🏦 Build a secure banking dashboard with Streamlit, bridging Python UI and Cloud-SQL for a seamless banking experience.

End-to-end machine learning project for loan approval prediction with leakage handling, model comparison, and feature importance analysis.

Machine learning project predicting loan defaults using HMEQ dataset. Implements multiple classification algorithms (Logistic Regression, Decision Tree, Random Forest) with comprehensive EDA and model evaluation. Capstone project for MIT ADSP Program

End-to-end ML project that predicts loan approval using applicant and financial data, featuring preprocessing, feature engineering, and Streamlit deployment.

ML system predicting loan approvals with 95%+ accuracy. Features 6 algorithms, Streamlit UI, and comprehensive data analysis.

Predict loan approval from applicant data using scikit-learn. Includes EDA, training pipeline, and a Streamlit demo app.

ML system for loan default prediction with XGBoost, FastAPI, and Streamlit UI

A Machine Learning web app that predicts loan approval status and explains rejection reasons using SHAP values. Built with Flask and Scikit-learn.

Machine Learning project to classify loan status (default vs non-default) using Decision Tree, KNN, and Naive Bayes

🔍 Analyze customer financial behavior with comprehensive loan and transaction data insights using dashboards in Excel, Power BI, and Tableau.

A Flask-based loan prediction web app using a Random Forest model to predict loan approval based on user input. It includes a clean, responsive UI, form validation, and real-time prediction display.

A model for predicting loan eligibility for Tanzanian students using a Random Forest.

Machine learning project to predict loan default

Predict if your loan will be accepted or not. This happens by using a labeled data for applicants who applied for a loan before, analyzing these data and using some classification models on it.

End-to-end Machine Learning Loan Prediction project with Model Training, FastAPI backend, and React frontend UI. Includes trained ML model, API for predictions, and a user interface for real-time loan approval prediction.

Machine learning project for predicting loan approval with preprocessing, multiple models, GridSearchCV tuning, and performance analysis.

End-to-end ML loan prediction app using Flask API, XGBoost, MySQL, HTML/JS.

This project is an interactive Streamlit application that predicts customer loan approval using a Random Forest Classifier. It features dataset upload, automated preprocessing, label & one-hot encoding, SMOTE oversampling, exploratory data analysis (EDA), performance metrics, confusion matrix visualization, and ranked prediction results.

🔴 Credit Risk Prediction (Loan Applicants) 🔴 In this project, I analyzed loan applicant data to predict default risk. I cleaned and prepared the dataset, encoded categorical features, and trained classification models. The goal was to identify key factors influencing loan approval and build a reliable prediction pipeline.

This project aims to predict loan eligibility based on applicant details such as: ✅ Income ✅ Credit history ✅ Employment status ✅ Loan amount Using machine learning, the model classifies applicants as eligible or ineligible, helping financial institutions streamline their approval process.