Preventing heart diseases has become more necessary than ever. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, ensuring that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting heart diseases, and the predictions made are quite accurate.
This project involves the analysis of the heart disease patient dataset with proper data processing. Different models were trained and predictions were made using various algorithms like KNN, Decision Tree, Random Forest, SVM, Logistic Regression, etc. This repository contains the Jupyter notebook code and dataset used for the Kaggle kernel 'Binary Classification with Sklearn and Keras'.
I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not.
- Logistic Regression (Scikit-learn)
- Naive Bayes (Scikit-learn)
- Support Vector Machine (Linear) (Scikit-learn)
- K-Nearest Neighbours (Scikit-learn)
- Decision Tree (Scikit-learn)
- Random Forest (Scikit-learn)
- 90% (Random Forest)