Overview

For the project, a data visualization dashboard of the apriori algorithm and the pre-processing process was developed. Streamlit was used as the tool for building the web dashboard.

The visualization charts are the results of analyzing the product purchase history of users. For the dashboard, we have used a mock dataset for the data visualizations as the data from the client is confidential.

<aside> <img src="/icons/report_red.svg" alt="/icons/report_red.svg" width="40px" /> Disclaimer: The dashboard we have developed is not going to be used as a public website, but will be integrated in the internal software of shop owners in the future. Thus, the dashboard is in a locally controllable format, not a public website.

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Description: Streamlit page

The Streamlit data visualization includes the following interactive charts and images:

• Interactive Graphs

  • No. of items sold in 2014 & 2015: analyzes and visualizes the number of items sold each month in the years 2014 and 2015

    

  • Cumulative day transaction - 2014 & 2015: visualizes cumulative day transactions for 2014 and 2015

    

  • Monthly quantity purchased from grocery store - Drag over bars and find average: visualizes purchase quantity of each month

    

  • Number of quantity purchases across weekdays: shows the number of quantity purchases across weekdays

    

  • Top customers

    

• Still Images

  • Word cloud derived from the dataset based on the frequency of words

    

  • Scatter plot based on association rules

  

  

  • Nodes and edges image

    

  • Heatmap representing lift value for the corresponding pair of items

    

The visualizations on the dashboard can be created by simply running the code for each dataset. If you want to apply this method to another domain, you may modify your dataset and apply the method.

Significance

The significance of the data visualization dashboard is to enhance the understanding of product purchase history for shop owners. Additionally, this data visualization dashboard serves various pivotal purposes in the project:

1. Insight Generation: Visualizations, like graphs and interactive charts, help in exploring and understanding the product purchase history of users. This enables the project team to extract meaningful insights and patterns, even with a mock dataset substituting confidential client data.