Description

SmartLoans was developed as a tool that simplifies loan eligibility assessments using a trained machine learning model, to enhance my data analysis and backend development ability. This project built on my frontend expertise and provided hands-on experience with API connections and backend processing, bridging data science with user-facing applications. It was the first time I had worked with exploratory data analysis and training a model given a dataset.

Table of Contents

• Features
• Jupyter Notebook
• License
• Acknowledgements

Features

• Machine Learning Model SmartLoans utilizes a cutting-edge machine learning model trained on a robust Kaggle dataset. This model has trained to deliver highly accurate loan eligibility predictions, ensuring reliable assessments based on diverse financial profiles.
• Comprehensive Data Analysis The tool incorporates advanced exploratory data analysis (EDA) techniques, offering deep insights into financial data trends. This empowers users to understand the factors influencing their loan eligibility, providing a transparent and data-driven decision-making process.

• Seamless API Integration: SmartLoans bridges the gap between data science and user experience through seamless API integration. This enables real-time processing and instant feedback, allowing users to quickly assess their loan eligibility and make informed financial decisions.

Jupyter Notebook

Overview

This Jupyter notebook guides you through a complete loan eligibility analysis, from data exploration to model training. It leverages advanced exploratory data analysis (EDA) and machine learning techniques to build a predictive model. Please see acknowledgements for resources used.

Data Analysis and Preparation

• Exploration: The notebook starts with univariate, bivariate, and multivariate analyses to uncover patterns and relationships in the data. This step is crucial for understanding how different factors might influence loan eligibility.

• Cleaning: Missing values are handled with imputation, and outliers are analyzed to ensure a clean, accurate dataset.

• Statistical Testing: Various statistical tests validate assumptions about the data, laying the groundwork for reliable modeling.

Preprocessing and Feature Engineering

• Encoding and Selection: Categorical variables are encoded using LabelEncoders, and feature importance metrics are used to select the most impactful variables, streamlining the model for better performance.

Model Training and Evaluation

• Building the Model: A machine learning model is trained to predict loan eligibility, with careful attention to balancing the dataset to avoid biased outcomes.

• Results: The notebook concludes with an analysis of feature importance and model performance, offering insights into which factors most strongly influence eligibility.

Testing

• To activate the backend: cd backend python app.py
• To activate frontend: cd frontend npm run dev
• Note: If testing locally, make sure to change the API endpoints

License

• This project is licensed under the MIT License

Acknowledgements

• Guide Credit: This project’s Jupyter notebook was heavily inspired by and built upon the concepts learned from the following guide: Mastering Loan Eligibility Analysis - Advance EDA by Abu Talha. The guide was instrumental in helping me understand exploratory data analysis and training a machine learning model.
• Special thanks to the Kaggle community for providing valuable resources and datasets.