shap-analysis

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.

Binary classification of industrial machine failures using ensemble learning techniques (XGB, LGBM, RF) with SHAP interpretability.

Análise Avançada de Dados com Causalidade e Aprendizado por Reforço

Análise do Impacto da Padronização de Markdown na Carga Cognitiva e Desempenho de Tarefas

GLM with sklearn, joblib and SHAP project

AI powered retail intelligence platform for demand forecasting, inventory optimization, explainable AI, and automated business insights, built with React, FastAPI, Python, MySQL, Redis, Celery, and Docker.

Análise Causal de Intervenções de Ansiedade com Algoritmos de Descoberta Causal

Análise de Intervenção para Ansiedade com Mediação Causal

Multi-operating condition analysis and interpretable modelling of EV battery system behaviour using regression and SHAP analytics.

Análise Aprimorada de Intervenção para Ansiedade com LLM Fine-Tuned

Análise de Intervenção em Ansiedade com Descoberta Causal

This project contains codes and paperwork based on the course CSI5155 at University of Ottawa (delivered by Professor Dr. Herna Viktor).

🌐 Predicting Customer Churn with Decision Tree, XGBoost & Neural Network Models on the Cell2Cell Dataset

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

Reproducible ML pipeline evaluating temporal leakage in Expected Pass Turnovers (xPT) models for football analytics. Compares 4 algorithms (mixed-effects logistic, penalised logistic, random forest, XGBoost) across leakage-inclusive and leakage-corrected feature sets. Supporting code for manuscript under review.

A comprehensive machine learning pipeline for cardiovascular disease prediction using deep neural networks with explainable AI capabilities.

Healthcare AI Assistant Pro is an advanced analytics platform that leverages machine learning and artificial intelligence to predict patient readmission risks. This enterprise-grade solution provides healthcare institutions with data-driven insights to improve patient care, reduce readmission rates, and optimize resource allocation.

This repository contains the Python implementation for the article "VITA: A Voice-based Intelligent Transformer-GMM Assessment Framework for Parkinson’s Disease Diagnosis". 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