The goal is to model wine quality based on physico-chemical tests.The dataset 4 contains the following attributesAttribute Information:Input variables (based on physicochemical tests):1 - fixed...

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The goal is to model wine quality based on physico-chemical tests. The dataset 4 contains the following attributes Attribute Information: Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9-pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (discrete score between 0 and 8) Your goal is to build 1. A decision tree model (gini index) that uses physicochemical tests to predict wine quality. 2. Perform 10 fold cross validation and present precision, recall, accuracy, and F1 score. 3. Alinear regression model that uses physicochemical tests to predict wine quality. 4. Perform 70-30 holdout and present Mean Squared Error of the developed model. (3+3+3+3) Submit a folder that contains your code and results. a. Code b. Results (question no 1 - decision tree diagram, question 2 performance measures, question 3 linear regression coefficients, question no 4 MSE score)