This project analyzes customer shopping behavior to uncover insights that help businesses improve marketing strategies and product recommendations. It demonstrates end‑to‑end data analytics skills — from data cleaning and SQL querying to visualization and reporting.
The dataset contains customer transaction details, product categories, purchase amounts, and demographics.
- Python – Data loading, cleaning, and exploratory data analysis (EDA)
- SQL (MySQL) – Querying and aggregating customer data
- Power BI – Dashboard creation and visualization
- Jupyter Notebook – Code execution and documentation
- GitHub – Version control and project hosting
- Loaded dataset using Python (Pandas)
- Cleaned and transformed data for analysis
- Performed EDA to identify trends and patterns
- Executed SQL queries for deeper insights
- Built interactive Power BI dashboards
- Created a final report and presentation summarizing findings
- Customer segmentation by purchase frequency and amount
- Product category performance
- Monthly revenue trends
- Insights on customer retention and churn
- Identified top‑spending customer groups
- Highlighted seasonal buying patterns
- Provided actionable insights for marketing optimization