In this project, our dataset included 10,000 observations for each attribute. Utilizing SAS Enterprise Miner for comprehensive data preprocessing and model assessment. The ensemble model performance secured us the 10th rank among 43 teams on the Kaggle leaderboard.
This report on an NIDDK data set is assessed, interpreted, and predictively analyzed using SAS-EM Machine Learning unified experience for generating models and gaining insights. As well as by using a widely used Data Visualization tool, Tableau to perform exploratory data analysis.
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