This repository contains a machine-learning model developed to predict the likelihood of heart disease based on a set of medical attributes. The project uses various classification algorithms to analyze the data and determine the most accurate model for prediction.
The goal of this project is to classify whether a patient has heart disease based on features such as age, sex, chest pain type, resting blood pressure, cholesterol level, and more. The dataset used in this project is sourced from the UCI Machine Learning Repository.
- Data Preprocessing: Handling missing values, encoding categorical features, and normalizing data.
- Model Training: Implementation of multiple classification algorithms including Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forests.
- Model Evaluation: Comparison of model accuracy, precision, recall, and F1 score to identify the best performing model.
- Visualization: Graphical representation of data distributions and model performance metrics.
Ensure you have the following installed:
- Python 3.7+
- Required Python libraries (listed in requirements.txt)
- Clone the repository:
bash
git clone https://github.com/ceodaniyal/heart-disease-project.git
cd heart-disease-project
- Install the required libraries:
bash
pip install -r requirements.txt
Run the Jupyter Notebook:
bash
jupyter notebook Heart_Disease_Classification.ipynb
Open the Jupyter Notebook and follow the steps to preprocess the data, train the models, and evaluate their performance. Modify the notebook to test additional models or tune hyperparameters.
The best-performing model achieved an accuracy of XX% (replace with actual value).
This project is licensed under the MIT License.