Successfully built a crop yield prediction system using advanced machine learning in R, which leverages agro-environmental, soil, and weather data to forecast agricultural yield (in tons per hectare). It features an automated pipeline for data preprocessing, model training, and evaluation, to support data-driven agricultural decisions.

Simple R package for basic data cleaning tasks

Successfully developed a robust customer churn prediction system using advanced machine learning models in R, leveraging techniques like logistic regression, decision trees, random forests, boosting, bagging, SVM, neural networks, and regularization methods (Lasso/Ridge).