ICT707 Data Science Practice: Predicting Chronic Heart Disease

This project delves into predicting chronic heart disease using data science techniques. The student utilized the Framingham Heart Study dataset and implemented various machine learning algorithms, including collaborative filtering, logistic regression, and k-means clustering. The project involved data exploration, preprocessing, and model evaluation. The student assessed the performance of each algorithm, with logistic regression emerging as the most effective for predicting the likelihood of chronic heart disease within a ten-year timeframe. The report includes detailed explanations of the algorithms, results, and relevant visualizations, along with references to support the methodology and findings. The project aims to demonstrate the practical application of machine learning in healthcare and disease prediction.