E-Commerce Data Analysis using Pandas and Matplotlib This project analyzes an e-commerce dataset using Python to uncover sales patterns, customer behavior, and business insights. The analysis includes data cleaning, feature engineering, exploratory data analysis(EDA), and visualization.
- Perform data cleaning and preprocessing
- Analyze sales trend over time
- Identify top customers and products
- Understand purchasing patterns
- Generate business insights
Python Pandas Numpy Matplotlib (Visualizations) Jupter Notebook
Transactional dataset containing: 1.InvoiceNo,StockCode,Description 2.Quantity,InvoiceDate,UnitPrice 3.CustomerID,Country
1.Removed deuplicates 2.Handling missing values 3.Coverted date format 4.Removed invalid values(negative quantity & price)
Created TotalPrice = Quantity * UnitPrice Extracted Year and Month
- Strong seasonal sales pattern
- Revenue driven by few customers
- Majority of transactions are low-value
- Geographic concentration of sales
This project demonstrates how pandas can be used for data cleaning, analysis and visualization to derive actionable business insights.