🧩 This builds on Challenge 01 (Spaghetti → Schema) by focusing on detecting, repairing, and validating data to ensure integrity and reproducibility.

**🧭 Rules – Challenge 02 Errors → Integrity**

This is an educational and prototype competition created to explore the process of building real-world data challenges on Kaggle.  
It is not an official or prize-bearing competition. Participation is open to anyone for learning and collaboration.

This is the second challenge in the *Grocery Retail – Driven by Data* series. Like the others, it is educational and open for experimentation.  
No prizes or formal rankings apply.


**1️⃣ Conduct**

All participants must follow the [Kaggle Code of Conduct](https://www.kaggle.com/community-guidelines) and [Terms of Service](https://www.kaggle.com/terms).  
Be respectful, cite external sources appropriately, and share constructive feedback with others.

**2️⃣ Data Usage**

The dataset provided is for educational and demonstration purposes only.  
You may download, transform, and share derivative work for personal or academic learning, but no commercial use is implied or permitted.

**3️⃣ Collaboration**

Teams and discussion are encouraged.  
Public sharing of general approaches, code snippets, or data auditing and validation ideas is welcome.  
Do not share direct solutions or private datasets that would invalidate learning goals.

**4️⃣ Submissions**

Since this challenge focuses on data validation and reproducibility rather than scoring metrics:  
Submissions may include `.zip` folders, `.ipynb` notebooks, or `.sql` files.  
Evaluation will be manual and qualitative.

**5️⃣ Timeline**

This challenge is currently in prototype mode and will remain open while materials and data are being refined.  
No prizes, deadlines, or rankings apply at this stage.  
Future versions may transition into formal scored competitions as data pipelines mature.

**6️⃣ Acknowledgment**

By joining this project, you acknowledge that participation is voluntary and intended for educational experimentation and public learning.

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*This challenge leads into Challenge 03 – Data → Signal, where validated data becomes predictive insights.*

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