Amazon Sales Analysis - SQL, Power BI, Python

Project Overview

This project analyzes Amazon sales data using SQL for data extraction & transformation, Power BI for interactive data visualization, and Python for data analysis & forecasting. The goal is to uncover key business insights, including revenue trends, customer behavior, product performance, discount impact, and future sales predictions.

Key Insights & Analysis

SQL Data Analysis

• Total Sales & Profit Calculation
• Monthly & Yearly Sales Trends
• Top-Selling Products & Categories
• Customer Segmentation (High-Spenders, Frequent Buyers)
• Discount Impact on Profitability
• Sales Forecasting using Moving Averages

Power BI Dashboard

• Total Sales: $517K | Total Profit: $102K
• Best-Selling Categories & Products
• Sales by Region & Shipping Cost Analysis
• Yearly & Monthly Sales Trends Visualization
• Customer Review Analysis & Payment Method Preferences

Python Data Analysis & Forecasting

• Data Cleaning & Preprocessing (Pandas, NumPy)
• Sales Trend Analysis using Matplotlib & Seaborn
• Predictive Modeling (Time Series Forecasting using Scikit-learn)
• Correlation Analysis for Discount & Profitability

SQL Techniques Used

• Aggregation Functions: SUM(), AVG(), COUNT()
• Filtering: WHERE, HAVING
• Date Functions: YEAR(), DATE_FORMAT()
• Window Functions: RANK(), LAG(), AVG() OVER()
• Subqueries & CTEs: WITH ... AS for improving query readability

Power BI Features Used

• Dynamic Dashboards with filters & slicers
• Bar, Line & Pie Charts for trend visualization
• Geographical Maps for regional sales insights
• KPI Cards to highlight total sales, profit, and units sold

Python Libraries Used

• Pandas & NumPy for data manipulation
• Matplotlib & Seaborn for data visualization
• Scikit-learn for forecasting & predictive analytics

Business Recommendations

• Increase stock of best-selling products based on monthly sales trends.
• Target marketing campaigns on peak sales days identified in sales trends.
• Monitor discounting strategies to avoid profit loss.
• Optimize shipping for slow-delivery products to enhance customer satisfaction.

Conclusion

This project provides valuable insights into sales performance, customer behavior, and profitability. By leveraging SQL, Power BI, and Python, businesses can make data-driven decisions to improve revenue growth and operational efficiency.

Project Files & Resources

• SQL Queries: SQL script
• Power BI Dashboard: Power BI
• Python Analysis Notebook: Python

How to Use This Project

1. Clone this repository:

   https://github.com/Divya-M17/Amazon-Sales-Analysis-SQL-Python-Power-BI

2. Open and run the SQL queries on your database.
3. Open Power BI to explore the interactive dashboard.
4. Run the Python Notebook for forecasting insights.