Superstore Sales Analysis

Overview

This project provides a comprehensive exploratory data analysis (EDA) of a Retail Superstore's sales data. The goal is to identify key business trends, understand regional performance, and evaluate profitability across different segments and categories.

Dataset

The dataset superstore.csv contains historical sales records from a US-based retail store.

• Observations: 9,994
• Features: 21 (including Order Date, Region, Category, Sub-Category, Sales, Profit, and Quantity)

Key Analysis Components

1. Data Cleaning & Exploration: Checking for missing values, duplicates, and understanding the data structure.
2. Financial Performance: Calculation of total Revenue, total Profit, and overall Profit Margins.
3. Time-Series Analysis: Visualizing Yearly Revenue trends to identify growth patterns.
4. Regional Analysis: - Revenue distribution by Region (East, West, Central, South).
   • Profitability and Margin analysis across different regions.
5. Visualizations: Detailed charts using Matplotlib and Seaborn to communicate insights effectively.

Installation

To run this project locally, ensure you have Python installed.

1. Clone this repository:

   git clone https://github.com/RahaibAnas/Superstore-Sales-analysis
   cd superstore-analysis