Binary Classification Using SVM - Heart Attack Prediction

This repository contains a project that uses Support Vector Machines (SVM) for binary classification to predict the likelihood of heart attacks based on patient data. The goal is to provide an accurate and efficient predictive model for early detection.

Files and Directories

• README.md: Documentation of the project.
• SVM_Heart_disease.ipynb: Jupyter notebook with the implementation of the SVM model.
• heart.csv: Dataset containing patient health data and heart attack occurrence labels.
• report.pdf: Detailed analysis and findings of the project.

Features

• Data preprocessing and exploration of the heart dataset.
• Implementation of an SVM model for binary classification.
• Evaluation of model performance using metrics such as accuracy and confusion matrix.
• Insights into features contributing to heart attack prediction.

How to Use

1. Clone this repository:

   git clone https://github.com/naveensankar5905/Binary-classification-using-SVM---heart-attack.git

2. Install required dependencies:

   pip install -r requirements.txt

3. Open the Jupyter notebook:

   jupyter notebook SVM_Heart_disease.ipynb

4. Load the heart.csv dataset and run all the cells to train and evaluate the model.

Dataset

The dataset contains features such as age, cholesterol levels, blood pressure, and other health metrics, labeled with 1 (indicating heart attack) or 0 (no heart attack).

License

This project is licensed under the MIT License.

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© 2024 Naveen Sankar RS

All rights reserved.