Super-Store-Retail-EDA

This repository contains an Exploratory Data Analysis (EDA) of a Super Store retail dataset. The EDA provides insights into various aspects of the Super Store's operations, including sales, profits, customer segments, and regional performance. The analysis aims to uncover patterns, trends, and actionable insights that can help improve business decision-making.

Dataset

The dataset used for this analysis is the Super Store dataset, which includes information about sales, profits, customers, products, and regions. The dataset is available in the file superstore_dataset.csv in the data directory.

Project Structure

The project is organized as follows:

• notebooks/: Contains Jupyter Notebooks for data cleaning, exploratory data analysis, and visualizations.
• data/: Contains the Super Store dataset (superstore_dataset.csv).
• images/: Contains images of visualizations generated during the analysis.
• README.md: Provides an overview of the project and instructions for running the notebooks.

Analysis Steps

The EDA process includes the following steps:

1. Data Cleaning: Perform data cleaning tasks such as handling missing values, removing duplicates, and ensuring data integrity.

2. Data Exploration: Explore various aspects of the dataset, such as sales trends, regional performance, customer segments, and product categories. Use statistical measures, visualizations, and descriptive analysis techniques to gain insights.

3. Data Visualization: Create informative and visually appealing visualizations to present the findings from the EDA. Use charts, graphs, and maps to highlight key patterns and trends in the data.

4. Insights and Recommendations: Summarize the key insights gained from the analysis and provide actionable recommendations for improving business performance. Consider factors such as product assortment, customer targeting, and regional strategies.

Dependencies

To run the Jupyter Notebooks, you'll need the following dependencies:

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn

Install the required packages using the following command: pip install or conda install