E-commerce Sales Analysis

📌 Project Overview

This project analyzes an online retail dataset to uncover sales trends, top products, best customers, and other actionable business insights.
It demonstrates key data science skills:

• Data cleaning
• Exploratory data analysis (EDA)
• Visualization
• Business insight generation

🗂 Project Structure

Ecom-sales-analysis/

• data/ # Raw and cleaned datasets
• notebooks/ # Jupyter notebooks for analysis
• reports/ # Generated reports & summaries
  • images/ # Generated EDA visualizations
• README.md # Project documentation

📊 Dataset

• Source: Kaggle – Online Retail Dataset
• Rows: ~540,000
• Columns: 8 (InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country)

🛠 Tools & Libraries

• Python
• Pandas
• Matplotlib / Seaborn
• Jupyter Notebook

💼 Business Questions Answered

• What are the top selling products?
• Which are the top spending countries?
• Which are the sales peaking month and hours?
• who are the top spending customers?

📈 Progress

• Data cleaning and anomaly analysis done.
• EDA visualizations and interpretations for the business questions.