An AI-driven healthcare tool that predicts early symptoms of diseases based on user input. Uses ML classification on patient data and symptom patterns for prediction and prevention suggestions.

⚽ A machine learning project that predicts the 2026 FIFA World Cup using FIFA rankings, historical match results, Poisson regression, and Monte Carlo simulation.

Simple IA de détection de spam

ChurnSense-AI: End-to-end telecom churn prediction system with XGBoost, SHAP explainability, SQL analytics, Power BI dashboards, and customer risk intelligence.

End-to-End MLOps Pipeline: Train, track, containerize, and deploy ML models via FastAPI REST API with automated CI/CD on Render.

End-to-end ML project that classifies emails as Spam or Not Spam using NLP, TF-IDF, Logistic Regression and Streamlit.

An end-to-end Machine Learning application that predicts the likelihood of heart disease based on patient medical attributes. The project demonstrates the complete ML workflow, including data preprocessing, exploratory data analysis, model training, evaluation, and deployment through a user-friendly Flask web application.

Fine-tuned DistilBERT for political misinformation classification with cross-dataset evaluation across LIAR and Fakeddit benchmarks, compared against Logistic Regression, Naive Bayes, and SVM baselines.

Machine Learning project for predicting customer purchase behavior using a Decision Tree Classifier on the Bank Marketing Dataset with data preprocessing, feature importance analysis, confusion matrix visualization, and decision tree interpretation.

A complete financial data analytics project leveraging Python, SQL, Power BI, and machine learning for stock analysis, portfolio optimization, risk assessment, and time-series forecasting.

Python-based sales analytics project that transforms raw sales data into actionable business insights through data cleaning, KPI analysis, and interactive visualizations.

Machine Learning project for predicting autonomous vehicle accident severity using DMV collision data using classification models.

Cost-sensitive ML pipeline for diabetes risk classification — custom 5:1 false-negative penalty, threshold optimization, and SHAP interpretability on a 100K-patient dataset

Customer Segmentation using K-Means Clustering with Python and Scikit-Learn

AI-powered IPL analytics platform with match prediction, team/player insights, and Explainable AI (XAI) — built with Flask, scikit-learn & Plotly.

Java program that plots random points in a 3D space and assigns their (x, y, z) coordinates their respective [R, G, B] values and displays them in the plot with their RGB color.

ML-based web app predicting diabetes, heart disease & Parkinson's using SVM, Logistic Regression & Random Forest

NLP and ML system that recommends government jobs and predicts salaries based on user skill profiles.

A deep learning project that predicts a student's **chance of admission** to a graduate program based on academic and profile features.