This project showcases a complete data analysis workflow and an interactive dashboard built using Power BI, based on customer purchase behavior. The process includes data cleaning, exploratory analysis, and visual storytelling to uncover key insights about customer demographics, spending habits, and purchasing patterns
| File Name | Description |
|---|---|
| Customer_Dashboard.pbix | Power BI dashboard showing visual insights |
| raw_data.csv | Original dataset before cleaning |
| cleaned_data.csv | Dataset after data cleaning (used in Power BI) |
• Power BI – For building the dashboard and creating interactive visualizations.
• Jupyter Notebook – For initial data cleaning using Python (Pandas).
• Power Query – For additional data transformation inside Power BI.
• Tooltips – Used in some visuals to provide extra context on hover.
• Git & GitHub – For version control and project sharing.
- Gender distribution of customers
- Age group segmentation
- Purchase trends by season
- Popular product categories
- Preferred shipping methods
- Average spend per customer
- The raw data was initially cleaned using Jupyter Notebook with Python (Pandas), including handling missing values and filtering unnecessary columns.
- The dashboard was designed for clarity, usability, and effective storytelling.