Superstore-Data-Analysis

This project presents an end-to-end exploratory data analysis (EDA) and visualization of a retail Superstore sales dataset using Python. The goal is to uncover key business insights such as sales trends, top-performing products, regional sales patterns, and customer contributions.

🎯 Objectives

• Analyse monthly sales trends
• Identify top-performing products and customers
• Explore regional and category-wise sales patterns
• Visualise sales distribution across categories

📁 Dataset

• Source: Kaggle - Superstore Dataset
• Columns: Order Date, Sales, Region, Product Name, Category, Customer Name, etc.

🧰 Tools & Libraries

• Python 3.12
• pandas
• matplotlib
• seaborn
• Jupyter Notebook

📊 Visualizations

• Monthly sales trend (line chart)
• Top 10 products (horizontal bar chart)
• Sales by region (bar chart)
• Category-wise sales distribution (box plot)
• Top 5 customers (bar chart)

🔍 Key Insights from the Superstore Data Analysis

Based on the analysis and visualisations performed, here are some key insights:

• 📈 Monthly Sales Trend
  The monthly sales trend shows a clear seasonality, with peaks typically occurring towards the end of the year (November and December). There appears to be consistent growth over the years, although there are fluctuations month-to-month.

• 🔥 Top 10 Selling Products
  The analysis of top-selling products highlights that high-value items like copiers and binding machines contribute significantly to overall sales.

• 🌍 Sales by Region
  The West and East regions have the highest sales figures, while the South and Central regions have lower sales. This suggests potential areas for targeted marketing or sales efforts in the underperforming regions.

• 📦 Sales Distribution by Category
  The box plot of sales distribution by category indicates that 'Technology' products generally have a higher sales range and potentially more outliers (high-value sales) compared to 'Furniture' and 'Office Supplies'. This aligns with the observation about top-selling products being high-value technology items.

• 👤 Top 5 Customers by Sales
  The top 5 customers represent a significant portion of the total sales, indicating the importance of maintaining strong relationships with these high-value customers.

🚀 How to Run

Clone the repo and open the notebook:

git clone https://github.com/snehithmthomas/superstore-sales-analysis.git
cd superstore-sales-analysis
jupyter notebook Superstore_Sales_Analysis.ipynb

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### 10. 🙋 **Author / Contact Info**
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## 🙋‍♂️ Author

**Snehith M Thomas**  
[LinkedIn](https://www.linkedin.com/in/snehith-m-thomas-231046194/) | [Email](mailto:snehithmoni20@gmail.com) 

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This project is part of my data analysis portfolio using Python.