**Excel and Power Query.
The Challenge**
The dataset had over 100+ rows of orders across regions, with issues like:
• Negative unit prices
• Required dates earlier than order dates
• Inconsistent discount percentages
My goal was to clean, classify, and model scenarios to make the data useful for decision-making.
Data Cleaning
• Fixed anomalies in Unit Price and Required Date.
• Standardized discount percentages.
Scenario Modeling
• Built columns for Adjusted Discount, Inflated Unit Cost, Uplifted Quantity.
• Compared baseline vs scenario totals.
Price Band Classification
• Used and to group orders.
• Validated thresholds with documentation for reproducibility.
Key Insights
• Scenario modeling showed how small changes in discount rates can significantly impact profit.
• Quartile-based classification revealed which products consistently fall into premium vs budget bands.
• Cleaned data is now ready for dashboard visualization in Excel or Power BI.
Final Thoughts
This project reinforced the importance of methodical, stepwise analysis. Cleaning data isn’t glamorous, but it’s the foundation of reliable insights.
If you’re learning Excel or Power Query, I highly recommend practicing with real-world messy datasets it’s the best way to build confidence.
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