Bandersnatch Project

Introduction

Bandersnatch is a data science and machine learning project that allows you to work with monster data, create visualizations, and build a machine learning model. The project is structured as a series of sprints that build on each other, guiding you through database operations, data visualization, and machine learning model development.

Project Structure

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

Sprint Tickets

Work through these sprints in order:

1. Sprint 1: Database Operations - Set up MongoDB and create monster data
2. Sprint 2: Dynamic Visualizations - Create interactive visualizations with Altair
3. Sprint 3: Machine Learning Model - Build and integrate a machine learning model

Getting Started

Fork and Clone the Repository

1. Fork this repository to your GitHub account
2. Clone your forked repository locally:

git clone https://github.com/your-username/BandersnatchStarter.git
cd BandersnatchStarter

Setup Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate
python -m pip install --upgrade pip setuptools wheel
python -m pip install -r requirements.txt

macOS/Linux

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade pip setuptools wheel
python3 -m pip install -r requirements.txt

Alternatively, macOS/Linux users can use the provided install script:

source venv/bin/activate
./install.sh

Running the Application

Windows

python -m app.main

macOS/Linux

python3 -m app.main

Or use the provided run script:

./run.sh

The application will be available at http://127.0.0.1:5000/

Deploying to Render.com

1. Create a free account on Render.com
2. From your dashboard, click "New" and select "Web Service"
3. Connect your GitHub repository or use the public GitHub URL
4. Configure your web service:
   • Name: Choose a name for your application
   • Environment: Python
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app.main:APP
5. Add environment variables:
   • Add your MongoDB connection string as DB_URL
6. Click "Create Web Service"

Your application will be deployed and available at a URL provided by Render.

Tech Stack

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

Database Setup (for Sprint 1)

1. Sign up for a MongoDB account: MongoDB
2. Create a "Shared Cluster" (free tier)
3. Add your IP address to the allowed locales list
4. Copy the connection string into a .env file in your project root:

   DB_URL=mongodb+srv://<username>:<password>@<cluster>.<project_id>.mongodb.net
   

Windows-Specific Notes

• Gunicorn is not Windows compatible, so don't use the run.sh script on Windows

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