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An intelligent Steam deal analytics dashboard leveraging Python, MySQL, and Power BI to surface the most worthwhile discounts.

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SteamSmartBuy 📉🎮

SteamSmartBuy is a smart data analysis dashboard built to help gamers track and discover the best Steam deals. It leverages a combination of API-driven data collection, hands-on data analysis, and visual storytelling through Power BI—helping users make smarter purchasing decisions based on pricing history, discount patterns, and game quality.


🚀 Try It Out!

Check out the dashboard here:
🔗 Power BI Report


💡 Overview

This project integrates multiple data sources and analytical techniques to create a centralized view of Steam game pricing and deal quality. Our goal is not just to surface discounts—but to evaluate whether a deal is actually worth it using historical data, scarcity metrics, and quality scores.


📊 Features

  • 🧠 Deal Scoring Engine that evaluates discounts by factoring in current deal depth, historical trends, frequency, and scarcity—alongside game quality indicators.
  • 📈 Price and Deal History Visualization showing how pricing and discount behavior evolve over time.
  • 🔍 Interactive Power BI Dashboard for intuitive filtering, scoring, and searching—easily spot the best deals or search for your favorite titles.
  • 🔮 Built for Expansion, with predictive features (e.g. estimating when the next good deal might arrive) planned in future updates.

🔧 Architecture

  1. Data Collection & Analysis:

    • Python scripts handle data gathering and preprocessing.
    • Performed extensive analysis to calculate historical deal strength, frequency, and game review metrics to support the scoring logic.
  2. APIs Used:

    • 🛒 IsThereAnyDeal API – for price logs and deal data across multiple stores.
    • 🧪 Steamworks Web API – for metadata, user review rates, and pricing info.
    • 🎮 RAWG API – for enhanced Metacritic score access when Steam data is insufficient.
  3. Database (Self-hosted and maintained):

    • All data is stored in a MySQL database hosted on PlanetScale, self-hosted and maintained directly by the project author.
    • Database schema is modular and normalized for long-term scalability and analytics performance.
    • View Database Schema
  4. Visualization with Power BI:

    • Power BI connects directly to the PlanetScale database.
    • Built measures and transformations within Power BI to enable real-time filtering, scoring, and ranking of game deals.

📬 Contact the Author

If you’d like to get in touch:

  • 💻 Project or code-related inquiries – Feel free to open an issue on this repo or reach out via email.
  • 💼 Job opportunities or collaborations – I’m actively seeking roles in data analytics or related fields!

Email: xx2448@columbia.edu
LinkedIn: xx-xiaoxiao


✅ Acknowledgments

Special thanks to the following platforms whose APIs made this project possible:

  • IsThereAnyDeal – Historical deal records and real-time discount info.
  • Steamworks Web API – Game details, user reviews, and current pricing.
  • RAWG API – Expanded metadata including platform-specific Metacritic scores.

📄 License

This project is open-sourced under the MIT License.


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An intelligent Steam deal analytics dashboard leveraging Python, MySQL, and Power BI to surface the most worthwhile discounts.

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