⚽ A machine learning project that predicts the 2026 FIFA World Cup using FIFA rankings, historical match results, Poisson regression, and Monte Carlo simulation.
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.
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 ML project that classifies emails as Spam or Not Spam using NLP, TF-IDF, Logistic Regression and Streamlit.
End-to-End MLOps Pipeline: Train, track, containerize, and deploy ML models via FastAPI REST API with automated CI/CD on Render.
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.
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.
Cost-sensitive credit-card fraud detection: tuned LightGBM + a dollar-cost-optimized decision threshold, with a live interactive dashboard.
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 autonomous vehicle accident severity using DMV collision data using classification models.
Implement Support Vector Machines using Scikit-Learn in Python to perform both binary (2-class) and multi-class (all 10 classes) image classification on CIFAR-10.
Cost-sensitive ML pipeline for diabetes risk classification — custom 5:1 false-negative penalty, threshold optimization, and SHAP interpretability on a 100K-patient dataset
End-to-end machine learning project for predicting industrial machine failures using Logistic Regression.
Digital twin for manufacturing equipment, live sensor ingestion, ML-based anomaly detection, and predictive maintenance (MTBF/MTTR/OEE tracking)
End-to-end machine learning project for credit risk prediction using Logistic Regression, Random Forest, Gradient Boosting, SMOTE, and model interpretability.
Python-based sales analytics project that transforms raw sales data into actionable business insights through data cleaning, KPI analysis, and interactive visualizations.
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.
A supervised machine learning pipeline for potential fraud-risk classification using SMOTE, Logistic Regression, and Random Forest.
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