This project helps to predict whether a person has heart disease or not using certain medical parameters given to the system.
First,we download the dataset from UCI repository . Then we have to apply feature engineering into dataset to clean the data , feature scaling , data pre-processing and find correlation between different features. Then we divide our dataset into two parts , independent features and dependent feature . In dependent feature we consider target and independent feature consider rest of the column . Then we divide the dataset into two sets, first part train dataset and second part test dataset . After that we choose an algorithm for classification and then train it and perform hyperparameter tuning.
After creating the model we have to deploy our Classification model into web application . For that purpose we use a Python framework called Flask . Flask is basically used to link our model with templates made using HTML and CSS. Different app routes are also created in our app.py file.
To deploy the project, We follow this documentaion - https://docs.microsoft.com/en-us/azure/app-service/ .
Created a new resource group . Made and configured a new web app resource by using Azure Web App service . In the resource deployment center, We set the source as Github and selected the project repository from the Github account. Finally, We deploy the mentioned Github repository . We then test our project by giving the medical parameters to the web app and submit the form. At the bottom of the page, 0 is displayed if the person dosen't have heart disease. 1 is displayed if the person has heart disease.
In this project We have learnt about how to load dataset , how to apply feature engineering ,how to find feature correlation, how to fit model into dataset , how to check accuracy of model , how to do hyperparameter tuning, how to deploy our web app and many more things.
https://github.com/Tushar00728/Heart-Disease-predictor-Azure
http://heartdiseasepred.azurewebsites.net/ (May take 5 minutes to load up).