Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.

At Infosys Springboard, I worked on a project focused on unsupervised anomaly detection in healthcare providers. I implemented three machine learning algorithms—Isolation Forest, Elliptic Envelope, and One-Class SVM—as well as a deep learning approach using autoencoders. Additionally, I conducted individual SHAP analysis

An analysis that explores marketing metrics and how they can be attributed to insurance claims.

This repository contains the Python implementation for the article "Integrating Transformers and Gaussian Mixture Models for Parkinson's Detection." The code combines GMM, Transformers, and SHAP analysis for accurate and interpretable voice-based diagnosis.

Collection of the assignments for Data Science Engineering Methods on National Stock Exchange Dataset and TMNIST dataset

Predicts store sales using machine learning models, featuring data preprocessing, feature engineering, and hyperparameter tuning. Includes end-to-end workflows for model optimization and evaluation. Visualizes insights and performance metrics with SHAP analysis and feature importance plots.