ML-Capstone1-Project

Machine Leaning Model for Heart Disease Prediction

Welcome to our Machine Learning repository dedicated to Heart Disease prediction. This collection features a diverse dataset and explores predictive models to enhance understanding and accuracy in identifying potential cardiovascular risks.

Github repo: https://github.com/Itssshikhar/ML-Capstone1 Dataset: https://www.kaggle.com/datasets/utkarshx27/heart-disease-diagnosis-dataset

Dataset

Heart Disease Prediction Dataset. Predicting the Presence or Absence of Heart Disease Based on Various Factors

Features:

-- 1. age
-- 2. sex
-- 3. chest pain type (4 values)
-- 4. resting blood pressure
-- 5. serum cholestoral in mg/dl
-- 6. fasting blood sugar > 120 mg/dl
-- 7. resting electrocardiographic results (values 0,1,2) -- 8. maximum heart rate achieved
-- 9. exercise induced angina
-- 10. oldpeak = ST depression induced by exercise relative to rest
-- 11. the slope of the peak exercise ST segment
-- 12. number of major vessels (0-3) colored by flourosopy
-- 13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect -- 14. Target(Absence (1) or presence (2) of heart disease)

Capstone1 Project Requirements (Evaluation Criteria)

• Problem description
• EDA
• Model training
• Exporting notebook to script
• Model deployment
• Reproducibility
• Dependency and environment management
• Containerization
• Cloud deployment

Dependency and Environment Management Guide

You can easily install dependencies from requirements.txt and use virtual environment.

• pip install pipenv

• pip shell

• pip install -r requirements.txt

If can't or don't know how to, here are the needed packages, just run

• pip install pipenv Flask==3.0.0 graphviz==0.20.1 matplotlib==3.8.0 numpy==1.26.1 pandas==2.1.2 Requests==2.31.0 scikit_learn==1.3.1 seaborn==0.13.0

Depolyment Guide

To run it locally:

• Run python predict.py on a terminal
• Open a terminal and run python test_predict.py

To run it docker:

• Download and run Docker Desktop: https://www.docker.com/

• Open a terminal

• docker build -t capstone_test .

• docker run -it --rm -p 6969:6969 capstone_test

• Open a new terminal and run python test_predict.py

To run it in cloud:

• This is still being worked on.