An analytical project examining retail store sales performance to uncover trends, key drivers, and opportunities for growth.
This project focuses on analyzing retail sales data to:
- 📈 Identify top-performing stores, products, and categories
- 🗓️ Uncover seasonal and monthly sales trends
- 👥 Understand customer purchase behavior
- 💡 Deliver actionable insights for better inventory and marketing strategies
- 🥇 Best-Selling Products and their revenue contribution
- 🏬 Top Performing Stores based on total sales
- 📊 Category-wise Revenue distribution
- 🕒 Monthly Sales Trends with moving averages
- 👤 Customer Purchase Frequency analysis
- 🐍 Python (Pandas, Matplotlib, Seaborn)
- 💾 SQL for querying large datasets
- 📊 Tableau / Power BI for dashboarding
- 🔢 EDA & Predictive Modeling for performance insights
- 📊 Data-driven insights enhance sales strategies and inventory management
- 🧭 Seasonal trends reveal predictable demand cycles
- 🤝 Customer data supports retention and cross-selling opportunities
Contributions are welcome! Feel free to fork this repo, analyze your own dataset, and submit a pull request 💪