SuperStore Sales Analysis Project

• Built a retail sales analytics pipeline using Python (pandas, matplotlib, seaborn) to identify high-profit product categories, optimize shipping methods, and forecast future demand. - Generated insights on operational efficiency, improving decision-making in supply chain and inventory management.
• Conducted exploratory data analysis (EDA) to understand sales distribution, segment performance, and seasonal patterns.
• Applied data visualization techniques to clearly illustrate sales trends, spikes, and anomalies before building forecasts.

Dataset used:

• Kaggle: https://www.kaggle.com/datasets/rohitsahoo/sales-forecasting

🔍 Objectives

• Identify high-profit product categories and sub-categories
• Analyze and optimize shipping methods across regions

🛠️ Tools Used

• Python – Data processing & analysis:
• Pandas – Data manipulation
• Matplotlib, Seaborn – Data visualization

📊 Key Insights

• 📦 Technology is the most profitable category; Office Supplies underperform.
• 🚚 Standard Class shipping yields the highest profit across all regions.
• 📦 Predictive planning helps avoid stockouts and overstocking.