A Data Science Supervised Machine Learning Project to predict multiple diseases like Diabetes, Heart Disease etc from symptoms given by user using Support Vector Machine Algorithm

🫀 Check your heart disease risk using ML, powered by Streamlit! 💖

This project aims to predict heart disease using machine learning models and ensemble methods. The goal is to build a model that can accurately predict the presence of heart disease based on various medical attributes. Evaluations are done using the Cleveland dataset.

show who has heart disease by RandomForestClassifier

I serve as a mentor in their initiative "Mentober" - a month-long mentorship program, wherein I guide my mentee in building a strong profile and helping her in developing technical skills in Web Development & Machine Learning.

Final Project of the Machine Learning I Course - Artificial Intelligence Specialization - Embedded Systems Lab - University of Buenos Aires. Data Analysis and Model Development for the UCI Heart Disease Dataset.

Second project of computational intelligence course

Repository for multiple pattern recognition algorithm for heart disease prediction

Heart Disease Prediction using SVM Classifier in Python

Heart Disease Prediction System Developed a machine learning model to predict heart disease using 13 key medical parameters (e.g., BP, cholesterol, chest pain type). Achieved 85% accuracy, enabling early detection and intervention strategies. Utilized algorithms like Logistic Regression, SVM, and Random Forest.

A heart disease identification machine learning workflow.

This project uses Logistic Regression to predict the likelihood of heart disease based on medical attributes such as age, cholesterol levels, and blood pressure. It includes model training, evaluation, and an interactive Gradio interface for real-time heart disease risk prediction.

Bayesian prediction for ❤️ diseases

The project is based upon the kaggle dataset of Heart Disease UCI. The final model is generated by Random Forest Classifier algorithm, which gave an accuracy of 88.52% over the test dataset that is generated randomly choosing of 20% from the main dataset.

Voting Classifier and Artificial Neural Network for predicting heart disease.

Web application using Flask and Machine Learning model to predict heart disease.

Ensemble Learning

A system that predicts the risk of heart diseases in patients using information such as age, sex, chest pain, serum cholestoral, thalassemia, exercise-induced angina, resting electrocardiographic results, etc.

An initiative to predict heart disease earlier using various parameters input to a machine learning model trained on a dataset.

# R-project Heart Disease Dataset The data analyzed in the project comes from a publicly available database and deals with aspects related to heart disease: https://archive.ics.uci.edu/ml/datasets/heart+disease **Project goals** The purpose of the project was to analyze and present the data presented in the Heart Disease Dataset.