Heart-Disease-Prediction-Deployment

Heart-Disease-Prediction

Overview

A simple web application which uses Machine Learning algorithm to predict the heart condition of a person by providing some inputs about the person health like age, gender, blood pressure, cholesterol level etc built using Flask and deployed on Heroku.

Motivation

As being a Data and ML enthusiast I have tried many different projects related to the subject but what I have realised is that Deploying your machine learning model is a key aspect of every ML and Data science project. Everything thing I had studied or been taught so far in my Data science and ML journey had mostly focused on defining problem statement followed by Data collection and preparation, model building and evaluation process which is of course important for every ML/DS project but what if I want different people to interact with my models, how can I make my model available for end-users? I can't send them jupyter notebooks right!. That's why I wanted to try my hands on complete end-to-end machine learning project.

• To View the Deployed Application, click on the link given below : Heart Disease Predictor Web App - https://heart-disease-predictor-flask.herokuapp.com/

• To get the Code for Exploratory data analysis/visualisations, different algorithms used and the model evaluation, click on the link mentioned below : Link of jupyter notebook - https://github.com/asthasharma98/Data-Science/blob/main/Heart%20Disease%20Prediction/heart_disease_prediction.ipynb

A Demo of the Web App :

Technical Aspect

This Project is mainly divided into two parts:

1. Exploring the dataset and traning the model using Sklearn.
2. Building and hosting a flask web app on Heroku.

About the repository Structure :

• Project consist app.py script which is used to run the application and is engine of this app. contians API that gets input from the user and computes a predicted value based on the model.
• prediction.py contains code to build and train a Machine learning model.