Abstract : Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests see [Cortez et al. Cerdeira, F. Almeida, T. Matos and J. The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine.
For more details, consult: [Web Link] or the reference [Cortez et al. Due to privacy and logistic issues, only physicochemical inputs and sensory the output variables are available e. These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced e. Outlier detection algorithms could be used to detect the few excellent or poor wines.
Linear Regression, Gradient Descent, and Wine
Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods. For more information, read [Cortez et al. 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 score between 0 and Cortez, A.
Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47 4 Available at: [Web Link]. Please include this citation if you plan to use this database: P. Center for Machine Learning and Intelligent Systems.After spending a lot of time playing around with this dataset the past few weeks, I decided to make a little project out of it and publish the results on rpubs. The UCI webpage for this dataset has a link to an academic study on this dataset.
The I will use the results that were published in that study as a benchmark to compare my results to. More information, including a link to an academic paper on the dataset, can be found here.
Red and white wine each have their own dataset and will be analyzed seperately. A full analysis of the white wine data will be done first. The above output tells us that there are samples and 12 variables. The response variable is quality. The eleven predictor variables are of the numeric class and the response variable, quality, is of the integer class.
There are samples but only 20 are of the 3 class and only 5 are of the 9 class. There are not enough samples of those classes to split the data into useable training and test sets and perform cross-validation. However, the academic paper did not make any changes to the classes by merging classes, for example to deal with the class imbalances.
So for now, I will not make any changes to the classes so that my results can remain comparable to theirs. To visualize the data, plots for each predictor variable will be displayed. The first thing that stands out in the plots is the presence of outliers for most of the predictor variables. However, the residual. The outlier has a residual. The next highest sugar level in the dataset is Additionally, the sugar outlier comes from the same sample as the density outlier, so removing it cleans up the density distribution as well.
But that sample has a quality of 3, the lowest quality in the dataset. The high value for free. Outliers can have other affects that may lead to them being filtered out, like affecting the mean and sd used in standardization. The k-nearerst neighbors algorithm requires the predictors to be transformed such that they all have a common range. I will use standardization to do this.
Wine Classification Using Linear Discriminant Analysis with Python and SciKit-Learn
Fortunately, the dataset has a large number of samples.We lay out the theory and use of linear regression and gradient descent, develop our own custom functions to understand the application and limitations, and introduce sklearn. LinearRegression to handle the heavy lifting for us. Linear regression is a very useful technique for problems in business as well as science. It provides a good way for the analyst to evaluate relationships between data and make predictions using a simple model.
A few examples include:. Really, there are a myriad of applications and regression is a great first pass to understand relationships between data. However, there are four basic assumptions that are made when using linear regression. If any of these assumptions don't hold, then the regression is going to be flawed as are the decisions and predictions made using the model.
This means that the output scales with the input. If this relationship holds and is truly linear, you expect that ratio to remain constant and to hold no matter how much money you put in. I'd be willing to bet you don't have some relationship like this in reality, but that's what you would expect if it were in fact linear.
This is a fancy name that just means that the variance of the data is the same for all of your data. This is important because if your variance varies, you'll get much different fits over certain domains than others meaning you lose predictive power of your model. Independent residuals indicates that the data is not biased. After developing your regression model, use a residual plot tp see if they are more or less randomly distributed around the x-axis.
Lack of independence could be caused by methodological changes in data collection e. The parameters that are used to fit the model can be skewed by large outliers if they are present in the data. This can be quickly checked by viewing a histogram of the residuals. This isn't a show-stopper for a regression analysis as there are ways to remove outliers or make adjustments. See this page for an example of a model which violates each of the above assumptions. Let's begin with an example using a data set from the UCI Machine Learning Repository - which is a very useful archive for getting data and developing models.
We'll use the Wine Quality data set, in particular, the red wine data. This data has 12 attributes, and the task is to predict the quality of Portuguese wine. First, go ahead and download the data, and load it into pandas with the code below. We do this by training the model. A cost function is really just how we penalize the model for making an error.
This difference is then squared and calculated for each prediction-output combination and then summed and divided by 2 to give the mean squared error. This is the ordinary least squares procedure and is generalizable to multiple input variables making it very useful. In Python, we can define it as:. Now that we have our data and our functions, let's get our data in the proper format to build our model.
As a reminder, here are the summary statistics for our data:. It's fairly simple and easy to understand with a single variable, but now we'll expand it to multiple variables and see if we can improve upon our wine predictions. We import the function, then follow a similar process, but most of it is wrapped up in the LinearRegression function. We can pass it pandas DataFrames directly without having to convert them to numpy matrices or anything of that sort, which speeds things up.
Fitting doesn't require our gradientDescent function either, simply calling the fit method with the respective X and y data. It's that easy! So we didn't get a linear model to help make us wealthy on the wine futures market, but I think we learned a lot about using linear regression, gradient descent, and machine learning in general. Every model comes with its own set of assumptions and limitations, so we shouldn't expect to be able to make great predictions every time.
The key is to understand what we did and why, and see if we can apply it to other situations that may be more fitting. Even though this is a rather basic algorithm, it is often times the go to for quick and simple models in business.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
This project has the same structure as the Distribution of craters on Mars project. The two data sets containing physicochemical and sensory characteristics of red and white variants of the Portuguese "Vinho Verde" wine were taken from the UCI Machine Learning Repository. These data sets are the courtesy of Paulo Cortez.
There are samples of red wine and samples of white wine in the data sets. Each wine sample row has the following characteristics columns :. By the means of data management, visualization, analysis, regression modeling, and machine learning, I explore the relationships and correlations between the wine characteristics and its quality score.
Machine Learning with the UCI Wine Quality Dataset
The main focus of this work is to try different predictive algorithms on the data and examine the resutls. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Data The two data sets containing physicochemical and sensory characteristics of red and white variants of the Portuguese "Vinho Verde" wine were taken from the UCI Machine Learning Repository.
Each wine sample row has the following characteristics columns : Fixed acidity Volatile acidity Citric acid Residual sugar Chlorides Free sulfur dioxide Total sulfur dioxide Density pH Sulphates Alcohol Quality score between 0 and 10 Goals and work flow By the means of data management, visualization, analysis, regression modeling, and machine learning, I explore the relationships and correlations between the wine characteristics and its quality score. The work flows through the following sections: Data management and visualization Data analysis Regression modeling Machine learning Recources P.
Cortez, A. Cerdeira, F.In this R tutorialwe will be estimating the quality of wines with regression trees and model trees. Machine learning has been used to discover key differences in the chemical composition of wines from different regions or to identify the chemical factors that lead a wine to taste sweeter.
In most cases, wine experts rate wine that can predict whether the wine is labeled as the bottom or top shelf. The methods used will be regression trees and model trees to create a system capable of mimicking ratings of wine.
This will allow the winemakers to identify the key factors that contribute to better-rated wines. Since we will be using the wine datasets, you will need to download the datasets. As one can see from the below, the visualization in the decision tree is much easier to read. Also, the digits parameter rounds all digits to the 3 places.
This addition will show visualizations with the dissemination of regression tree results, as they are readily understood even without a mathematics background. The lead nodes are predicted values for the examples reaching that node. We must now make predictions on the test data, we use the predict function. This will return the estimated numeric value for the outcome variable.
As one can see that the outcome is 0. This correlation only measures how strong the predictions are related to the true value. This is not a measure of how far off the predictions were from the true values. We could consider how far, on average, its prediction was from the true value. The above shows room improvement. As one can see, MAE shows 0. The M5 algorithm will return a model tree object that can be used to make predictions.
Also, we will use the predict function that will return a vector of predicted numeric values. Decision trees were used for numeric prediction to model the wine data. The model trees, which builds a regression model at each leaf node in a hybrid approach. However, the latest cor did not improve much, it did surpass the performance of the neural network model published.
I'm going to gain some knowledge of wine by conducting the exploratory data analysis of the data set with the physicochemical and quality of the wine. This dataset is public available for research. The details are described in [Cortez et al. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Modeling wine preferences by data mining from physicochemical properties.
In Decision Support Systems, Elsevier, 47 4 ISSN: This data set contains 4, white wines with 11 variables on quantifying the chemical properties of each wine. At least 3 wine experts rated the quality of each wine, providing a rating between 0 very bad and 10 very excellent.Wine Quality Prediction
All the independent variables are numerics and the quality - expert review - is integer. If might be helpful for the further exploration to operate quality as a factor variable, so I createthe new variable and call it quality.
The summary function let us dig into data. Most of the variables, except Residual sugar and alcohol, have means and medians pretty close to each other. At the same time their max values are far from the third quartile except pH. I guess the distributions of this variables might be normal with outliers on the right tail.
Despite the fact that experts were able to grade the quality of the wine between 0 and 10, wine in the dataset has the quality scores from 3 to 9. To get more feeling about the data, I'm going to visualize the histrograms of the varibles in the next section. The following grid visualize the distributions of provided variables.To understand EDA using python, we can take the sample data either directly from any website or from your local disk.
To find what all columns it contains, of what types and if they contain any value in it or not, with the help of info function. Another useful function provided by pandas is describe which provides the count, mean, standard deviation, minimum and maximum values and the quantities of the data.
Above two observations, gives an indication that there are extreme values- deviations in our data set. From above we can conclude, none of the observation score 1 poor2 and 9, 10 best score. All the scores are between 3 to 8. Above processed data provide an information on vote count for each quality score in descending order.
We can check missing values in our white-whiskey csv data set with the help of seaborn library. From above we can see there is no missing values in the dataset.
Incase if there is any, we would have seen figure represented by different colour shade on purple background. Above, positive correlation is represented by dark shades and negative correlation by lighter shades. From above we can see, there is a strong positive correlation of density with residual sugar.
However, a strong negative correlation of density and alcohol.
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