📊 Superstore Sales & Profit Analysis

📌 Project Overview

This project analyzes the sales, profit, and quantity trends of different product categories in a superstore dataset from 2011 to 2014. Using Python, Pandas, Matplotlib, and Seaborn, we visualize key business insights through interactive charts, bar graphs, and pie charts.

🎯 Key Features

• Category-Wise Analysis: Sales, profit, and quantity trends for Furniture, Office Supplies, and Technology.
• Yearly Trends: Comparison of key metrics from 2011 to 2014.
• Top Performers: Identification of the most profitable sub-categories and countries.
• Correlation Insights: Understanding the impact of discount & shipping cost on profit.
• Visualizations:
  • Grouped Bar Charts (Yearly Comparisons)
  • Pie Charts (Category-wise Sales Distribution)
  • Line Graphs (Quarterly & Weekly Trends)
  • Stacked Bar Charts (Top 5 Contributors)

🔧 Installation & Setup

Ensure you have Python 3.7+ installed and install the required libraries:

pip install pandas matplotlib seaborn numpy

📂 Dataset

The dataset used in this project is a Superstore Sales CSV file containing:

• Order Date: The date of purchase.
• Category & Sub-Category: Product classification.
• Sales & Profit: Financial performance.
• Quantity: Number of items sold.
• Discount & Shipping Cost: Impact on revenue.

📊 Visualizations & Insights

1️⃣ Annual Category Performance

• Grouped Bar Charts for Sales, Profit, and Quantity across 2011-2014.
• Key Findings:
  • Office Supplies had the highest sales but lower profit margins.
  • Technology products contributed the highest profits.
  • Furniture sales fluctuated significantly over the years.

2️⃣ Top Performing Sub-Categories

• Stacked Bar Chart of the Top 3 most profitable sub-categories.
• Helps in identifying best-selling product lines for targeted marketing.

3️⃣ Quarterly & Weekly Trends

• Line Graphs for profit trends per quarter & week.
• Showcases peak business periods & seasonal performance.

4️⃣ Correlation Analysis

• Scatter Plots & Regression Analysis for:
  • Profit vs Discount
  • Profit vs Shipping Cost
• Understanding the impact of discounts & logistics on revenue.

5️⃣ Country-Wise Performance

• Horizontal Bar Charts displaying the Top 10 profitable countries per year.
• Allows business expansion strategies into high-revenue regions.

🏆 Business Impact

This analysis provides actionable insights for: ✅ Product Line Optimization – Focus on high-profit categories. ✅ Discount Strategies – Reduce excessive discounting where unnecessary. ✅ Market Expansion – Target the most profitable regions. ✅ Inventory Management – Ensure stock availability in peak seasons.

👨‍💻 Usage

To run the analysis, simply execute:

python analysis.py

📜 License

This project is licensed under the MIT License.