The goal of this project is to build a machine learning model to predict customer churn for Camtel 📡, a major telecommunications provider. Customer churn—the rate at which customers leave the service—is a key challenge that directly impacts revenue 💸 and business stability 🏢.

Developed a sentiment analysis model to measure tweet positivity across regions using advanced NLP techniques. This project involved data preprocessing, feature engineering with TF-IDF and Doc2Vec, and training supervised machine learning models. Performance was validated using cross-validation and metrics like accuracy and precision

A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.

End-to-end machine learning projects involve the complete process of developing a machine learning model, starting from data collection and preprocessing to training, evaluation, and deployment. These projects encompass data exploration, feature engineering, model selection, performance evaluation, and integration with production systems