Sales Analysis Project

Goal

To use data analysis, especially time series, to help a business succeed by finding insights and predicting sales.

Data

The data is SuperStore sales data from a company based in United States. It has order_date, product, category, quantity, price, profit, and Region etc. It covers two years, from 2019 to 2020.

Steps

• Clean and prepare the data: Check and fix missing values, outliers, duplicates, and data types.
• Explore the data: Use statistics, graphs, and tests to understand the data and its patterns.
• Analyze the time series: Use techniques like decomposition, stationarity, autocorrelation, and forecasting models to study the sales over time.
• Create a dashboard: Use a tool like Power BI to make an interactive dashboard to show the data and the analysis results.

Results

The results are:

• Insights on the sales performance, seasonality, and growth of the company by product and category.
• Accurate sales forecasts for the next 15 days.
• An interactive dashboard that lets the user explore the data and the analysis results easily.

Data Sources

The project uses sales data from the following sources:

• https://github.com/rayon25/SuperStore-Sales/blob/main/Data/SuperStore_Sales_Dataset.csv

Please refer to the documentation for more details on the data schema and structure.

Website Link

You can access the interactive dashboard on our website: https://app.powerbi.com/view?r=eyJrIjoiNWQwNmVjOWUtYjBlNi00Mzk3LTllYzAtZTlkYWIzMmM0NDUwIiwidCI6IjVlMjVjZTVhLWJlMzItNGUxYy05MjczLWYyY2MzODYzYjkzYSJ9

Conclusion

The conclusion is that data analysis, especially time series, can help a business to do better by giving insights and predicting sales. The project also shows the skills and knowledge of the data analyst in using these techniques and making a dashboard.