This repo contains machine learning projects for beginners.

Predicting readmission risk in heart failure patients using machine learning algorithms and patient data.

Heart Failure Prediction Model (ML) Logistic Regression 85% Accuracy

Machine Learning based Heart Failure Detection

Using data about patients with a heart disease, I created a prediction model for the death event of a patient. I did extensive data preprocessing, added meaningful visualizations, and eventually created a Random Forest model for this problem. I used Pandas, Scikit-Learn, Seaborn, Matplotlib, Numpy, etc.

Hearth Failuire Prediction Analysis: classification task to predict whether patient had a heart disease event or not.

Developing, Evaluating, and Comparing different Classification Models on Heart Failure Prediction Dataset

Binary classification model using PyTorch to predict heart failure using clinical data.

A project about heart failure prediction using classification models. This project is related with MLZoomcamp 2022 midterm project.

This repository contains code and a dataset for predicting heart failure rates using PyTorch. The predictive model is built upon the "Heart Failure Clinical Records Dataset" obtained from Kaggle, which includes various clinical features related to heart health.

This repository contains a web application designed to predict the likelihood of heart disease in patients using machine learning models.

Application to predict 10 year risk of heart failure. The application also allows storage (consented) of submitted patient data + real-time analysis of the data in database. Machine learning model trained and tested using Python (FraminghamModel.ipynb) and deployed as a Django web app. see http://new-hf-predictor.herokuapp.com/ for demo

Binary Classification Project

Heart Failure Prediction with Model Deployment

A heart failure prediction model, crafted through the utilization of pandas, numpy, seaborn, and matplotlib, holds immense potential for real-life impact. By analyzing key health indicators, such as age, blood pressure, and cholesterol levels, the model facilitates early identification of individuals at risk of heart failure.

New decision support system in predicting heart failure using logistic regression algorithm

It's a straightforward Matlab code that can predict the patient's heart failure.

This repository contains a machine learning algorithm written for predicting whether a person can suffer from heart failure or not based on their habits and numerical data related to their health.

Utilizing Principal Component Analysis (PCA) for insightful feature reduction and predictive modeling, this GitHub repository offers a comprehensive approach to forecasting heart disease risks. Explore detailed data analysis, PCA implementation, and machine learning algorithms to predict and understand factors contributing to heart health.

Heart Failure prediction using machine learning python