E-commerce Sales Analysis

Project Overview

This project is about analyzing an e-commerce sales dataset using Python. The main goal was to understand sales performance and find useful business insights that can help improve revenue, profit, and customer retention.

I first cleaned the dataset by handling missing values and converting the date columns into the correct format. After that, I created a few additional columns to make the analysis easier.

Then I performed exploratory data analysis to understand different parts of the business, such as sales trends, customer behavior, product performance, and profit.

Objectives

• Analyze overall sales performance
• Find profitable product categories
• Study customer purchasing behavior
• Understand the effect of discounts on profit
• Find useful business insights

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Dataset

• Source: Kaggle
• Records: 5,000
• Features: 14

Analysis Performed

• Data Cleaning
• Feature Engineering
• Monthly Sales Analysis
• Category-wise Sales Analysis
• Region-wise Sales Analysis
• Customer Analysis
• Payment Mode Analysis
• Top Selling Products
• Sales and Profit Analysis
• Discount and Profit Analysis
• Correlation Analysis

Key Insights

• High sales did not always mean high profit.
• Discounts had only a small negative relationship with profit.
• Profit depended on many factors such as product category and pricing.
• Some regions and product categories performed better than others.
• Customer buying patterns were different across regions.

Business Recommendations

• Focus on bringing back inactive customers.
• Improve the performance of low-performing regions and categories.
• Manage inventory based on customer demand.
• Create better marketing strategies for high-performing products.
• Use discounts carefully to maintain profitability.

Future Improvements

• Build an interactive Power BI dashboard.
• Perform customer segmentation using RFM analysis.
• Build a machine learning model for sales prediction.

Skills Learned

• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis
• Data Visualization
• Business Insight Generation
• Python for Data Analysis

Conclusion

This project helped me improve my data analysis skills by working with real sales data. It also showed how data can be used to understand business performance and support better decision-making.