⚽ 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.
ChurnSense-AI: End-to-end telecom churn prediction system with XGBoost, SHAP explainability, SQL analytics, Power BI dashboards, and customer risk intelligence.
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.
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.
Simple IA de détection de spam
Customer Segmentation using K-Means Clustering with Python and Scikit-Learn
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 credit risk prediction using Logistic Regression, Random Forest, Gradient Boosting, SMOTE, and model interpretability.
AI-powered IPL analytics platform with match prediction, team/player insights, and Explainable AI (XAI) — built with Flask, scikit-learn & Plotly.
End-to-end machine learning project for predicting industrial machine failures using Logistic Regression.
Cost-sensitive credit-card fraud detection: tuned LightGBM + a dollar-cost-optimized decision threshold, with a live interactive dashboard.
Digital twin for manufacturing equipment, live sensor ingestion, ML-based anomaly detection, and predictive maintenance (MTBF/MTTR/OEE tracking)
Python-based sales analytics project that transforms raw sales data into actionable business insights through data cleaning, KPI analysis, and interactive visualizations.
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.
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.
Collection of machine learning tasks covering classification, regression, clustering, feature engineering, and model evaluation.
Optimal Phishing Detection is a research-based machine learning project designed to identify phishing websites and distinguish them from legitimate websites. The project analyzes different URL and website-related features and applies multiple machine learning algorithms to find an effective detection model. It also uses a risk scoring approach.
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.
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