This project analyzes a retail sales dataset using Python and Power BI to uncover meaningful business insights related to sales trends, customer behavior, and product performance.
The goal of this project is to simulate real-world business analysis by combining data processing, exploratory analysis, and interactive dashboard visualization.
- Clean and preprocess raw retail transaction data
- Analyze sales performance across regions and categories
- Identify high-value customers and top-performing products
- Understand monthly and seasonal sales trends
- Build an interactive dashboard for business insights
- Python
- Pandas
- Jupyter Notebook
- Power BI
- Data Analysis & Visualization
The dataset contains retail transaction records with fields such as:
- Order ID
- Order Date
- Customer Name
- Segment
- State
- Category
- Product Name
- Sales
Total records analyzed: ~9,800 transactions
- Handled missing values
- Converted date columns to datetime format
- Created derived columns:
- Year
- Month
- Month Name
- Verified data types and consistency
- Sales by Region and State
- Product performance analysis
- Customer revenue analysis
- Monthly sales trend analysis
- Average Order Value (AOV) calculation
The project includes a multi-page interactive dashboard:
- Total Sales, Orders, and AOV
- Sales by Category
- Monthly sales trends
- Growth patterns over time
- Top 10 customers
- Sales distribution by customer segment
- Sales by sub-category
- Top-performing products
- West region generated the highest sales
- Technology category is the primary revenue driver
- A small group of customers contributes a significant portion of total revenue
- Sales show strong seasonal trends with peak periods
This project demonstrates the ability to:
- Work with real-world datasets
- Perform data cleaning and analysis
- Extract business insights
- Build professional dashboards using Power BI
Glory Vijitha
Aspiring Data Analyst | Python | Data Analytics | AI Projects