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Read the Documentation for information on how to get started.

Deployed App

• Logic: Python3
• API Framework: Flask
• Templates: Jinja2
• Structure: HTML5
• Styling: CSS3
• Database: MongoDB
• Graphs: Altair
• Machine Learning: Scikit
• Hosting: Heroku

• HTML Templates
• CSS Styles
• API Framework
• Miscellaneous Helper Files
• Sprint Specific Documentation

• /: Splash Page
• /data: Tabular Data
• /view: Dynamic Visualizations
• /model: Interactive Machine Learning Model

For best results, complete each sprint in order, before going on to the next sprint.

1. Sprint 1: Database Operations
   • Develop a database interface class
   • Create random data
   • Populate the database with at least 1000 datapoints
2. Sprint 2: Dynamic Visualizations
   • Notebook exploration
   • Chart function
   • API integration
3. Sprint 3: Machine Learning Model
   • Notebook exploration
   • Machine Learning interface class
   • Model serialization (save and open)
   • API model integration

• Use ElephantSQL instead of MongoDB
• Use Plotly instead of Altair
• Use PyTorch instead of Scikit
• Use FastAPI instead of Flask
• Add the ability for the user to reset & reseed the database
• Add the ability for the user to re-train the machine learning model
• Add the ability for the user to download a working serialized model and dataset
• Add authentication to sensitive pages
• Use a different set of features to train the model
• Use your own dataset entirely

• Windows users should not use therun.sh shell script, as it depends on gunicorn.
• Windows users should usepy -m app.main to start the app with Flask acting as the server.
• Mac and Linux users can use./run.sh script or type the command directlypython3 -m gunicorn app.main:APP.
• Feel free to modify the shell scripts to suit your needs, these are intended to run locally.
• In any case you should not modify the Procfile, this is the run script for the remote server.