Interactive Data Clustering and Visualization System for COVID-19 Analysis
The "Clustermaps" project supports the NHS's data analytics department in analyzing COVID-19 data and identifying incidence clusters. Through an interactive report with visually appealing geographic distribution visuals, the k-means clustering algorithm allows users to observe different patterns by adjusting the number of clusters. The user-friendly graphical interface facilitates data loading, cleaning, and preparation, along with customization options for visualizations. The system aims to provide insights for efficient resource allocation within the NHS. Python programming language with libraries such as Pandas and Scikit-Learn is employed for implementation.
The "Clustermaps" project aims to support the NHS's data analytics department in analyzing COVID-19 data and identifying clusters of high and low incidence. The system will provide an interactive and visually appealing report that showcases the geographic distribution of cases. By utilizing the k-means clustering algorithm, users can adjust the number of clusters to observe different patterns and adapt the report accordingly.
Clustermaps will offer a user-friendly graphical interface for loading, cleaning, and preparing the data. It will enable users to save the prepared data, generate visualizations, and manipulate the range of values for customized output. The system's primary goal is to help the NHS management gain insights into the distribution of COVID-19 cases, allowing them to allocate resources more efficiently. The NHS welcomes any additional features that contribute to the project's success and enhance the overall usability of the system.
Programming Language: Python
Libraries used: Pandas, Scikit-Learn