BEGINNER - This is a classification project for the subject "Data Mining" in the 3rd year of Statistics (SSE) at the University of Milano-Bicocca.

Heart Failure/Heart Disease Prediction through Statistical Analysis and Machine Learning

A comprehensive exploration of machine learning techniques and data science best practices applied to the UCI Heart Disease dataset. Focusing on data preprocessing, exploratory analysis, and predictive modelling to identify key factors in heart disease. Part of Big Data Management and Analytics (BDMA) program.

A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.

CARDIOsetu is a web application designed to monitor individual heart health. It uses API integration to enable voice-to-text input for accessibility, making it easier for individuals with verbal and visual disabilities to interact with the app.

Kaggle Dataset Analysis on Exploring Important Factors to Heart Disease

Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

Machine learning project seeks to identify patterns to determine if a certain patient has heart disease

This project focuses on enhancing healthcare data security and privacy. We leveraged the Gaussian Differential Privacy (GDP) algorithm to protect individual patient information while enabling robust data analysis.

LPIII Mini Project

This is a machine learning project that uses various machine learning alogorithms to predict whether a patient is suffering from heart disease or not. Here I am using variour machine learning algorithms like Random Forest classifier, XGBClassifier, GaussianNB, Decision Tree Classifier, K-Nearest Neighbours and Logistic Regression.

A repository of the heart disease paper published on Springer

Code of the Cardiovascular Risk Prediction Project, which is used to identify risk factors for cardiovascular disease related to coronary heart disease and stroke in adults.

A tool for predicting Heart Disease probability based on ML model

Identification system for the molecular basis of coronary heart disease powered by AI ( Artificial Intelligence ) and machine learning algorithms.

My effort has been to do this project with logistic regression

In the ipynb file I'm running multiple ML classifier and regression algorithm's

Deploying a ML model using docker in Kubernetes

Applied Machine Learning Final Project