README.md

📊 Task 6: Sales Trend Analysis Using Aggregations (ELEVATE LABBS INTERNSHIP)

🧠 Objective

Analyze monthly revenue and order volume trends using SQL aggregations on an online sales dataset.

🛠️ Tools Used

• SQLite (via strftime() for date extraction and formatting)
• SQL for data manipulation and aggregation
• Dataset:
• Orders.csv: Contains Order_ID, Order_Date (in DD-MM-YYYY format)
• Details.csv: Contains Order_ID, Sales

📁 Files Included

• task6_sales_trend.sql: SQL script with all steps
• Orders.csv, Details.csv: Sample datasets
• screenshots/: screenshots of query results
• README.md: This documentation

📌 Key Steps & Logic

1. 📅 Date Conversion Converted Order_Date from DD-MM-YYYY to YYYY-MM-DD using string manipulation for compatibility with strftime().

2. 📆 Monthly & Yearly Grouping Used strftime('%Y') and strftime('%m') to extract year and month from formatted dates.

3. 💰 Monthly Revenue Joined Orders and Details tables on Order_ID, then used SUM(Sales) to calculate monthly revenue.

4. 📦 Monthly Order Volume Grouped orders by month and year, using COUNT(DISTINCT Order_ID) to get unique order volume.

5. 📈 Combined Revenue & Volume Merged revenue and volume metrics by month/year to analyze trends and sort results chronologically.

6. ⏳ Time Filtering Applied filters to limit results to specific time periods for focused analysis.

❓ Interview Prep Highlights

• Difference between COUNT(*) vs COUNT(DISTINCT col)
• Handling NULL values in aggregates
• Role of GROUP BY vs ORDER BY
• Extracting top 3 months by revenue using ORDER BY SUM(Sales) DESC LIMIT 3

##✅ Outcome

• Learned how to group data by time dimensions
• Practiced SQL joins, aggregations, and filtering
• Built a reusable script for sales trend analysis