Visualisation of data may give a consolidated view of a large dataset to highlight multiple key features of the data and aid in a decision process.
Visualisation of data may give a consolidated view of a large dataset to highlight multiple key features of the data and aid in a decision process. Often the analysis required is meaningful in terms of the partition of the data according to one or more parameters to answer questions like; What are gender-based differences in the occurrence of anaemia, high blood pressure, diabetes ?, What are the demography based (rural/urban) differences of the same parameters? The data may be further analysed to reflect sample sizes and variance to convey reliability. The analysis may be extended also to other databases like education and business too.
Outcomes: The CAB database was analysed for gender-based and demography based partitions. A shiny GUI was created for an interactive visualization of the data. Work on partitioning and analysis of the data based on a threshold value of a continuous variable (example, Hemoglobin > 13) is in progress.
Other Member: Yesha Bhavsar
Keywords: Data Science: Cloud Computing, Data Analytics and Machine Learning