Loan Eligibility Prediction using Gradient Boosting Classifier

Loan Eligibility Prediction using Gradient Boosting Classifier

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

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What will you learn

Understanding the problem statement and business intent
Understanding different libraries and their respective uses
In depth exploratory data analysis of each feature & Feature engineering
Data Cleansing and Preparation
Creating custom functions for Machine Learning Models
In depth explanation of data imputation and filling missing values
Defining an approach to solve ML Classification problems
Data preparation for the Machine Learning models
Training and testing the model using Cross Validation
Building statistical models like Gradient Boosting, XGBoost etc
Selecting the best model based on different metrics
Understanding metrics like ROC Curve, MCC scorer etc
Creating pickle files for model reusability
Data Balancing using SMOTE
Scheduling ML jobs for automation

Project Description

SYL bank is one of Australia’s largest banks. Currently, the loan applications which come in to their various branches are processed manually. The decision whether to grant a loan or not is subjective and due to a lot of applications coming in, it is getting harder for them to decide the loan grant status. Thus, they want to build an automated machine learning solution which will look at different factors and decide whether to grant loan or not to the respective individual.

In this ML problem, we will building a classification model as we have to predict if an applicant should get a loan or not. We will look at various factors of the applicant like credit score, past history and from those we will try to predict the loan granting status. We will also cleanse the data and fill in the missing values so that our ML model performs as expected. Thus we will be giving out a probability score along with Loan Granted or Loan Refused output from the model.

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Curriculum For This Mini Project

Package Installer - PIP requirements
05m
Jupyter vs Microsoft Visual Studio
06m
Business Problem
10m
Data Science Problem - Binary Classification
06m
Business Stakeholders Roles
05m
Show Me The Data
09m
Model Evaluation Metrics
05m
Solution Workflow
04m
Data Ingestion
05m
Exploratory Data Analysis (eda) - Drop Duplicates
04m
Eda - Loan Status Plot
05m
Eda - Quartiles - Quantiles
10m
Eda - Removing Outliers
09m
Eda - Replace Null Values
08m
Eda - Home Mortgage - Annual Income - Variables
05m
Eda - Purpose - Monthly Debt - Variables
06m
Eda - Credit History Variables 1
03m
Eda - Credit History Variables 2
05m
Eda - Credit History Variables 3
07m
Ordinality - Dummy Variables
06m
Imputation Of Missing Values
06m
Ordinalise - Impute
06m
Drop Dummy Variables - Scaling
07m
Introduction To Logistic Regression
09m
Split data into train and test
06m
Introduction to Decision Trees
10m
Introduction to Random Forest
08m
Introduction to Boosting Algorithm
08m
How to choose an Algorithm
09m
Building a Generic Classify Function
08m
Feature Importance
08m
Running the Models
10m
Balancing The Dataset - Smote Pickle
08m
Predicting Output Of The Model
06m
Task Scheduler
03m