This project aims to analyze a supermarket data set using Excel, providing insights into sales, customer satisfaction, and profitability across different product categories. The data set includes information on various metrics such as sales, customer satisfaction levels, and gross income, allowing for detailed analysis of key performance indicators.
supermarket_dataset.xlsx: The data set used for the analysis in XSLX format.
"Supermarket_Analysis.pbix: " Power BI file used for creating interactive visualizations.
"Report.pdf:" Final report containing the analysis results and insights.
Sales analysis by branch and product category
Customer satisfaction analysis by product category
Profitability analysis by product category
Cost of goods sold (COGS) analysis by product category
Income per quantity analysis by product category
The analysis was performed using Python libraries such as Pandas, Matplotlib, and Seaborn. Pivot tables, bar charts, and line plots were used to visualize the results and gain insights into the data.
The analysis provides insights into the performance of different product categories, customer satisfaction levels, and other key metrics. This information can be useful for individuals or businesses interested in similar analyses or using the data set for their own research purposes.
The Excel file contains the methods used for the analysis, including importing and cleaning the data, creating pivot tables, and generating visualizations. To view the interactive visualizations created using Power BI, open the Supermarket_Analysis.pbix file.
This project was completed as a part of a data analysis course. The data set used for the analysis was obtained from an online source.
This project is licensed under the MIT License. See the LICENSE file for more details.