Storytelling using data
Welcome to my data visualization project! In this project, I have taken on the challenge of visualizing complex data sets to make it easy for anyone to understand and draw valuable insights.The dataset I have used for this project is the Global Supermarket Sales dataset, which contains information about sales, profits, and customer demographics from different supermarkets around the world.
To create visualizations for this dataset, I have used various platforms such as Tableau and Matplotlib. Tableau is a powerful data visualization tool that allows me to create interactive dashboards and visualizations. I used Tableau to create visualizations that enable the viewer to explore the dataset in a user-friendly way. With Tableau, I was able to create interactive dashboards that allowed me to drill down into specific regions and explore the data from different angles.
I also used Matplotlib, a popular Python library for creating static visualizations. Matplotlib allowed me to create custom charts and graphs that helped me to showcase specific insights in a clear and concise manner. With Matplotlib, I was able to create various charts such as bar charts, scatterplots, and line graphs to display different aspects of the dataset.
In addition to Tableau and Matplotlib, I also used D3.js, a JavaScript library for creating dynamic and interactive data visualizations, to create a custom visualization. With D3, I was able to create a unique and engaging visualization that showcased the supermarket sales data in a visually appealing and interactive way. Lastly, I used Gephi, an open-source network analysis and visualization tool, to analyze the relationships between different supermarkets and their customers. With Gephi, I was able to create network visualizations that allowed me to identify clusters of customers and their buying behaviors.
Overall, by using various platforms such as Tableau, Matplotlib, D3.js, and Gephi, I was able to create a diverse range of visualizations that provide valuable insights into the Global Supermarket Sales dataset.
The Tableau Dashboard uses a dataset downloaded from Kaggle, which provides information on global supermarket sales. The dashboard consists of several visualizations that allow users to gain insights into the sales performance of the supermarkets.
The first set of visualizations includes year-wise bar graphs that allow users to filter data by year and view the profit, sales, and profit ratio per month. This helps in identifying the sales and profit trends over time and allows for the identification of any seasonal patterns or fluctuations.
The second visualization is a doughnut chart that shows sales by region, enabling users to quickly understand the contribution of each region to the overall sales.
The third visualization consists of a horizontal bar graph that has nested sorting. This means that the sales are sorted by market, segment, and region, providing users with a better understanding of which segments are driving sales in each market and region.
The fourth visualization is a map that shows sales by country, with density points highlighting profit. This provides a clear view of the sales distribution across different countries and highlights the regions where profits are high or low.
The last visualization is a dual-axis graph that shows profit as a line chart and sales as an area chart for all three segments. This helps in understanding the correlation between sales and profit and assessing the performance of each segment.
In conclusion, the Tableau Dashboard provides a comprehensive view of global supermarket sales data, allowing users to explore and analyze the data from different perspectives. This enables users to make informed decisions to improve the sales performance of supermarkets.
The same dataset for 'Global Supermarket Sales' which was used for tableau dashboard was used for data visualization using Python and Matplotlib.Here's a detailed explanation of the visualizations:
A bar graph has been used to show the sales by market. This visualization helps to compare the sales performance of each market and identify which market is performing better than others. You can also customize the chart by adding labels, changing the colors, and adjusting the axes to improve the readability of the chart.
A horizontal bar graph has been used to show the sales by subcategory. This visualization provides a quick overview of the sales distribution by subcategory and helps identify the subcategories that are driving sales. You can also customize the chart by adding labels, changing the colors, and adjusting the axes to improve the readability of the chart.
A pie chart has been used to show the sales per region. This visualization provides a clear representation of the sales distribution by region and helps identify the regions that are contributing the most to sales. You can also customize the chart by adding labels, changing the colors, and adjusting the sizes of the pie slices to improve the readability of the chart.
A line chart has been used to show the sales per segment. This visualization helps to track the sales performance of each segment over time. Line charts are useful when you want to show trends or changes in a variable over time. You can customize the chart by changing the colors, adding labels, and adjusting the axes to make it more informative.
verall, these visualizations are helpful in providing insights into the sales performance of the global supermarket and its various segments, markets, subcategories, and regions. By using different types of charts and graphs, you can create a visually appealing and informative data visualization that helps to identify trends and patterns in the data.
