🏪 Exploratory Data Analysis: The Superstore

This project explores and visualizes data from the "Superstore" dataset, focusing on key business insights such as regional sales trends, customer segmentation, and profitability analysis. By leveraging Python's data analysis and visualization libraries, we uncover patterns and relationships in the data to guide decision-making.

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📊 Key Features

🔍 Data Overview

• Dataset: Superstore Dataset.
• Dataset Details:
  • 9,994 entries and 21 columns.
  • Covers customer orders, products, sales, profit, discounts, and shipping.

📈 Visualizations and Insights

• Average Sales Across Regions:
  • A bar chart showing sales distribution across different regions.
• Trends Over Time:
  • Line chart visualizing average sales and profit from 2014 to 2018.
• Sales and Profit Distribution:
  • Histograms, boxplots, and KDE plots for analyzing sales and profit distributions.
• Customer Segmentation Analysis:
  • Scatter plot exploring the relationship between sales, profit, and customer segments.

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🛠️ Libraries and Tools

• Python
• Libraries:
  • numpy, pandas for data manipulation.
  • matplotlib, seaborn for visualizations.

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🚀 How to Use

1. Clone this repository:

   git clone https://github.com/lkaba-pro/exploratory-data-analysis-superstore.git