🐍 E-commerce Sales Analysis (Python)

🔎 Overview

This project analyses a large e-commerce transaction dataset to understand revenue trends, product performance, and customer purchasing behaviour.

Using Python, Pandas, and Matplotlib, the analysis explores transaction patterns across products, time periods, and customer segments to uncover key revenue drivers.

The goal of this project is to demonstrate how data analysis can support business insights, marketing decisions, and product strategy.

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🛠 Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook
• Data Cleaning & Transformation

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✨ Features

📊 Revenue Trend Analysis

Explores monthly and seasonal sales patterns to identify revenue fluctuations.

🛍 Product Performance Analysis

Evaluates which product categories contribute most to total revenue.

👥 Customer Behaviour Analysis

Analyses purchasing frequency and spending behaviour across customers.

📈 Data Visualisation

Uses charts and visualisations to highlight sales trends and performance insights.

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⚙️ The Process

1️⃣ Data Preparation

Loaded the dataset and cleaned missing values using Pandas.

2️⃣ Exploratory Data Analysis

Examined data distributions, sales trends, and key variables.

3️⃣ Revenue Analysis

Calculated revenue contributions across products and time periods.

4️⃣ Customer Analysis

Segmented customers based on purchasing behaviour and frequency.

5️⃣ Visualisation

Created charts to illustrate patterns in revenue and customer activity.

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🚀 Impact

• Analysed 500K+ transaction records
• Identified 35% revenue spike during Q4
• Discovered top 20% of products generating 65% of total sales

This analysis demonstrates how large datasets can be used to uncover strategic insights for business decision-making.

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👨‍💻 Author

Puneeth Rao
Business & Information Systems Student
Data Analytics & Business Intelligence