This repository, "heart-disease-classification-ml", houses a Python-based machine learning project aimed at classifying whether a patient has heart disease based on a set of medical features.
The project utilizes a comprehensive dataset of patient records, each characterized by a unique set of medical features. The primary objective is to employ machine learning techniques to predict the presence or absence of heart disease in each patient.
A rigorous data analysis process has been undertaken to ensure the quality and reliability of the dataset. This process includes data cleaning, preprocessing, and exploratory data analysis. Through this analysis, we have gleaned important insights from the dataset, which have been thoroughly documented and explained within the project.
The heart disease prediction has been approached using various machine learning models from the scikit-learn library. Each model's performance has been evaluated and compared to identify the most effective approach. Furthermore, we have fine-tuned the models to enhance their performance and improve the accuracy of our heart disease predictions.
This project serves as a practical application of machine learning in the healthcare domain, demonstrating the potential of machine learning in aiding medical diagnostics.