    "                  'term',\n",
    "                  'int_rate',\n",
    "                  'grade',\n",
    "                  'sub_grade',\n",
    "                  'emp_length',\n",
    "                  'Mortgage','Other','Own','Rent',\n",
    "                  'annual_inc_log',\n",
    "                  'Not Verified', 'Source Verified', 'Verified',\n",
    "                  'dti',\n",
    "                  'earliest_cr_line_year',\n",
    "                  'revol_bal_log',\n",
    "                  'total_acc',\n",
    "                  'pub_rec_bankruptcies',\n",
    "                  'postcode3',\n",
    "                  'issue_d_year',\n",
    "                  'issue_d_month',\n",
    "                  'revol_util',\n",
    "                  'AK','AL','AR','AZ','CA','CO','CT','DC','DE','DP','FL','GA','HI','IA','ID','IL','IN','KS','KY','LA','MA','MD','ME','MI','MN','MO','MS','MT','NC','ND','NE','NH','NJ','NM','NV','NY','OH','OK','OR','PA','PS','RI','SC','SD','TN','TX','US','UT','VA','VT','WA','WI','WV','WY'\n",
    "                 ]]\n",
    "\n",
    "\n",
    "import xgboost as xgb\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=5)\n",
    "\n",
    "model = xgb.XGBClassifier(learning_rate=0.1, random_state=5)\n",
    "XG_model= model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba = XG_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) \n",
    "\n",
    "y_predict= XG_model.predict(X_test)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_predict)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)\n",
    "\n",
    "\n",
    "#xgb Kfold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statistics\n",
    "\n",
    "kf = KFold(n_splits=10)\n",
    "k_fold_Accuracy = []\n",
    "model = xgb.XGBClassifier(learning_rate=0.1, random_state=5)\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    \n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    XG_model= model.fit(X_train, y_train)\n",
    "    y_pred= XG_model.predict(X_test)\n",
    "    Accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "    k_fold_Accuracy.append(Accuracy)\n",
    "    \n",
    "print('\\nAccuracy_score on test dataset : ',statistics.mean(k_fold_Accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f06406d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_train_df[['loan_amnt',\n",
    "                  'term',\n",
    "                  'int_rate',\n",
    "                  'grade',\n",
    "                  'sub_grade',\n",
    "                  'emp_length',\n",
    "                  'Mortgage','Other','Own','Rent',\n",
    "                  'annual_inc_log',\n",
    "                  'Not Verified', 'Source Verified', 'Verified',\n",
    "                  'dti',\n",
    "                  'earliest_cr_line_year',\n",
    "                  'revol_bal_log',\n",
    "                  'total_acc',\n",
    "                  'pub_rec_bankruptcies',\n",
    "                  'postcode3',\n",
    "                  'issue_d_year',\n",
    "                  'issue_d_month',\n",
    "                  'revol_util'\n",
    "                 ]]\n",
    "\n",
    "# Select the features you want to use to predict the loan_status\n",
    "y = new_train_df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e439b8f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#catboost with traintest split\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=5)\n",
    "\n",
    "CB_model = CatBoostClassifier(random_state= 5,\n",
    "                              iterations=1000,   \n",
    "                              scale_pos_weight=2,\n",
    "                              l2_leaf_reg=1,\n",
    "                              logging_level='Silent'\n",
    "                             )\n",
    "\n",
    "CB_model.fit(X_train, y_train)\n",
    "\n",
    "y_predict= CB_model.predict(X_test)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_predict)\n",
    "print('\\nAccuracy_score on test dataset : ', accuracy_test)\n",
    "\n",
    "y_pred_proba= CB_model.predict_proba(X_test)[:,1]\n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred_proba)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c061da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#optimising catboost\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)\n",
    "CBC = CatBoostClassifier()\n",
    "\n",
    "parameters = {'depth'         : [4,5,6,7,8,9, 10],\n",
    "              'learning_rate' : [0.01,0.02,0.03,0.04],\n",
    "              'iterations'    : [10, 20,30,40,50,60,70,80,90, 100]\n",
    "             }\n",
    "\n",
    "Grid_CBC = GridSearchCV(estimator=CBC, param_grid = parameters, cv = 2, n_jobs=-1)\n",
    "Grid_CBC.fit(X_train, y_train)\n",
    "\n",
    "print(\" Results from Grid Search \" )\n",
    "print(\"\\n The best estimator across ALL searched params:\\n\",Grid_CBC.best_estimator_)\n",
    "print(\"\\n The best score across ALL searched params:\\n\",Grid_CBC.best_score_)\n",
    "print(\"\\n The best parameters across ALL searched params:\\n\",Grid_CBC.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42267526",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate(model, val_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3752689c-48dd-4176-aa09-443e50c93fa5",
   "metadata": {},
   "source": [
    "# 5.📊 Generate and Export Predictions from your Final Model\n",
    "---\n",
    "\n",
    "> **OVERALL GOAL:** \n",
    "> - Export your predictions and submit it to kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b09891-3204-4fe2-a703-16e5026f4ed1",
   "metadata": {},
   "source": [
    "## 5a. Re-fit your final model on train.csv\n",
    "\n",
    "Say for instance you have determined the (based on AUC score)\n",
    "1. optimal basket of features, \n",
    "2. the most optimal model for this dataset, and \n",
    "3. the best parameters for the model you have chosen\n",
    "\n",
    "You should then retrain the chosen model with the optimal parameters, on the chosen basket of features on all the 316824 rows of data you have (instead of just on X_train, y_train). This is because you want to fully utilise your 316824 rows of data to maximise what your model can learn!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98909b6a-81cf-43be-90ff-0a6bd283a9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_train_df[['loan_amnt',\n",
    "                  'term',\n",
    "                  'int_rate',\n",
    "                  'grade',\n",
    "                  'sub_grade',\n",
    "                  'emp_length',\n",
    "                  'Mortgage','Other','Own','Rent',\n",
    "                  'annual_inc_log',\n",
    "                  'Not Verified', 'Source Verified', 'Verified',\n",
    "                  'dti',\n",
    "                  'earliest_cr_line_year',\n",
    "                  'revol_bal_log',\n",
    "                  'total_acc',\n",
    "                  'pub_rec_bankruptcies',\n",
    "                  'postcode3',\n",
    "                  'issue_d_year',\n",
    "                  'issue_d_month',\n",
    "                  'revol_util'    ]]\n",
    "\n",
    "# Select the features you want to use to predict the loan_status\n",
    "y = new_train_df['loan_status']\n",
    "\n",
    "print(len(X))\n",
    "print(len(y))\n",
    "\n",
    "from catboost import CatBoostClassifier\n",
    "CB_model = CatBoostClassifier(random_state= 5,\n",
    "                              iterations=1000,   \n",
    "                              scale_pos_weight=2,\n",
    "                              l2_leaf_reg=1,\n",
    "                              logging_level='Silent', \n",
    "                             )\n",
    "\n",
    "CB_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09cbb6b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 5b. Generate predictions for the test_df\n",
    "\n",
    "Here, we will generate the predictions for the test_df. Make sure to apply whatever feature engineering technique you performed on train_df to test_df as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fe88645-d671-4970-be93-fa8732ce54cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"data/lc_testset.csv\")\n",
    "\n",
    "for date_col in ['earliest_cr_line', 'issue_d']:\n",
    "    test_df[date_col] = pd.to_datetime(test_df[date_col], format = \"%b-%Y\")\n",
    "\n",
    "#dropping wordy and categorical variables with alot of nuniques\n",
    "drop_columns = ['id', 'emp_title', 'purpose', 'title']\n",
    "new_test_df = test_df.drop(columns = drop_columns)\n",
    "\n",
    "#converting term to int, removing months\n",
    "\n",
    "def term(months):\n",
    "    if months == ' 36 months':\n",
    "        return int(36)\n",
    "    else:\n",
    "        return int(60)\n",
    "    \n",
    "new_test_df['term'] = new_test_df['term'].apply(term)\n",
    "\n",
    "#int_rate no feature engineering\n",
    "#installment no feature engineering\n",
    "\n",
    "#changing grades to integer\n",
    "def grade(letter):\n",
    "    if letter == 'A':\n",
    "        return int(6)\n",
    "    elif letter == 'B':\n",
    "        return int(5)\n",
    "    elif letter == 'C':\n",
    "        return int(4)\n",
    "    elif letter == 'D':\n",
    "        return int(3)\n",
    "    elif letter == 'E':\n",
    "        return int(2)\n",
    "    elif letter == 'F':\n",
    "        return int(1)\n",
    "    else:\n",
    "        return int(0)\n",
    "new_test_df['grade'] = new_test_df['grade'].apply(grade)\n",
    "\n",
    "#sub_grade Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "subgrade_label= LabelEncoder()\n",
    "subgrade_label.fit(['A1','A2','A3','A4','A5',\n",
    "                    'B1','B2','B3','B4','B5',\n",
    "                    'C1','C2','C3','C4','C5',\n",
    "                    'D1','D2','D3','D4','D5',\n",
    "                    'E1','E2','E3','E4','E5',\n",
    "                    'F1','F2','F3','F4','F5',\n",
    "                    'G1','G2','G3','G4','G5'])\n",
    "\n",
    "new_test_df['sub_grade']= subgrade_label.transform(new_test_df['sub_grade'])\n",
    "\n",
    "#emp_length- replacing and fillna with 0\n",
    "new_test_df['emp_length'] = new_test_df['emp_length'].map({'10+ years':'10',\n",
    "                                                    '7 years':'7',\n",
    "                                                    '9 years':'9',\n",
    "                                                    '5 years':'5',\n",
    "                                                    '1 year':'1',\n",
    "                                                    '2 years':'2',\n",
    "                                                    '3 years':'3',\n",
    "                                                    '6 years':'6',\n",
    "                                                    '4 years':'4',\n",
    "                                                    '8 years':'8',\n",
    "                                                    '< 1 year':'1'})\n",
    "\n",
    "new_test_df['emp_length'] = new_test_df['emp_length'].apply(pd.to_numeric)\n",
    "new_test_df['emp_length'] = new_test_df['emp_length'].fillna(0)\n",
    "\n",
    "#OHE for home ownership- non nominal categorical variable\n",
    "def home_owner(variable):\n",
    "    if variable == 'ANY':\n",
    "        return 'Other'\n",
    "    elif variable == 'OTHER':\n",
    "        return 'Other'\n",
    "    elif variable == 'NONE':\n",
    "        return 'Other'\n",
    "    elif variable == 'MORTGAGE':\n",
    "        return 'Mortgage'\n",
    "    elif variable == 'RENT':\n",
    "        return 'Rent'\n",
    "    else:\n",
    "        return 'Own'\n",
    "\n",
    "new_test_df['home_ownership'] = new_test_df['home_ownership'].apply(home_owner)\n",
    "home_ownership_OH= pd.get_dummies(new_test_df['home_ownership'])\n",
    "new_test_df = pd.concat([new_test_df, home_ownership_OH],axis= 1)\n",
    "\n",
    "#annual_inc setting upper limit\n",
    "new_test_df['annual_inc_log'] = (new_test_df['annual_inc']+1).transform(np.log)\n",
    "\n",
    "#verification_status- OHE\n",
    "verification_status_OH= pd.get_dummies(new_test_df['verification_status'])\n",
    "new_test_df = pd.concat([new_test_df, verification_status_OH],axis= 1)\n",
    "\n",
    "#issue_d\n",
    "new_test_df['issue_d_year'] = new_test_df['issue_d'].dt.year\n",
    "new_test_df['issue_d_month'] = new_test_df['issue_d'].dt.month\n",
    "\n",
    "\n",
    "#extracting year from earliest_cr_line\n",
    "new_test_df['earliest_cr_line_year'] = new_test_df['earliest_cr_line'].dt.year\n",
    "new_test_df['earliest_cr_line_month'] = new_test_df['earliest_cr_line'].dt.month\n",
    "\n",
    "\n",
    "#revol_bal\n",
    "new_test_df['revol_bal_log'] = (new_test_df['revol_bal']+1).transform(np.log)\n",
    "\n",
    "\n",
    "#OHE for initial_list_status\n",
    "initial_list_status_OH = pd.get_dummies(new_test_df['initial_list_status'])\n",
    "new_test_df = pd.concat([new_test_df, initial_list_status_OH],axis= 1)\n",
    "\n",
    "#OHE for application_type\n",
    "application_type_OH= pd.get_dummies(new_test_df['application_type'])\n",
    "new_test_df = pd.concat([new_test_df, application_type_OH],axis= 1)\n",
    "\n",
    "#fillna in mort_acc, mode, can try median.\n",
    "new_test_df['mort_acc'] = new_test_df['mort_acc'].fillna(new_test_df['mort_acc'].mode()[0])\n",
    "\n",
    "#fill na for pub_rec_bankruptcies\n",
    "new_test_df['pub_rec_bankruptcies'] = new_test_df['pub_rec_bankruptcies'].fillna (new_test_df['pub_rec_bankruptcies'].mode()[0])\n",
    "\n",
    "#extract postal code and state, dropping address\n",
    "new_test_df['postcode'] = new_test_df['address'].str.extract('(\\d{5}$)')\n",
    "new_test_df['postcode3'] = new_test_df['postcode'].str[:3]\n",
    "\n",
    "new_test_df['state']= new_test_df['address'].str.extract('([A-Z]{2})')\n",
    "\n",
    "#OHE for state\n",
    "state_OH= pd.get_dummies(new_test_df['state'])\n",
    "new_test_df = pd.concat([new_test_df, state_OH],axis= 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f14032",
   "metadata": {},
   "source": [
    "Here we use <code>.predict_proba</code> instead of <code>.predict()</code> because this is the format which the Kaggle platform requires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baafd68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_x = new_test_df[['loan_amnt',\n",
    "                  'term',\n",
    "                  'int_rate',\n",
    "                  'grade',\n",
    "                  'sub_grade',\n",
    "                  'emp_length',\n",
    "                  'Mortgage','Other','Own','Rent',\n",
    "                  'annual_inc_log',\n",
    "                  'Not Verified', 'Source Verified', 'Verified',\n",
    "                  'dti',\n",
    "                  'earliest_cr_line_year',\n",
    "                  'revol_bal_log',\n",
    "                  'total_acc',\n",
    "                  'pub_rec_bankruptcies',\n",
    "                  'postcode3',\n",
    "                  'issue_d_year',\n",
    "                  'issue_d_month',\n",
    "                  'revol_util'    ]]\n",
    "# Extract same features as train set (section 4a)\n",
    "probabilities = CB_model.predict_proba(kaggle_x)\n",
    "probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3952dd4-38c3-4d7c-ab40-75f231fc2384",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_preds = probabilities[:,1]  # extract values from the rightmost column \n",
    "#78237\n",
    "len(kaggle_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f998967",
   "metadata": {},
   "source": [
    "Make use of the <code>.to_csv()</code> function to output your predictions in the form of a csv, which will be the format you will be required to submit to Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee19198",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dataframe = pd.DataFrame({\n",
    "    'Id': list(range(len(kaggle_preds))),\n",
    "    'Predicted': kaggle_preds\n",
    "})\n",
    "output_dataframe.to_csv('my_predictions.csv', index=False)  \n",
    "\n",
    "# Check for the .csv in the same folder as your Jupyter Notebook\n",
    "# Try uploading this .csv to the Kaggle competition!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc09157",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>All the Best! May the best model win!</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50a9630-e897-44fd-8e9e-50c75064f915",
   "metadata": {},
   "source": [
    "# 📑 Individual Report [5 Marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837c6e4d-a5e7-4aa9-9318-15087dcbbe7b",
   "metadata": {},
   "source": [
    "What are your key takeaways and insights from working on this project (2 mark)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589a3593-6db7-431e-924b-257ed7dff2d7",
   "metadata": {},
   "source": [
    "Insights gained was that catboost is a really useful and powerful model which can beat all the rest of the models. I will read up more about the reason behind this model's effectiveness soon\n",
    "\n",
    "Another insight is that catboost can just bring up AUC score with any other non-conclusive features and making sense of the data, which can be a potential sign of overfitting the data(?) I avoided doing that for reasons not to just focus on pushing the AUC but also to understand the concepts behind using each of the features instead.\n",
    "\n",
    "Finally, to take responsibility of your own assignments and own learnings as team member did not contribute much (<5%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476f6566-effe-4a62-b0d5-8bb118fe321b",
   "metadata": {},
   "source": [
    "What are some areas in your current workplace where you think the introduction of big data application will be beneficial? (2 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c80f49-f72d-45b0-a1b4-443615ba7858",
   "metadata": {},
   "source": [
    "Currently i cannot think of any application for the introduction in my job scope, but one potential area which my current industry can work with is prediction of disease models or something which can be more powerful with the medical devices.\n",
    "\n",
    "Other areas also includes supply chain planning where the planners can do a forecasting based on the countries, using big data and past trends across all regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0d553f-63e3-4c6d-84b0-f621b262ed7e",
   "metadata": {},
   "source": [
    "Walk us through how you will go about implementing one big data application in your workplace (1 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65c3954-516a-4ef3-b479-9f07248f7d72",
   "metadata": {},
   "source": [
    "Currently no such job scopes is available in the company.\n",
    "\n",
    "I am getting some link ups with the data team to work on potential applications (hopefully) to get past some confidential data regulations and start working on models.\n",
    "\n",
    "Then I can go about introducing the potential impact of collecting good data and how we can continually use these data to plan the business. Ultimately, the purpose of this data is to generate more revenue and improve the lives of humanity."
   ]
  }
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