SQL Practice Problems - Day 10
Focus: Conditional Logic with CASE Statements

1 - Categorize products by weight: Light (<0.3kg), Medium (0.3-0.6kg), Heavy (>0.6kg).
Input Table: Products
Output: SKU_Code, Weight, Weight_Category

2 - Flag sales transactions as High Value (>1000), Medium Value (500-1000), or Low Value (<500).
Input Table: Sales_Transactions
Output: Sale_ID, Gross_Amount, Value_Category

3 - Count products by price range buckets (0-500, 501-1000, 1001-2000, 2000+).
Input Tables: Products, Pricing
Output: Price_Range, Product_Count

4 - Classify customers as Platinum (spent >5000), Gold (2000-5000), or Silver (<2000).
Input Tables: Customers, Sales_Transactions
Output: Customer_ID, Customer_Name, Total_Spent, Customer_Tier

5 - Identify warehouse operations that need attention: Inbound with quantity <10 or Outbound with quantity >50.
Input Table: Warehouse_Ops
Output: Operation_ID, Operation_Type, Quantity, Attention_Needed

6 - Calculate commission rates: 10% for sales >2000, 7% for sales 1000-2000, 5% for sales <1000.
Input Table: Sales_Transactions
Output: Sale_ID, Gross_Amount, Commission_Rate, Commission_Amount

7 - Categorize sales by time of day: Morning (6-12), Afternoon (12-18), Evening (18-24), Night (0-6).
Input Table: Sales_Transactions
Output: Sale_ID, Timestamp, Time_Category

8 - Flag products with inconsistent pricing: Amazon and Flipkart prices differing by more than 15%.
Input Table: Pricing
Output: SKU_Code, Amazon_MRP, Flipkart_MRP, Is_Inconsistent

9 - Calculate bonus amounts for warehouse staff: 100 for Inbound >100 units, 50 for Outbound >50 units.
Input Table: Warehouse_Ops
Output: Operation_ID, Operation_Type, Quantity, Bonus_Amount

10 - Classify sales regions based on customer location patterns.
Input Tables: Customers, Sales_Transactions
Output: Region, Customer_Count, Total_Sales

11 - Create a product performance score: 3 points for sales >100 units, 2 for 50-100, 1 for <50.
Input Tables: Products, Sales_Transactions
Output: SKU_Code, Total_Sold, Performance_Score

12 - Implement tiered discount system: 20% off for >5 items, 15% for 3-5 items, 10% for 1-2 items.
Input Table: Sales_Transactions
Output: Sale_ID, Quantity, Discount_Rate, Discounted_Amount

13 - Categorize inventory turnover: Fast (<30 days), Medium (30-90 days), Slow (>90 days).
Input Tables: Inventory, Sales_Transactions
Output: SKU_Code, Avg_Days_Between_Sales, Turnover_Category

14 - Flag customers with unusual purchase patterns: sudden spending increases >200%.
Input Tables: Customers, Sales_Transactions
Output: Customer_ID, Current_Spend, Previous_Spend, Pct_Increase, Is_Unusual

15 - Create a comprehensive product health score combining sales, inventory, and pricing factors.
Input Tables: Products, Sales_Transactions, Inventory, Pricing
Output: SKU_Code, Sales_Score, Inventory_Score, Price_Score, Overall_Health