This repository contains an end-to-end machine learning project to automate the loan eligibility process for a housing finance company. By building and evaluating predictive models, the project provides a data-driven solution to streamline loan approvals and improve operational efficiency.
The Challenge: Dream Housing Finance wants to automate its manual loan approval process and make faster, more accurate decisions. The goal is to build a machine learning model that can predict a customer's loan eligibility based on key financial and demographic data.
The Solution: This project develops a complete machine learning pipeline, from data preprocessing to model deployment. We compare the performance of a Decision Tree Classifier and a Random Forest Classifier to identify the most effective model for this business problem.
- Source: A publicly available dataset from Kaggle.
- Features: Gender, Marital Status, Education, Dependents, Income, Loan Amount, Credit History, and others.
- Target Variable:
Loan_Status(Y for approved, N for not approved).
- Exploratory Data Analysis (EDA): Initial data inspection, including identifying and handling missing values.
- Data Preprocessing: Imputing missing data, creating new features like
TotalIncome, and encoding categorical variables. - Model Building & Evaluation:
- Decision Tree: A baseline model to establish performance metrics.
- Random Forest: An ensemble model optimized using GridSearchCV to improve accuracy and generalization.
- Performance Analysis: Models are evaluated using a comprehensive classification report and a confusion matrix.
The Random Forest Classifier was selected as the final model due to its superior performance and ability to generalize well to unseen data. Its robustness and high accuracy make it ideal for deployment in a real-time application system.
See the full Jupyter notebook for detailed analysis, visualizations, and a breakdown of the results.
Here are some plots from the analysis:
- Deployment: The trained Random Forest model is ready to be deployed as a microservice using frameworks like Flask or Streamlit.
- Further Analysis: Explore additional feature engineering or experiment with other ensemble methods.
To run the notebook locally, clone this repository and install the required libraries:
git clone https://github.com/Elsie-Muhumuza/loan_eligibility_prediction_model.git
cd loan_eligibility_prediction_model
pip install -r requirements.txt