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NOTE; YOUR DEPENDENT VARIABLE Y is quality1 (qual.pdf

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This document discusses building classification models to predict wine quality using logistic regression, kNN, random forest, and boosted tree models. It instructs the user to preprocess the data as needed for each model and assess their performance using recall, precision, F1, and ROC AUC metrics to determine the best model based on ROC AUC.

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*******NOTE; YOUR DEPENDENT VARIABLE Y is 'quality'******** 1 (quality > 6) and 0 (quality < 7) Explore the information value and distribution of features using the pair plot. Develop logistic regression, kNN (optimize k), random forest, and boosted tree models to predict wine quality. Make sure to preprocess the data as needed by the respective models. Assess the performance of each model using the following metrics: Recall, Precision, F1, ROC AUC. Which is the best model based on ROC AUC?

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