On Finetuning Tabular Foundation Models Paper Code

Automated validation toolkit for tabular ML models in finance and regulated domains.

A complete machine learning and deep learning pipeline for customer churn prediction. Includes preprocessing, model training (RandomForest, XGBoost, CatBoost, Keras), evaluation (confusion matrix, ROC, feature importance), and best model saving using callbacks.

A complete fraud detection pipeline using ML models (CatBoost, XGBoost, LightGBM), class weighting, SMOTE, and custom feature engineering. Achieved strong recall and precision balance for real-world deployment.

This project focuses on predicting the likelihood of heart disease using machine learning techniques. The dataset includes medical features like age, blood pressure, cholesterol, and heart rate. The model uses algorithms like CatBoost and Random Forest to predict the presence of heart disease, assisting early diagnosis.

Production-style end-to-end credit risk ML system with modular feature pipelines, multi-model benchmarking, XGBoost final model selection, and business profit threshold optimization using real financial risk modeling principles.

End-to-end credit risk ML pipeline with CatBoost, SHAP & LIME explainability, fairness monitoring, and auto-generated PDF reports — built for auditability over accuracy.

CropAI is a full-stack AI system built for smart agriculture. It predicts crop diseases using a MobileNetV2 deep learning model, recommends optimal crops based on soil climate conditions, suggests the right fertilizers using ML models.The project includes an API backend (FastAPI),an interactive dashboard (Streamlit),and cloud deployment on Render.