This repo contains machine learning projects for beginners.
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Updated
May 31, 2023 - Jupyter Notebook
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
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.
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 is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.
Heart Failure prediction using machine learning python
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.
Explore a modular, end-to-end solution for heart disease prediction in this repository. From problem definition to model evaluation, dive into detailed exploratory data analysis. Experience seamless integration with MLOps tools like DVC, MLflow, and Docker for enhanced workflow and reproducibility.
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