Superstore Sales Data Analysis

Please find the project overview below

This project is a beginner-friendly data analysis of the Superstore dataset from Kaggle.
The goal is to analyze sales trends, top-performing products, customer segments, and regional contributions to provide actionable business insights.

Dataset

• Source: Kaggle Superstore dataset
• Columns included:
  • Order ID, Order Date, Ship Date, Ship Mode
  • Customer ID, Customer Name, Segment, Country, City, State, Region
  • Product ID, Category, Sub-Category, Product Name, Sales

Please note: The dataset used in this project is included as train.csv.

Tools & libraries

• Python 3
• Jupyter Notebook
• Pandas (data manipulation)
• Matplotlib & Seaborn (visualization)

Analysis Performed

1. Data Cleaning

   • Removed duplicate rows
   • Ensured numeric columns are correct
   • Checked for missing values

2. Sales Analysis

   • Total sales by Region
   • Total sales by Category and Sub-Category
   • Monthly sales trends over time

3. Top Performers

   • Top 10 products by sales
   • Top 10 customers by sales

4. Segment & Shipping Analysis

   • Sales contribution by customer segment
   • Sales contribution by shipping mode

5. Optional Visualizations

   • Regional heatmap of sales by category
   • City-wise and state-wise top sales

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Key Insights

• Region with highest sales: West region
• Most profitable category (highest sales): Technology
• Top-selling product: Canon imageCLASS 2200 advanced copier
• Top customer: Sean Miller
• Segment contributing most sales: Consumer
• Sales trend: Monthly sales show seasonal fluctuations with peaks during key periods.

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How to Run

1. Clone this repository:

git clone https://github.com/shreyagh1/SuperstoreSales_DataAnalysis.git