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The repository contains Heart Disease Project which is solved using Machine Learning. It is deployed on the Flask server, implemented End-to-End by developing a Front End to consume the ML model and is deployed in AWS EC2 Instance and the Heroku Cloud. Refer to README.md for the working demo and application links.

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Tejas-TA/Machine-Learning-Heart-Disease-Prediction

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Machine Learning Heart Disease Prediction

AWS - http://ec2-3-129-176-183.us-east-2.compute.amazonaws.com:8080/
Heroku - https://predicting-heart-disease.herokuapp.com/

Heart Disease app predicts whether a person has a heart condition or not based on age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, and thal

Dataset

https://www.kaggle.com/ronitf/heart-disease-uci

Libraries Used

1. Flask
2. gunicorn
3. itsdangerous
4. Jinja2
5. MarkupSafe
6. Werkzeug
7. Pillow
8. Numpy
9. Scikit-learn
10. Pandas
11. Seaborn
12. Joblib
13. Matplotlib
14. HTML
15. CSS
16. Bootstrap
17. JavaScript

Project Walkthrough

1. Exploratory Data Analysis(EDA)
2. Data Visualization and Cleaning
3. Feature Engineering
4. Feature Selection and Scaling
5. Trained Machine Learning algorithms
6. Predicted models on test dataset
7. Model Evaluation
8. Exported the model
9. Created an EC2 instance in AWS running Ubuntu AMI and moved all the code to AWS cloud server
10. App running successfully in AWS and Heroku
11. Accuracies and Confusion matrices as below

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Blogs - https://tejasta.medium.com/
LinkedIn - https://www.linkedin.com/in/tejas-ta/
Email - [email protected]


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The repository contains Heart Disease Project which is solved using Machine Learning. It is deployed on the Flask server, implemented End-to-End by developing a Front End to consume the ML model and is deployed in AWS EC2 Instance and the Heroku Cloud. Refer to README.md for the working demo and application links.

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