This project converts a Jupyter-based machine learning model into a modular, cloud-ready data engineering pipeline using Python, AWS S3, and PostgreSQL. It enables automated data ingestion, transformation, and loading of credit card transaction data for downstream analysis or modeling.

A robust end-to-end machine learning pipeline for credit card fraud detection using Python, scikit-learn, and Streamlit. Includes data preprocessing, feature selection, model training & evaluation, saving the best model, and an interactive Streamlit app for predictions.

Predicting customer retention in an e-commerce platform using classification models. Includes data preprocessing, feature engineering, and model evaluation (Logistic Regression, SVM, Random Forest, KNN, Decision Tree). Best model achieves 83% accuracy and perfect recall. Ideal for business use.