Heart_Disease
Heart Disease Predictor
This project applies machine learning techniques to predict heart disease based on various health indicators
Machine Learning
1 stars
1 watching
1 forks
Language: Jupyter Notebook
last commit: over 3 years ago
Linked from 1 awesome list
decision-treesknn-classificationmatplotlib-pyplotnumpypandasrandom-forestseabornwarnings
Related projects:
Repository | Description | Stars |
---|---|---|
| An R implementation of Hellinger distance decision tree algorithm. | 10 |
| Predicts cell health from morphological profiles using machine learning and image analysis | 35 |
| Recurrent neural network model for predicting people's next location based on spatial and temporal features | 28 |
| Develops machine-learning models to predict conflict risk from climate and environmental factors. | 7 |
| This project uses deep learning to analyze chest radiographs and diagnose COVID-19. | 50 |
| Develops machine learning models to detect breast cancer from mammography images using deep learning techniques | 378 |
| Develops machine learning models to predict and understand drought using climate science data | 93 |
| This project trains machine learning models to predict stock returns using various algorithms and techniques. | 1,745 |
| An open-source project evaluating deep learning methods for predicting ligands and drugs in chemogenomics | 3 |
| Matlab implementation of a deep learning-based method for classifying hyperspectral images | 56 |
| An experimental project comparing different machine learning algorithms and ensemble methods for predicting stock market trends. | 1 |
| Develops a deep learning model for large-scale object detection that leverages hybrid knowledge and routing mechanisms. | 105 |
| A machine learning-based system for detecting mitosis in breast cancer tumor cells from images | 23 |
| Develops an automatic prediction model for breast cancer proliferation scores from whole-slide histopathology images using deep learning techniques. | 207 |
| An open-source software tool for applying consensus clustering to multi-omic data for disease subtyping | 5 |