The total sales were analyzed year by year. A bar graph was used to visualize the annual sales. The sales trend shows how the business performed each year from 2015 to 2018.
Monthly sales for each year were analyzed and compared to identify seasonal patterns or trends. A bar graph was plotted for monthly sales in each year (2015 to 2018) to observe how sales varied throughout the year.
The sales for each category (Furniture, Office Supplies, and Technology) were calculated and compared annually. Technology emerged as the best-selling category over the years, with a steady increase in sales.
The top-selling products were identified and listed based on total sales. The highest-selling product was highlighted, with a horizontal bar graph displaying the sales of the top 10 items.
The sales trends for the top products were further broken down by year to see how each product performed annually. This analysis helps identify if any of the top products had consistent sales or if there were significant fluctuations year to year.
The analysis of yearly sales showed a general upward trend, indicating growth in the business.
Technology was consistently the best-performing category, followed by Office Supplies and Furniture.
A few products dominated the sales, with significant contributions from the top 10 items. Understanding these trends can help in inventory management and marketing focus.
These insights can guide future business strategies by highlighting the areas of strength and potential opportunities for improvement.
To run the analysis, you'll need to download the following files:
- Python Script (
analysis.py): Contains the code for performing the sales analysis. - Dataset (
sales_data.csv): Holds the sales data from 2015 to 2018. - Image (
sales_trend.png): Displays the sales trend over the analyzed period.
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Download the Files
Make sure you have the following files:
analysis.pysales_data.csvsales_trend.png
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Set Up the Environment
- Ensure Python is installed on your system.
- Install any necessary libraries. Check the
requirements.txtfile (if available) for a list of dependencies.
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Place the Files
- Make sure all files are in the same directory or adjust the file paths in the script accordingly.
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Execute the Script
Open your terminal or command prompt and navigate to the directory containing the files. Run the following command:
python analysis.py