The primary objective of creating the heart disease prediction model is to develop a robust and accurate tool for early detection and risk assessment of heart diseases. By leveraging machine learning algorithms, the model aims to analyze diverse medical parameters and provide timely predictions, enabling proactive healthcare interventions. Ultimately, the goal is to enhance preventive care strategies and contribute to better patient outcomes by identifying potential heart-related risks in advance.

https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction

1. MaxVoting
2. Logistic Regression
3. Bagging
4. Gradient Boosting Classifier
5. Naïve Bayes
6. Support Vector Machine (SVM)
7. K-Nearest Neighbors (KNN)
8. Random Forest Classifier
9. Decision Tree Classifier

[Maximum Accuracy achieved: 93% (approx) using MaxVoting]

https://b87c4231-668a-48e2-a055-42cb89e1676e-00-1rsj2y8z34rp4.picard.replit.dev/