Built this project after taking inspiration from a lot of projects that I saw on youtube based on COVID19. I'm glad I completed this project. Even though I did not know all the tech used, I learned all of it in the process of making this project.😊
Build an application to combine my ML + web development skills. Create a Web application to predict the patient's probability of the contract of COVID19 based on the given symptoms.
Python libraries - pandas, NumPy, Sklearn, pickle;
and Visual Studio Code.
Generated a dataset that contains 6 columns, where 5 columns are namely age(1-100), Body temperature in Fahrenheit(98-104), Body pain(0/1), Cough(1/0), Difficulty in breathing(-1/0/1) and the 6th column tells if the person has the disease or not(0/1).
I trained it using logistic regression and predicted the probability. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).
You can see how I used the logistic regression on the dataset here.
Used the python pickle library to save the model. The pickle module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure. “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as “serialization” or “flattening”, however, to avoid confusion, the terms used here are “pickling” and “unpickling”.
Used simple HTML and Bootstrap to create the UI. Used basic elements like navbar, forms, and buttons. You can check the screenshots of the App below.
Now the only thing left is to combine everything using the Flask web framework. Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.
You can understand how I did it here.
Source code can be viewed at the GitHub repository given below. 🎉
Live demo of the project is shown below.
Do check it out and provide feedback. Thank you!💖