.RAR

# a linear model that predicts the quality of a bottle of wine

Construct a linear model that predicts the quality of a bottle of wine based on the following features: 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 (score between 0 and 10) You must turn in a screen shot showing the results of linear regression using the following steps: 1. Download the data from http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/ 2. Construct and evaluate a separate model for both red and white wines. Specifically, I want you to report the cross-validation R2 value. To do this you must create a driver program that: a. Loads/rearranges the data into the proper format. I suggest using the following command as an example: ds = dataset('File','winequalityred.csv','delimiter',';'); b. This function returns a dataset object that has a lot of useful functionality. Here are some commands that might be useful. If you want to know what variables are available, type ds.Properties.VarNames c. So if you want to construct a X matrix using two of the variables, you can use the following command: X = [ds.fixedAcidity ds.volatileAcidity]; d. Likewise you can construct a y vector: y = ds.quality e. Construct a X matrix using all of the features (except quality of course), and then construct a linear model using LinearModel.fit model = LinearModel.fit(X,y) f. Just like classification, we need to evaluate this on data that is hasn’t seen yet, so we need cross-validation. To do this you’ll need to use the following commands: cp = cvpartition(length(y),'k',10); cvMSE = crossval('mse',X,y,'predfun', @doregression,'partition',cp) cvR2 = 1 – cvMSE/mean((y – mean(y)).^2) g. But in order to run this code you’ll have to define a function called doregression that looks like the following: function ypredicted = doregression(xtrain, ytrain, xtest) model = LinearModel.fit(xtrain,ytrain); % Create the model ypredicted = model.predict(xtest); % Run prediction on our training data end 3. I want you to turn in a screen shot(s) showing the code that you created and the results for both types of wine.

You'll get a 640.0KB .RAR file.