This project analyzes customer shopping behavior using transactional data from ~3,900 purchases. The goal is to uncover insights into customer spending patterns, product preferences, and subscription behavior to support data-driven business decisions.
- Understand customer demographics and buying patterns
- Analyze revenue and sales across categories
- Identify high-value customers and segments
- Evaluate the impact of discounts and subscriptions
- Build an interactive dashboard for business insights
├── Dataset/
├── Customer.sql
├── Customer_Behavior_Analysis.ipynb
├── Customer_Behavior_Analysis.pbix
├── Dashboard Preview/
├── Customer_Shopping_Behavior_Analysis.pdf
- Rows: 3,900
- Columns: 18
- Demographics: Age, Gender, Location, Subscription Status
- Purchase Info: Item, Category, Amount, Season, Size, Color
- Behavior: Discount usage, Frequency, Ratings, Shipping Type
- Python (Pandas, NumPy)
- SQL (MySQL/PostgreSQL)
- Power BI
- Data cleaning and preprocessing
- Missing value handling
- Feature engineering
- Revenue analysis
- Customer segmentation
- Product insights
- Total Customers: 3.9K
- Avg Purchase Amount: $59.76
- Avg Rating: 3.75
- Revenue by category
- Sales by category
- Subscription analysis
- Age group insights
- Clothing generates highest revenue
- Young adults contribute most revenue
- Majority are non-subscribers
- Loyal customers dominate
- Implement loyalty programs
- Optimize discount strategy
- Target high-value customers
- Increase subscription adoption
git clone https://github.com/your-username/customer-behavior-analysis.git
pip install pandas numpy matplotlib
jupyter notebook Customer_Behavior_Analysis.ipynb
- Open
.pbixfile in Power BI Desktop
- Add ML models
- Build recommendation system
- Deploy dashboard
Mehtab