Module 3: Adding Data Science to a Web Application
Module Overview
You have your application, you have your data - now it's time for science! Let's use what we've learned throughout the program to add some useful intelligent functionality to our web application.
Learning Objectives
- Add a machine learning model to our web server that generates predictions when passed the appropriate inputs
- Add routes to our app that will listen for POST HTTP requests (form submissions) and respond accordingly
- Display appropriate messages on the screen after user actions including error messages when invalid actions are taken
Guided Project
Open guided-project.md in the GitHub repository to follow along with the guided project.
Module Assignment
Reproduce the lecture tasks (logistic regression fitting, predicting, returning) in a REPL/notebook with different real data, incorporate predictive code in the application, and add forms for user interaction with the predictive model.
Assignment Solution Video
Additional Resources
Data Science Tools
- Scikit-Learn Linear Models
- Scikit-Learn StandardScaler
- Scikit-Learn Model Persistence
- Python Pickle Module