📊 E-Commerce Sales Analytics (SQL + Excel + Tableau)

🔹 Project Overview This project showcases end-to-end data analysis on an E-Commerce sales dataset using MySQL, Excel, and Tableau.

The workflow covers:

1. Data Import → Load dataset into MySQL
2. SQL Queries → Clean & analyze sales patterns
3. Excel & Tableau Dashboards → Visualize insights for business decisions

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🔹 Dataset

• 📂 File: - Data: ecommerce_dataset.csv (200 records)
• 🧾 Columns:
  • order_id, customer_id, product, category, quantity, price, order_date, month, region, revenue, recency

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🔹 Tools & Technologies

• MySQL → querying & analysis
• Excel → pivot tables & charts
• Tableau → interactive dashboard
• GitHub → portfolio showcase

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🔹 SQL Analysis Queries written in Apna College style (simple + readable).

📂 File: - SQL: queries.sql

Example queries:

-- Total Orders
SELECT COUNT(order_id) AS total_orders FROM sales;

-- Unique Customers
SELECT COUNT(DISTINCT customer_id) AS total_customers FROM sales;

-- Top 5 Products by Revenue
SELECT product, SUM(revenue) AS total_revenue
FROM sales
GROUP BY product
ORDER BY total_revenue DESC
LIMIT 5;

-- Monthly Sales Trend
SELECT MONTH(order_date) AS month_no, SUM(revenue) AS monthly_sales
FROM sales
GROUP BY MONTH(order_date)
ORDER BY month_no;

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🔹 Dashboard (Tableau + Excel)

📈 Tableau Dashboard

• Tableau workbook: Tableau_Dashboard.twbx

Highlights:

• Top contributing products & customers
• Revenue by region (map view)
• Monthly sales trend
• KPIs: Total revenue, orders, AOV, customers

📊 Excel Analysis

📂 File: - Analysis (Excel): Excel_Analysis.xlsx
Contains pivot tables & charts for:

• Monthly revenue
• Category-wise contribution
• Customer segmentation
• Monthly sales trend
• Top selling products
• Revenue per customer
• Regional performance

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🔹 Deliverables

• insert_sales.sql → Insert dataset into MySQL
• queries.sql → All analysis queries
• ecommerce_dataset.csv → Original dataset
• Excel_Analysis.xlsx → Excel analysis, charts, pivot
• Project Report (PDF): Ecommerce_Sales_Analytics_Report.pdf
• Tableau dashboard screenshot

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🔹 Key Insights

• Cameras, Printers, and Smartphones are top revenue drivers
• South & North regions generate the most sales
• Sales peak in March, April, and June (seasonality effect)
• Identified high-value customers (spending > ₹1000) for loyalty focus
• Average order value (AOV) is around ₹1,100

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🔹 How to Run

1. Import insert_sales.sql into MySQL
2. Run queries from SQL: queries.sql
3. Open Analysis (Excel): Excel_Analysis.xlsx for Excel insights
4. Open Tableau workbook: Tableau_Dashboard.twbx
5. view Dashboard image: Tableau_Dashboard.twbx

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✨ This project demonstrates the complete analytics pipeline: SQL + Excel + Tableau → Actionable Insights.