E-Commerce Customer Analysis

This project focuses on analyzing customer behavior data from an e-commerce platform using Python. The objective is to understand how different user activity metrics influence customer spending and to extract meaningful insights through data analysis.

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📌 Project Description

The analysis is performed using a real-world e-commerce customer dataset. It explores relationships between session activity, app usage, website usage, membership duration, and yearly spending. The project is intended for learning data analysis, visualization, and data-driven decision making.

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📂 Repository Contents

• Day6.ipynb – Jupyter Notebook containing the complete analysis
• Dataset.txt – Dataset used for analysis

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🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

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📊 Dataset Information

• Total Records: 500
• Total Features: 8

Features:

• Email
• Address
• Avatar
• Avg. Session Length
• Time on App
• Time on Website
• Length of Membership
• Yearly Amount Spent

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🔍 Work Done in This Project

• Loaded and explored the dataset
• Checked data shape and structure
• Analyzed feature relationships
• Prepared data for visualization and modeling

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🎯 Learning Outcomes

• Hands-on experience with Pandas and NumPy
• Understanding of exploratory data analysis (EDA)
• Practical exposure to e-commerce customer data

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📌 Connect with me

🔗 LinkedIn: Chandan Vyas

🚀 How to Run the Project

pip install pandas numpy matplotlib seaborn
jupyter notebook Day6.ipynb