This project dives deep into a comprehensive sales dataset to uncover patterns, trends, and insights across products, locations, time periods, and customer behavior.
- 📆 Sales Peak in December: Highest sales volumes observed during the holiday season, indicating strong year-end consumer activity.
- 🛒 Most Profitable Products: Laptops, Phones, and Monitors consistently led in revenue contribution.
- 🌍 Top Performing Cities: New York, Los Angeles, and San Francisco recorded the highest sales.
- ⏰ Busiest Purchase Hours: Significant spike in purchases between 11 AM and 1 PM—suggests timing for marketing strategies.
- 📦 Frequently Bought Together: Product bundling patterns reveal common co-purchases like Phone + Charger and Laptop + Mouse.
- 💳 Preferred Payment Type: Credit cards are the dominant mode of payment, followed by debit cards.
- 📈 Correlation Between Quantity and Sales: Some items show an inverse relation—bulk buys often involve discounts or low-price goods.
- Python (Pandas, NumPy, Seaborn, Matplotlib)
- Jupyter Notebook
- Data Cleaning & Feature Engineering
- Visualization and Insights Interpretation