Flask build data science app | deploy Machine Learning Model using Flask | Flask Predictive Analyti

In this video, I will continue with building a data science app in flask. Flask is Python's micro web framework. It is classified as a microframework because it does not require particular tools or libraries.
In the previous session we build a basic app and displayed a template. I will cover the html template and review the building blocks of it. Then I will showcase how to pass form to Flask and return a response from Flask backend. Finally, I will cover how to save a ml model to file system and import it into Flask application and how to use in a web application.

Link to GitHub Code and template: https://github.com/hnawaz007/pythondataanalysis/tree/main/Flask
Link to Jupyter Notebook: https://github.com/hnawaz007/pythondataanalysis/blob/main/ML/Simple%20Linear%20Regression.ipynb

Link to Simple Linear Regression video:
https://www.youtube.com/watch?v=TR6vn4lZ3Mo

Add following lines of code to save model to file system:
# Let's save the model to disk
import pickle
# Save the model
pickle.dump(lr, open('simple_linear_regression.pkl', 'wb'))

#Flask #Python #FlaskmodelDeployment #predictiveanalytics #ml

You can get the code editor I use here: https://www.jetbrains.com/pycharm/
Overview of Flask: https://flask.palletsprojects.com/en/2.0.x/quickstart/

Subscribe: https://www.youtube.com/c/HaqNawaz
Github: https://github.com/hnawaz007
Instagram: https://www.instagram.com/bi_insights_inc/

Topics covered in this Deploy Machine Learning Model in Flask Video:
00:00 - 00:25 Introduction to building data science app
00:26 - 02:28 Overview of html template
02:29 - 03:46 Build Flask route for Post request
03:47 - 04:56 Test Input and return value
04:57 - 06:20 Import & configure ML model
06:21 - 07:15 Test model from web UI