Superstore_Sales_EDA

This project is an Exploratory Data Analysis (EDA) of a superstore dataset from the US. The goal of this project is to gain insights into the store's sales and customer behavior by analyzing the data and creating visualizations.

Dataset

The dataset used in this project is the Superstore Sales dataset, which contains information on orders, customers, products, and sales for a US-based superstore. The dataset was cleaned and preprocessed before analysis.

Analysis

The analysis of the dataset includes the following:

• Identification of top spending customers
• Identification of top spending states and cities
• Identification of top selling categories, subcategories, and products
• Analysis of revenue over a time period
• Analysis of revenue by shipping mode
• Analysis of revenue by customer segment

The analysis was performed using Python and several libraries, including Pandas, Numpy, Matplotlib, Seaborn, and Datetime.

Directory tree

Superstore_Sales_EDA
│   README.md
│   requirements.txt
│   data_cleaning.py
│   visualizations.py
│
└───data
│   │   sales_data.csv
|   |   data_cleaned.csv
│
└───notebooks
│   │   notebook1.ipynb
│   │   notebook2.ipynb
|
└───images
│   │   top_spending_customers.png
│   │   top_spending_cities.png
│   │   ...

Results

The results of the analysis are presented in the form of visualizations, including bar charts, line charts, pie charts and donut charts. The visualizations provide insights into the store's sales and customer behavior, such as which products and categories are the most popular and which customers spend the most money.

Conclusion

The EDA of the Superstore dataset provides valuable insights into the store's sales and customer behavior. The results can be used to make data-driven decisions to improve sales and customer satisfaction.