Skip to content

An app that generates RPG monster data, visualizes, and then models it.

Notifications You must be signed in to change notification settings

Crystal-Collins/BandersnatchStarter

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

42 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Bandersnatch Project

Read the Documentation for information on how to get started.

Deployed App

Tech Stack

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

Provided Code

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

Primary Features by URL

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

Primary Goals

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

Stretch Goals

  • 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

About

An app that generates RPG monster data, visualizes, and then models it.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 40.9%
  • CSS 32.1%
  • HTML 26.2%
  • Other 0.8%