Applying data mining for wine industry

CIS 435
Final Project
Francisco E. Figueroa
Abstract
Wine making has been around for thousands of years. It is not only an art but also a science.
Wine making is a natural process that requires little human intervention, but each winemaker guides
the process through different techniques. In general, there are five basic components of the
winemaking process: harvesting, crushing and pressing, fermentation, clarification, and aging and
bottling. Wine makers typically follow these five steps but add variations and deviations along the
way to make their wine unique. (Myers, 2014) The stages include harvesting, crushing and pressing,
fermentation, clarification, and aging and bottling. For the winemaker is important to understand
what chemical properties (attributes and values) affect or impact the classification on a wine. This
help the wine maker to monitor attributes such as alcohol,malic acid, ash, ash alkalinity,
magnesium, total phenols, flavonoids, non flavonoid phenols, proanthocyanidins, color intensity,
hue, OD280/OD315 concentration, and proline. This paper presents a study for the prediction of
the wine chemical parameters (alcohol,malic acid, ash, ash alkalinity, magnesium, total phenols,
flavonoids, non flavanoid phenols, proanthocyanidins, color intensity, hue, OD280/OD315
concentration, proline) to classify the wine into A, B or C. The project used Decision Trees and
Artificial Neural Networks to create models for the classification objectives of the Data Mining
concepts application. The experiments were conducted using the new the open-source WEKA
tool. Accuracies between 88% and 93% were obtained for all datasets provided in the case of
Decision Tree J48. This indicate that the use of decision tree of Data Mining models can be used
to predict the classification of the wine based on chemical attributes. Accuracies between 90%
and 97% were obtained for all datasets provided in the case of ANN - Multi-Layer Perceptron.
Introduction
Generally, data mining (sometimes called data or knowledge discovery) is the process of
analyzing data from different perspectives and summarizing it into useful information -
information that can be used to increase revenue, cuts costs, or both. Technically, data mining is
the process of finding correlations or patterns among dozens of fields in large relational
databases. Data mining is primarily used today by companies with a strong consumer focus -
retail, financial, communication, and marketing organizations. It enables these companies to
determine relationships among internal factors such as price, product positioning, or staff skills,
and "external" factors such as economic indicators, competition, and customer demographics.
There is a lot of evidence that data-driven decision making and big data technologies improve
business performance. The fundamental concepts include data mining tasks and the data mining
process that involve supervise or unsupervised data mining or a hybrid between the two methods
to find patterns from data into a set of well-defined sub-tasks.
For this project the intention is to apply classification data mining models to understand
the chemical properties of the wine to predict it to class A, B, or C. The two algorithms to be
used are Decision Tree and Artificial Neural Networks.

Decision tree builds classification or regression models in the form of a tree structure. It
breaks down a dataset into smaller and smaller subsets while at the same time an associated
decision tree is incrementally developed. The final result is a tree with decision nodes and leaf
nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and
Rainy). Leaf node (e.g., Play) represents a classification or decision. The topmost decision node
in a tree which corresponds to the best predictor called root node. Decision trees can handle both
categorical and numerical data.
Neural networks have proven themselves to be a valuable tool across a wide range of
functional areas affecting most businesses. Neural networks are simple computational tools for
examining data and developing models that help to identify interesting patterns or structures in
the data of your business. Neural networks can be used to learn to predict future events based on
the patterns that have been observed in the historical training data; learn to classify unseen data
into pre-defined groups based on characteristics observed in the training data.
The paper is organized as follows: data mining applications, data mining themes
associated to the project, an explanation of the Cross Industry Standard Process for Data Mining,
the understanding of the data provided, the preparation process of the data to make sure that data
is normalized or provided based on certain standards, data mining algorithms used and the
chosen one based on performance, an explanation of the experimental process and results, and
finally, closing conclusions are drawn.
Data Mining Applications
The vinification process is one important stages of the wines production that can
influence the achievement of wines quality. Based in a chemical samples this assessment is
traditionally realized by wine tasters that analyze some subjective parameters such as colour,
foam, flavour and savour. (Ribeiro). In the research titled “Wine Vinification prediction using
Data Mining Tools” to predict the organoleptic parameters (colour, foam, flavour and savour)
from the chemical parameters in the vinification process, Decision Trees, Artificial Neural
Networks and Linear Regression were applied as data mining techniques to create model for the
classification and regression objectives of the process.
In another case, the main purpose of the study was to predict wine quality based on
physicochemical data. This was also conducted to identify outlier or anomaly in sample wine set
in order to detect adulteration of wine. Two classification algorithms were applied, Decision tree
and Naïve Bayes on the dataset. The result was that Decision tree (ID3) outperformed Naïve
Bayesian techniques particularly in red wine, which is the most common type. The study also
showed that two attributes, alcohol and volatile-acidity contribute highly to wine quality. White
wine is also more sensitive to changes in physicochemistry as opposed to red wine, hence higher
level of handling care is necessary. The research concluded that classification approach will give
rooms for corrective measure to be taken in effort to increase the quality of wine during
production. (Appalasamy, 2012).
Based on the data sets and associated attributes and objects provided, we can define that
we want to classify the wine on Class A, B or C according to the following attributes: alcohol,
malic_acid, ash, ash_alalinity, magnesium, total_phenols, flavonoids, nonflavanoid_phenols,
proanthocyanidins, color intensity, hue, OD280_OD315 and Proline.

Data Mining Themes
There is a lot of evidence that data-driven decision making and big data technologies
improve business performance. The fundamental concepts include data mining tasks and the
data mining process that involve supervise or unsupervised data mining or a hybrid between the
two methods to find patterns from data into a set of well-defined sub-tasks. There are a large
number of specific data mining algorithms developed over the years, but there are only a handful
of different tasks that these algorithms address.
When finding correlations between a particular variable describing an individual and
other variable when viewing classification and regression tasks we can relate the following tasks:
a) classification and class probability which attempt to predict, for each individual in a
population, which of a small set of classes this individual belongs; b) scoring which represent the
probability that the individual belongs to each class; c) regression which attempts to estimate or
predict, for each individual, the numerical value of some variable for that individual; d)
similarity matching attempts to identify similar individual based on data known about them and
support the basis for recommendation systems; e) clustering attempts to group individuals in a
population together by their similarity, but not driven by any specific purpose; f) co-occurrence
grouping attempts to find associations between entities based on transactions involving them; g)
profiling attempts to characterize the typical behaviour of an individual, group or population and
mostly use for anomaly detection applications; h) link prediction attempts to predict connection
between data items, very common in social networks; i) data reduction to attempt to take a large
set of data and replace it with a smaller set of data that contains much of important information
in the larger set and the smaller data set it’s easier to manage; and j) casual modeling that
attempts to help to understand what events or actions influence others. (Provost, 2013)
Machine learning algorithms uses supervised, unsupervised or semi-supervised learning.
Supervised learning is where you have input variables (x) and an output variable (Y) and you use
an algorithm to learn the mapping function from the input to the output. Y = f(X). The goal is to
approximate the mapping function so well that when you have new input data (x) that you can
predict the output variables (Y) for that data. In the supervised tasks, we know the correct
answers, the algorithm iteratively makes predictions on the training data and is corrected by the
supervisor. Learning stops when the algorithm achieves an acceptable level of performance.
Supervised learning problems can be further grouped into regression and classification problems.
Some examples of supervised algorithms are: linear regression for regression problems, random
forest for classification and regression problems, and support vector machines for classification
problems. (Brownlee, 2016)
Unsupervised learning is where you only have input data (X) and no corresponding
output variables. The goal for unsupervised learning is to model the underlying structure or
distribution in the data in order to learn more about the data. Algorithms are left to their own
devises to discover and present the interesting structure in the data. Unsupervised learning
problems can be further grouped into clustering and association problems. Some popular
examples of unsupervised learning algorithms are k-means for clustering problems and Apriori
algorithm for association rule learning problems. (Brownlee, 2016)

Problems where you have a large amount of input data (X) and only some of the data is
labeled (Y) are called semi-supervised learning problems. These problems sit in between both
supervised and unsupervised learning. Many real world machine learning problems fall into this
area. This is because it can be expensive or time-consuming to label data as it may require access
to domain experts. Whereas unlabeled data is cheap and easy to collect and store. We can use
unsupervised learning techniques to discover and learn the structure in the input variables. We
can also use supervised learning techniques to make best guess predictions for the unlabeled
data, feed that data back into the supervised learning algorithm as training data and use the
model to make predictions on new unseen data. (Brownlee, 2016)
CRISP-DM
Data mining is the application of science and technology. There is a useful codification
of the data mining process that is given by the Cross Industry Standard Process for Data Mining.
To use and apply data mining correctly is is important to use a standard process such as The
Cross Industry Standard Process for Data Mining (CRISP-DM). This process provides a
framework to structure the thinking about data analytics and problems. CRISP-DM provides a
useful codification of the data mining process. The current process model for data mining
provides an overview of the life cycle of a data mining project. It contains the phases of a
project, their respective tasks, and the relationships between these tasks. At this description level,
it is not possible to identify all relationships. Relationships could exist between any data mining
tasks depending on the goals, the background, and the interest of the user–and most
importantly–on the data.
The CRISP-DM process includes business understanding, data understanding, data
preparation, modeling, evaluation, and deployment. Business Understanding - This initial phase
focuses on understanding the project objectives and requirements from a business perspective,
then converting this knowledge into a data mining problem definition and a preliminary plan
designed to achieve the objectives. Data understanding - the data understanding phase starts
with initial data collection and proceeds with activities that enable you to become familiar with
the data, identify data quality problems, discover first insights into the data, and/or detect
interesting subsets to form hypotheses regarding hidden information. Data preparation - the
data preparation phase covers all activities needed to construct the final dataset from the initial
raw data. Data preparation tasks are likely to be performed multiple times and not in any
prescribed order. Tasks include table, record, and attribute selection, as well as transformation
and cleaning of data for modeling tools. Modeling - various modeling techniques are selected
and applied, and their parameters are calibrated to optimal values. Some techniques have specific
requirements on the form of data. Evaluation - at this stage in the project, we have built a model
(or models) that appears to have high quality from a data analysis perspective. Before proceeding
to final deployment of the model, it is important to thoroughly evaluate it and review the steps
executed to create it, to be certain the model properly achieves the business objectives. A key
objective is to determine if there is some important business issue that has not been sufficiently
considered. At the end of this phase, a decision on the use of the data mining results should be
reached. Deployment - creation of the model is generally not the end of the project. Even if the
purpose of the model is to increase knowledge of the data, the knowledge gained will need to be
organized and presented in a way that the customer can use it. Depending on the requirements,
the deployment phase can be as simple as generating a report or as complex as implementing a

repeatable data mining process across the enterprise. In many cases, it is the customer, not the
data analyst, who carries out the deployment steps. However, even if the analyst will carry out
the deployment effort, it is important for the customer to understand up front what actions need
to be carried out in order to actually make use of the created models. (Chapman, 2000)
Data Understanding
For this project we obtained the following files: WineAllData.arff, WinCorre.arff, and
WineNoCorre.arff. For each file the number of objects are 178. For WineAllData.arff, the total
amount of objects is 178, with 14 attributes which thirteen (13) of them are numerical values and
one (1) is nominal. In the case of WinCorre.arff, were 178 objects with six (6) attributes, which
five (5) of them are numerical values and one (1) nominal value. Only correlated attributes
(Alcohol,Total_Phenols , Flavanoids,OD280_OD315, Proline, Type). WineNoCorre.arff, were
178 objects with eight (8) attributes, which seven (7) numerical values and one (1) nominal.
This data set is without correlated attributes. The only nominal attribute has the following labels:
A, B, C to classify or predict the wines based on the numerical values patterns. This nominal
value will be defined as the class. All the attributes are continuous. No missing attributes. The
nominal attribute objects are distributed as follows: Label A, 59 has object's , Label B has 71
object's, Label C has 48 object’s.
Data Source Name: WinAllData.arff
Attribute Name Data Type
Alcohol Numerical
Malic_Acid Numerical
Ash Numerical
Ash_Alcalinity Numerical
Magnesium Numerical
Total_Phenols Numerical
Flavanoids Numerical
Nonflavanoid_Phenols Numerical
Proanthocyanins Numerical
Color Intensity Numerical
Hue Numerical
OD280_OD315 Numerical
Proline Numerical

Type Nominal
DataSource: WineCorre.arff
Alcohol Numerical
Total_Phenols Numerical
Flavanoids Numerical
OD280_OD315 Numerical
Proline Numerical
Type Nominal
DataSource: WineNoCorre.arff
Malic_Acid Numerical
Ash Numerical
Ash_Alcalinity Numerical
Magnesium Numerical
Nonflavanoid_Phenols Numerical
Color Intensity Numerical
Hue Numerical
Type Nominal

Data Preparation
Data preprocessing and data preparation is a broad area and consists of a number of
different strategies and techniques that are interrelated in complex ways. For data pre-processing
there are several techniques that can be used such as aggregation, sampling, dimensionality
reduction, feature subset selection, feature creation, discretization and binarization, and variable
transformation. Aggregation is the combination of two or more objects into a single object.
The motivations for aggregation are that smaller data sets requires less memory and processing
time, provide high-level view of the data, and the behaviour of groups of objects or attributes is
often more stable. Sampling is a commonly used approach for selecting a subset of the data
objects to be analyzed. The motivations for sampling is to use a sample that will work almost as
well as using the entire data set if the sample is representative. When applying a sampling
technique it is important to guarantees high probability of getting a representative sample.
Dimensionality reduction works when having a data sets that have large number of features.
There are a variety of benefits to dimensionality reduction. A key benefit is that many data
mining algorithm work better if the dimensionality (the number of attributes in the data) is lower.
This is because dimensionality reduction can eliminate irrelevant features and reduce noise and
the curse of dimensionality. And the amount of time and memory required by the data mining
algorithm is reduced with a reduction in dimensionality. The curse of dimensionality refers to the
phenomenon that many type of data analysis become significantly harder as the dimensionality
of the data increases. Another way to reduce the dimensionality is to use only a subset of the
features. For example, irrelevant features contain almost no useful information for the data
mining task at hand. Feature creation is built from the original attributes and create a new set of
attributes that captures that important information in a data set much more effectively. Three
related methodologies for creating new attributes are the following: feature extraction, mapping
the data to a new space and feature construction. In the case of discretization and binarization,
support the data analyst to transform a continuous attribute into a categorical attribute
(discretization) and both continuous and discrete attribute many need to be transformed into one
or more binary attribute (binarization). And variable transformation refers to a transformation
applied to all the values of a variable.
In terms of data preparation, we analyzed the three data sets we obtained for the project:
WineAllData.arff, WinCorre.arff, and WineNoCorre.arff. To understand the data we used Weka.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can
either be applied directly to a dataset or called from your own Java code. Weka contains tools for
data pre-processing, classification, regression, clustering, association rules, and visualization. It
is also well-suited for developing new machine learning schemes. (Weka, 2016). As part of the
process, we uploaded the data into Weka. WEKA’s native data storage method is ARFF format.
The bulk of an ARFF file consists of a list of the instances, and the attribute values for each
instance are separated by commas. For each file the number of objects are 178.
WineAllData.arff has all 14 attributes discussed in the data understanding section. The
WinCorre.arff has six (6) attributes, which five (5) of them are numerical values and one (1)
nominal value, which will be defined as the class. And the WineNoCorre.arff, has eight (8)
attributes, which seven (7) numerical values and one (1) nominal, which will be defined as the
class. No missing values were found. In addition, the histogram for each attribute is verified

with the data type. For example all numeric attributes, the information presented its minimum
and maximum values, mean, and standard deviation.
Data Mining Algorithm
Classification is the most commonly applied data mining technique, which employs a set
of pre-classified examples to develop a model that can classify the population of records at large.
The data classification process involves learning and classification. In learning the training data
are analyzed by classification algorithm. In classification test data are used to estimate the
accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the
new data tuples. The classifier-training algorithm uses these pre-classified examples to determine
the set of parameters required for proper discrimination. The algorithm then encodes these
parameters into a model called a classifier. (Kumar, 2015)
A classification model is useful for the following purposes. Descriptive modeling, which
serve as as an explanatory tool to distinguish between objects of different classes. Predictive
modeling can also be used to predict the class label of unknown records. Classification
techniques are most suited for predicting or describing data sets with binary or nominal
categories. A general approach to solving classification problem (or classifier) is a systematic
approach to building classification models from an input data set. Examples include decision
tree classifiers, rule-based classifiers, neural networks, support vector machines, and naive Bayes
classifiers. (Tan, 2005)
Each technique employs a learning to identify a model that best fits the relationship
between the attribute set and class label of the input data. The key objective of the learning
algorithm is to build models with good generalization capability. The process of solving
classification problems involves a training set that consist of records whose class labels are
known and must be provided. The training set is used to build a classification model, which is
subsequently applied to the test set, which consists of records with unknown class labels. For the
purpose of this projects, all the data sets provided we know all the records.
It is important to evaluate the performance of the classification model. The evaluation of
the performance is based on the counts of test records correctly and incorrectly predicted by the
model. These counts are tabulated in a table known as a confusion matrix. The confusion matrix
provides the information needed to determine how well a classification model performs by
summarizing the information with a single number that would make it more convenient to
compare the performance of different models.
Accuracy is defined as follows: (number of correct prediction/total number of
predictions) and the error rate is defined as follows: (number of wrong predictions/total number
of predictions).
Decision tree builds classification or regression models in the form of a tree structure. It
breaks down a dataset into smaller and smaller subsets while at the same time an associated
decision tree is incrementally developed. The final result is a tree with decision nodes and leaf
nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and
Rainy). Leaf node (e.g., Play) represents a classification or decision. The topmost decision node
in a tree which corresponds to the best predictor called root node. Decision trees can handle both
categorical and numerical data.

Neural networks have proven themselves to be a valuable tool across a wide range of
functional areas affecting most businesses. Neural networks are simple computational tools for
examining data and developing models that help to identify interesting patterns or structures in
the data of your business. Neural networks can be used to learn to predict future events based on
the patterns that have been observed in the historical training data; learn to classify unseen data
into pre-defined groups based on characteristics observed in the training data; and learn to cluster
the training data into natural groups based on the similarity of characteristics in the training data.
We can use this technique for business problems in the areas of marketing and sales, risk
assessment and account, finance, management and strategy, and manufacturing. (Gupta)
ANN - Multi-layer Perceptron is more accurate than Decision Tree J48 for this type of
project in classifying the wine based on data provided. The following table shows a comparison
between algorithm, data set and correctly classified and wrongly classified.
Data Mining Algorithm Data Set Correctly Classified Wrongly Classified
Decision Tree J48 Wine All Data 93.8302% 6.1798%
ANN Multi-Layer Perceptron Wine All Data 97.1910% 2.8090%
Decision Tree J48 WineCorre 88.7640 % 11.2360%
ANN Multi-Layer Perceptron WineCorre 93.8202% 6.1798%
Decision Tree J48 WineNoCorre 88.7640% 11.2360%
ANN Multi-Layer Perceptron WineNoCorre 90.4494% 9.5506%
In this case, because the values are continuous is better to use ANN Multi-layer
perceptron in all the datasets. Other important point is that the WinAllData dataset have more
attributes that help to build a more accurate model. In the case of WinceCorre and WineNoCorre
datasets the accuracy is lower.
Experimental results and Analysis
For this project we used Weka. Weka is a collection of machine learning algorithms for data
mining tasks. The algorithms can either be applied directly to a dataset or called from your own

Java code. Weka contains tools for data pre-processing, classification, regression, clustering,
association rules, and visualization.
As part of the analysis to choose the algorithm for classification modeling, I started with decision
tree to analyze areas such as the amount of leaves, tree size, confusion matrix, correctly
classified instances, incorrectly classified instances for the following data sets: WineAllData.arff,
WinCorre.arff, and WineNoCorre.arff.
For the WinAllData data set we obtained the following results using cross-validation Folds 10:
Number of Leaves: 5; Size of the tree: 9; Correctly Classified Instances: 93.8302 %; Incorrectly
Classified Instances: 6.1798%
Confusion Matrix
A B C Classified As
58 1 0 a=A
3 67 1 b=B
0 5 42 c=C
For the WinCorre data set we obtained the following results using cross-validation Folds 10:

Number of Leaves: 8 ; Size of the tree: 15; Correctly Classified Instances: 89.3258 %;
Incorrectly Classified Instances: 10.6742 %
Confusion Matrix
A B C Classified As
55 4 0 a=A
4 64 3 b=B
1 7 40 c=C
For the WinNoCorre data set we obtained the following results using cross-validation Folds 10:

Number of Leaves : 8; Size of the tree : 15; Correctly Classified Instances: 88.764 %; Incorrectly
Classified Instances: 11.236 %
Confusion Matrix
A B C Classified As
51 6 2 a=A
4 63 4 b=B
3 1 44 c=C
In the case of decision tree, when applied each one of the data sets with different
attributes, the dataset that performed better was WineAllData. It is important to see that the
performance of each dataset when applied the decision tree is very different due to the amount of
relevant data in each dataset. When you are lacking of some relevant data the decision tree
becomes more complex and the probability of classifying wrongly some records increased, like
in the case of the WinNoCorre dataset.
Due to the nature of the wine chemical properties and classification models, artificial
neural networks can be a good fit to classify wine based on the datasets provided. Artificial
neural networks (ANN) was inspired by attempts to simulate biological neural systems. ANN is
composed on an interconnected assembly of nodes and directed links. Neural networks can be
used to learn to predict future events based on the patterns that have been observed in the
historical training data; learn to classify unseen data into pre-defined groups based on
characteristics observed in the training data like the datasets provided (WineAllData, WineCorre,
and WineNoCorre). For the purpose of this project and the data provided, Multi-layer
Perceptron will be used as the algorithm.
For the WineAllData dataset we obtain the following results when applying Multi-layer
Perceptron Cross-Validation 10:

Correctly Classified Instances : 97.191%; Incorrectly Classified Instances :2.809%.
Confusion Matrix
A B C Classified As
59 0 0 a=A
2 67 2 b=B
0 1 47 c=C
For the WineCorre dataset we obtain the following results when applying Multi-layer Perceptron
Cross-Validation 10:

Correctly Classified Instances : 93.8202%; Incorrectly Classified Instances : 6.1798%.
Confusion Matrix
A B C Classified As
58 1 0 a=A
5 63 3 b=B
0 2 46 c=C
For the WineNoCorre dataset we obtain the following results when applying Multi-layer
Perceptron Cross-Validation 10:

Correctly Classified Instances : 90.4494%; Incorrectly Classified Instances : 9.5506%.
Confusion Matrix
A B C Classified As
54 5 0 a=A
6 62 3 b=B
2 1 45 c=C
Conclusion and Future Work
This paper presents a study for the prediction of the wine chemical parameters (alcohol,malic
acid, ash, ash alkalinity, magnesium, total phenols, flavonoids, non flavanoid phenols,
proanthocyanidins, color intensity, hue, OD280/OD315 concentration, proline) to classify the
wine into A, B or C. We use the Decision Trees and Artificial Neural Networks to create models
for the classification objectives of the Data Mining concepts application. The experiments were
conducted using the new the open-source WEKA tool. Accuracies between 88% and 93% were
obtained for all datasets provided in the case of Decision Tree J48. This indicate that the use of
decision tree of Data Mining models can be used to predict the classification of the wine based
on chemical attributes. Accuracies between 90% and 97% were obtained for all datasets
provided in the case of ANN - Multi-Layer Perceptron. This indicate that the use of ANN is
better than Decision tree as Data Mining models to used to predict the classification of the wine
based on chemical attributes. With this work we present the advantages of the usage of Data

Mining tools to support the understanding what attributes and value affect the classification of a
wine based on chemical properties. In the future these attributes can be tie with consumer
buying decision making based not only on the classification of the wine but on the additional
chemical properties such as sugar level, among other new ones.
References:
Ajay Kumar Mishra , Dr.Subhendu Kumar Pani , Dr. Bikram Keshari Ratha. DECISION TREE
ANALYSIS ON J48 AND RANDOM FOREST ALGORITHM FOR DATA MINING USING
BREAST CANCER MICROARRAY DATASET. 2015. Retrieved from
http://www.ijates.com/images/short_pdf/1449290510_239D.pdf
Appalasamy, P., Mustapha, A., Rizal, N.D., Johari, F., Mansor, A.F. Classification-based Data
Mining Approach for Quality Control in Wine Production. Retrieved from
http://scialert.net/fulltext/?doi=jas.2012.598.601&org=11
Brownlee, J. Supervised and Unsupervised Machine Learning Algorithms. March 16, 2016.
Retrieved from
http://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.
CRISP-DM 1.0. 2000
Gupta, J. J. Neural Networks in Business: Techniques and Applications. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.466.8377&rep=rep1&type=pdf
Myers, K. 5 Stages of the Wine Making Process. Laurel Gray. November 14, 2014. Retreived
from http://laurelgray.com/5-stages-wine-making-process/
Provost, F., Fawcett, T. Data Science for Business. O’Reilly. 2013.
Ribeiro, K., Neve, J., Sanchez, J., Delgado, M., Machado, J., Novais, P. Wine Vinification
prediction using Data Mining Tools. Retrieved from
https://www.researchgate.net/publication/229011001_Wine_vinification_prediction_using_data_
mining_tools
Tan, P., Steinbach, M., & Kumar, V. Introduction to Data Mining, (2005). Addison-Wesley, ISBN

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