A real-time IPL Winning Prediction system powered by Machine Learning. The application analyzes live match data and predicts the winning probability of both teams through an interactive Streamlit interface.
Machine Learning project for predicting Titanic passenger survival using Python and Scikit-learn.
A collection of Machine Learning projects built using Python, Scikit-learn, Streamlit, and data visualization libraries to solve real-world prediction and classification problems.
Random Forest pipeline classifying 10-year coronary heart disease risk (4,000+ patient cohort), deployed as a FastAPI service in Docker. Team project — my role: API design & deployment.
Classical ML coursework: K-Means variants, SVM with PCA, polynomial regression and SVR
An end-to-end unsupervised machine learning pipeline comparing K-Means and Agglomerative Hierarchical Clustering for customer segmentation on ~1M online retail transactions using RFM analysis.
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