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Tabular data has been around for decades and is one of the most common data types used in data analysis and machinelearning. This exposed many data scientists and machinelearning engineers to the power of analyzing and building models on tabular data. This helped form a community of practice around tabular data.
Participants were tasked with developing predictive models, identifying correlations between population size and tax revenue, and assessing the impact of significant tax policy changes, such as eliminating the Professional Tax in 2010. billion pre-2010 to €1.97 billion post-2010. billion pre-2010 to €1.97
Machinelearning is a popular choice here. I tried several other machinelearning classifiers, but SVM turned out to be the best. Furthermore, it involves just dot-products, a fast operation for nowadays machines to carry on. Of course, any machinelearning algorithm requires a proper dataset to train on.
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