Data Analysis and Visualisation

Table of Content

• Project
• Problem Statment
• Approach
• Tools
• Data
• Findings
• Conclusion

Project

This project seeks to analyse sales data for Buymore, a fast-growing supermarket in Ghana.

Problem Statement

Buymore, like many other data-driven companies, requires insights from their data to make informed decisions. As the Data Scientist for Buymore, the following tasks need to be addressed:

• Determine the average sales and profit per market.
• Plot a stacked bar chart showing the average sales and profit per market.
• Identify the market that brings more sales on average.
• Extract the days and months from the Order Date and add them to the data frame with the name "Days" and "Months" respectively.
• Show the trend of profit from January to December with a line plot.
• Identify the day that the company makes high sales on average.
• Determine the correlation between sales and profit, and what it means, and create a scatter plot to show the relationship between sales and profit.
• Plot a grouped bar chart showing the total profit for the different product categories for each market.
• Determine the product category that performs well in each market.

Approach

The project was approached with a combination of data manipulation and visualisation techniques.

Tools

The following tools were used for this project:

• NumPy
• Pandas
• Matplotlib

Data

The data used in this project can be found in the Buymore_sales_data.csv file.

Findings

The following findings were made from this project:

• The average sales and profit per market were computed.
• A stacked bar chart was plotted showing the average sales and profit per market.
• The Kumasi market brings in the most sales on average.
• Days and months were extracted from the Order Date and added to the data frame.
• The trend of profit from January to December was shown with a line plot.
• The company makes the highest sales on Thursdays.
• There is a positive correlation between sales and profit.
• A scatter plot was created to show the relationship between sales and profit.
• A grouped bar chart was plotted showing the total profit for the different product categories for each market.
• Furniture and Office Supplies perform well in all the markets.

Conclusion

From this project, it can be concluded that data-driven insights can be valuable to companies, such as Buymore, to make informed decisions.