📊 E-commerce Customer & Revenue Analysis

🚀 Turning raw transaction data into meaningful business insights using Python and Pandas.

💡 A small percentage of customers contributes a large portion of total revenue (Pareto principle).

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📌 Overview

This project analyzes an e-commerce dataset to understand customer behavior, revenue trends, and business performance. The goal is to extract actionable insights from real-world data.

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📈 Key Analysis

🔹 Revenue Trend

• Analyzed how revenue changes over time
• Identified fluctuations and peak periods

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🔹 Top Countries by Revenue

• Compared revenue contribution across countries
• Identified top-performing regions

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🔹 Customer Insights

• Segmented customers based on total spending
• Identified high-value customers
• Analyzed repeat vs new customers

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🔍 Key Insights

• Top ~20% of customers contribute a large portion of revenue
• Revenue fluctuates over time indicating demand patterns
• Customer behavior varies across countries
• Repeat customers significantly impact revenue

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib

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📂 Project Structure

ecommerce-analysis/
├── data/        # sample dataset
├── notebook/    # analysis notebook
├── images/      # charts
├── README.md
├── requirements.txt

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▶️ How to Run

pip install -r requirements.txt

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📁 Dataset

Note: The full dataset is large, so a sample dataset is included for demonstration.

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