The dataset includes key details on:

• Products 📱💻 – Apple devices (iPhones, MacBooks, iPads, etc.), categories, and pricing.
• Customers 🧑‍💻 – Purchase behavior, demographics, and locations.
• Sales Transactions 💰 – Order date, quantity sold, revenue, and discounts.

✅ Sales Trends – Identifying top-selling products and seasonal trends.
✅ Revenue Analysis – Determining high-revenue products and customer segments.
✅ Customer Insights – Analyzing buying patterns and regional demand.
✅ Data Cleaning & Transformation – Handling missing values, duplicates, and inconsistencies.
✅ Data Visualization 📊 – Using graphs to represent trends and insights.

I used Matplotlib & Seaborn to create:
📈 Sales trend graphs – Line charts showing sales performance over time.
📊 Product comparison charts – Bar plots for revenue and unit sales of different products.
🗺️ Regional sales heatmaps – Showing sales distribution across different locations.

• Python (Pandas, Matplotlib, Seaborn, plotly, NumPy) for analysis & visualization.
• Jupyter Notebook for writing, running, and documenting the project.
• Data Cleaning & Preprocessing to enhance data quality.

• Implement time-series forecasting for future sales predictions.
• Create interactive dashboards with Plotly.