Predicts telecom customer churn using a Random Forest classifier on the Telco Customer Churn dataset. Includes preprocessing, SMOTE for class imbalance, and evaluation via ROC AUC, confusion matrix, and feature importance.
Physics-informed AI system for predicting aircraft bird strike risk using impact force calculations and machine learning techniques.
End-to-end machine learning project to predict customer churn using Python
ML Powered customer churn prediction for U.S. insurance 81% accuracy, 21% revenue uplift potential using Python, scikit-learn, SQL, and Power BI
Machine Learning project that predicts relationship compatibility, compatibility score, and relationship longevity using personality and lifestyle features.
Machine-learning phishing website detection system implemented as a Chrome extension with a Flask API backend.
Remote-sensing analysis of land-use and land-cover change in Ibadan Metropolitan Area using Landsat and Random Forest classification (2013–2023).
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