Superstore Sales Analysis Using Power BI and SQL

Project Overview

This project analyzes sales data from a fictional superstore to derive valuable business insights. The analysis combines data cleaning, SQL queries, and Power BI visualizations to provide comprehensive sales performance metrics.

Project Components

• Data Files:

  • superstore_sales_cleaned.csv: Cleaned dataset
  • superstore_final_dataset.csv: Final processed dataset
  • SuperstoreData_cleaned_dump_Dataset.sql: SQL dump file

• Analysis Files:

  • SuperstoreSalesAnalysis_JupyterNotebook.ipynb: Python data analysis notebook
  • Superstore_sales_analysis_SQL_Insights.sql: SQL queries for analysis
  • Superstore Sales Insights.pbix: Power BI dashboard file
  • Superstore Sales Insights.pdf: PDF report of findings

Getting Started

1. Import the SQL dump file into your database
2. Open the Power BI file to view the interactive dashboard
3. Review the Jupyter notebook for data cleaning and analysis steps
4. Execute the SQL queries to perform additional analysis

Project Structure

• Data cleaning and transformation using Python
• SQL database integration and querying
• Interactive Power BI dashboards with three main sections:
  • Sales Performance
  • Customer Analysis
  • Product Analysis

License

This project is licensed under the terms specified in the LICENSE file.