Data Visualization via Orange

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Clustering is a statistical method for processing data. The purpose of clustering is to find distinct groups, and/or hidden pattern structures within a given dataset; ones that share similar characteristics. Our attempt to cluster data has proven to be highly successful with the use of an app called Orange. Orange is a machine learning and AI platform that can be used for data analysis, visualization, and testing. During our analysis we’ve been able to identify certain patterns, and insights.

Ultimately, what we’ve gathered as a group, is that there is a level of visual accuracy and convenience, when using AI tools like Orange to conduct analysis. Using Orange to create clusters, gives the user an artful and significant visual of what could have been hard to understand or difficult to denote. Orange helped us to minimize errors by only using what was proven in the given dataset, which reduces the notion of probability, to be replaced by fact. Convenience comes from the array of widgets that makes it easy to access specific areas within the dataset. For example, the use of each widget helped us to pull variables, which was easily added to an x and y access necessary to further conduct multiple analysis. With these discoveries, it gives the user a level of confidence when drawing conclusions to help support in decision-making, which positively impacts growth and change.