compend of Available Data: From the white wine information set, I have 11 Input variables (based on physicochemical tests) and 1 output variable (based on centripetal information): 1 - located acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free reciprocal ohm dioxide 7 - total sulfur dioxide 8 - density 9 - pH (Potential of Hydrogen) 10 - Sulphates 11 - alcohol 12 - quality(0~10) Number of Instances: white wine - 4898. The inputs admit accusing tests (e.g. PH values) and the output is based on sensory data. The full graded the wine quality between (very bad) and 10 (very excellent). b. Machine Learning Methods: In this project, I will record the Naï ve Bayes Classifier with the Maximum-likelih! ood Estimate to estimate the data. And also I will use the SVMs (Support Vector Machines) to run the data which involves the separating data into discipline data and testing data. The goal of SVM is to produce a position (based on the training data) which predicts the target values of the test data given only the test data attributes. tally to the cardinal different methods, we can predict the...If you want to get a full essay, order it on our website: BestEssayCheap.com
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