Electronics Sales Analysis – April 2019

A complete exploratory data analysis (EDA) on more than 18,000 retail transactions to understand product performance, customer purchasing behavior, and city-wise sales patterns.

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1. Dataset Overview

The dataset includes the following fields:

• Order ID
• Product
• Quantity Ordered
• Price Each
• Order Date
• Purchase Address

Key Notes

• 59 completely empty rows removed
• 56 duplicate entries removed
• Final dataset size: 18,267 rows × 12 columns after data cleaning and feature engineering

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2. Data Preparation

Cleaning Steps

• Removed rows containing all null values
• Removed duplicate rows

Feature Engineering

• Extracted:
  • Date
  • Month
  • Hour from the Order_Date column
• Split Purchase Address into:
  • House Number
  • City
  • Pin Code
• Converted data types:
  • Quantity_Ordered → integer
  • Price_Each → float
  • Date fields → datetime
• Created a calculated column:
  • Sales = Quantity_Ordered × Price_Each

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3. Analysis and Visualizations

3.1 Monthly Sales Summary

Total revenue:

Month	Revenue ($)
April	3,384,047.56
May	10,559.29

(Almost all data belongs to April.)

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3.2 City-wise Sales Distribution

Highest order volumes:

City	Orders
San Francisco	4434
Los Angeles	3021
New York City	2429
Boston	1915
Atlanta	1470

San Francisco leads in both sales volume and revenue.

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3.3 Peak Purchase Hours

Key observations:

• Highest activity:
  • 10 AM to 1 PM
  • 6 PM to 8 PM
• Very low activity:
  • 2 AM to 5 AM
• Orders begin rising around 7 AM and decline after 9 PM

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3.4 Product-Level Performance

By Number of Orders

Top frequently purchased items:

• USB-C Charging Cable
• AAA Batteries
• AA Batteries
• Wired Headphones

Low-cost, high-frequency items dominate volume.

By Revenue Generated

Top revenue-generating products:

• MacBook Pro Laptop
• iPhone
• Google Phone
• Bose SoundSport Headphones

High-value electronics drive the majority of total revenue.

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4. Key Insights

• Customer activity peaks around late morning and early evening
• San Francisco, LA, and NYC dominate overall sales
• Accessories account for most orders, but high-end electronics produce most revenue
• Address parsing enables detailed geographic insights
• The dataset primarily represents April sales, limiting cross-month comparison

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5. Technologies Used

• Python
• Pandas
• NumPy
• Seaborn
• Matplotlib

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6. How to Run This Project

Clone the Repository

``bash git clone https://github.com/yourusername/ElectronicsSalesAnalysis.git

Install Dependencies

pip install pandas numpy matplotlib seaborn

Launch the Notebook

jupyter notebook Electronics_Sales_Analysis.ipynb

7. Future Enhancements

Add geospatial heatmaps for city/state-level analysis

Build forecasting models for sales prediction

Implement market-basket analysis for product bundling insights

Create an interactive Streamlit or Power BI dashboard

8. Contributing

Contributions are welcome. Feel free to add new analyses, visualizations, or improvements.

9. Support

If this repository helped you, consider giving it a star.