Data analysis and Machine Learning models to predict heart disease

A python3 program which used data analysis techniques to observe trends between various risk factors for heart diseases. Then, machine leaning models were created to predict whether a person has heart disease based on those risk factors. See my findings here!

Setup

Installation

1. Install Python 3.7 or above.
2. Install these modules:
   • NumPy
   • pandas
   • matplotlib
   • seaborn
   • scikit-learn

Running the program

1. Download the cleveland.csv file and heart_disease.py files.
2. Place them inside the same folder.
3. Open the aforementioned folder in your terminal (for MacOS and Linux) or command prompt (for Windows).
4. Type python heart_disease.py and press enter to run the program.

OR

1. Install any IDE.
2. Create a new project, copy the heart_disease.py code and paste it in a .py file.
3. Run the program.

Models used:

1. Logistic Regression
2. Support Vector Machine (SVM)
3. Gaussian Naive Bayes
4. Decision Trees

Contributing

Pull requests are welcome for adding more ML models or fixing exisiting issues.

License

MIT