📊 E-commerce Sales Analysis

This project is an exploratory data analysis (EDA) on an E-commerce dataset to derive insights into customer behavior, sales trends, and potential business opportunities. The analysis is performed using Python, with a focus on data cleaning, visualization, and statistical exploration.

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🗂️ Project Structure

📁 E-commerce-Sales-Analysis/ │ ├── e commerce sales.ipynb # Jupyter Notebook with complete analysis ├── Ecommerce Data.csv # Dataset containing e-commerce transaction details └── README.md # Project documentation (this file)

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📌 Objectives

• Clean and preprocess the dataset.
• Analyze customer purchasing patterns.
• Identify peak sales times and top-performing products.
• Understand user demographics and behavior.
• Visualize trends using seaborn, matplotlib, and pandas.

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🧾 Dataset Information

The dataset Ecommerce Data.csv contains the following key columns:

• Email: Customer's email address
• Address: Shipping address
• Avatar: Customer avatar (profile image)
• Avg. Session Length: Time spent on the site during average sessions
• Time on App: Time spent on the mobile app
• Time on Website: Time spent on the website
• Length of Membership: Years of membership
• Yearly Amount Spent: Annual spending amount by the customer

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🧪 Technologies Used

• Python 3.8+
• Pandas – for data manipulation
• Matplotlib & Seaborn – for data visualization
• Jupyter Notebook – for interactive analysis

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📈 Key Insights (Sample)

• Customers who spend more time on the app are likely to spend more money.
• Time on website has a weaker correlation with yearly spending compared to the app.
• Membership length is positively correlated with customer spending.
• Recommendations: Focus development on the mobile app to improve customer retention and spending.

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/e-commerce-sales-analysis.git
   cd e-commerce-sales-analysis
   

Create a virtual environment and install dependencies:

python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate pip install -r requirements.txt

Launch Jupyter Notebook:

jupyter notebook "e commerce sales.ipynb"

📄 Requirements

pandas

matplotlib

seaborn

notebook

Feel free to fork this repo, make changes, and submit pull requests. Contributions are welcome!

📬 Contact

Linkedin: Syed Darain Hyder Kazmi

Instagram: sawab_e_darain

Whatsapp: +923433055357

discord: sawab_e_darain