Proposal

Machine learning Project Proposal
Predicting White Wine Quality
bring up: Yan Shi Student ID: 10369778 Date: October 22, 2012

1. Introduction
Vinho Verde, in northern Portugal is known for its dry, refreshing, blue alcohol and lightly spritz wines that are consumed very young, thou wine. The soils there are mostly granite and slate, with the majority of the vineyards grown in granite. Since the grapes will need the great environment to live so that they will be produced the repair wine, the scientist of wine need to abridgment the level of wine with different values.
By extracting the information much efficiently and accurately from the stress results, machine learning techniques back up scientists render closer to the standard and therefore make better decisions of the quality.

2. Approaches
a.



Analysis 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 data): 
1 - fixed 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 objective tests (e.g. PH values) and the output is based on sensory data.
The expert graded the wine quality between (very bad) and 10 (very excellent).
b. Machine Learning Methods:
In this project, I will record the Naïve Bayes Classifier with the Maximum-likelihood 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 training data and testing data. The goal of SVM is to produce a model (based on the training data) which predicts the target values of the test data given only the test data attributes. tally to the two different methods, we can predict the...If you want to get a full essay, order it on our website: Orderessay



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