Streaming Package Comperator

Griaß eich! 🐄 This solution to the GenDev Challenge 2024 employs ✨Mixed Integer Programming.✨

🚀 Live Demo

Demo: https://streaming-package-comperator.vercel.app/
(Note: This is hosted on a free tier. The first query may take up to one minute because the backend is automatically deactivated when idle.)

💡 Main Idea

At the core of this application lies a mathematical model known as a Mixed Integer Program (MIP), which determines the optimal combination of streaming packages to minimize the cost of watching a given set of games. 📊

This mathematical model is solved using the PuLP solver in Python. 🐍

📽️ Watch this video where I showcase the application and delve deeper into the implementation and future improvements.

📚 For those who want to dive deeper into the mathematical details, see the explanation_for_nerds.ipynb file, which provides a detailed explanation of the underlying math.

🔧 Possible Future Improvements

Frontend 🖥️:

• 📋 Implement a table view where users can select specific games.
• 🏆 Add a tournament selection feature.
• 💰 Create a 'set maximum cost' option: Remove the worst deals iteratively until total_cost < maximum_cost.

Backend 🛠️:

• 📂 Currently, each query accesses the CSV files as provided in the problem statement. Accessing the data through a merged CSV file or a database could significantly improve speed.
• 🔄 The backend processes the dataframe in multiple loops. Consolidating operations into fewer loops could enhance performance.
• ✂️ The pruning logic (removal of the worst deals) is currently implemented on the frontend. Moving this to the backend could further optimize performance.
• ⏰ Lower time granularity for large queries. Adjusting the timedelta to one week or month instead of daily intervals could dramatically reduce computational complexity.
• ⚡ Use a more advanced solver like Gurobi or CPLEX for faster and more efficient performance, especially for larger queries or more complex constraints.

Theory/Math 📐:

• ❓ After pruning, there is no guarantee that the model remains optimal. Implementing methods from Sensitivity Analysis could help verify if re-solving is necessary.
• 📦 There are alternative ways to model this use case and related scenarios. For example, if users set a maximum monthly or yearly budget, it could be modeled as a Cutting Stock Problem.

🛠️ Local Setup

🔙 Backend

0. Ensure Python and pip are installed 🐍.

1. Install the required dependencies:

   cd backend
   pip install -r requirements.txt
   

2. Run the Flask application 🚀:

   python app.py
   

The backend should now be running on http://127.0.0.1:5000.

🌐 Frontend

0. Ensure Node.js and npm are installed 🌳.

1. Rename example.env to .env:

   • Linux/macOS 🐧:

     cd frontend
     mv example.env .env
     

   • Windows 🪟:

     cd frontend
     ren example.env .env
     

2. Install the required dependencies:

   npm i
   

3. Start the development server 🔥:

   npm run dev
   

The frontend should now be running on http://localhost:5173/.