Bank GoodCredit wants to predict cred score for current credit card customers. The cred score will denote a customer’s credit worthiness and help the bank in reducing credit default risk.

Developed a machine learning pipeline to predict customer churn with over 90% accuracy, leveraging data preprocessing, feature engineering, and Random Forest modelling. Conducted exploratory data analysis to uncover key drivers of churn, such as customer recency and cohorts from first transations.

A research study on How do factors like alcohol consumption, age, ethnic background, and medical history affect the risk of developing Alzheimer's disease?

This project focuses on sentiment analysis of the NLTK movie reviews dataset using machine learning. I implemented and compared Multinomial Naive Bayes and Logistic Regression models after preprocessing text data with TF-IDF vectorisation. The goal was to classify reviews as positive or negative.