Data Mining using Weka

Data mining techniques using WEKA

Submitted by:

Shashidhar Shenoy N (10BM60083)
MBA, 2nd Year, Vinod Gupta School of Management,
IIT Kharagpur

As part of the course “IT for Business Intelligence”

Introduction to Weka
Weka stands for ‘Waikato Environment for Knowledge Analysis’ and is a free open source software
developed by at the University of Waikato, New Zealand. It is a very popular set of software for
machine learning, containing a collection of visualization tools and algorithms for data analysis and
predictive modeling, together with graphical user interfaces for easy access to this functionality.

Although not as sophisticated as the other statistical packages, Weka’s popularity lies in the fact
that it is not only a freeware but also code is open source, which means that new algorithms can be
implemented by making use of the existing algorithms and sufficiently modifying them.

Weka can be used to do a wide variety of operations on the data. Some of the important operations
which can be carried out using weka suite are:

 Classification of data
 Regression analysis and prediction
 Clustering of data
 Associating data

A quick guide on how to carry out some of these operations is described in this document.

Quick note on the data used in the guide
Unless meaningfully interpreted, any data is meaningless. Most machine learning software would
accept any data as long as they are in the specified format without understanding why they are
used. Thus, the onus lies on the user of the software to choose proper data and feed it to the
software to derive meaningful insights on it.

Rather than using the pre-built examples given in Weka suite, some attempt is made to get freely
available data from the internet and the best place to get .arff files would be the Machine Learning
Repository located of UCI. The about page in their website says:

“The UCI Machine Learning Repository is a collection of databases, domain theories, and data
generators that are used by the machine learning community for the empirical analysis of machine
learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow
graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and
researchers all over the world as a primary source of machine learning data sets. As an indication of
the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited
"papers" in all of computer science”

For the demonstrations, two of the data sets have been used. Regression uses the data from Auto
MPG while the classification uses the data Contraceptive method choice. More details on the data and
its attributes are explained in the subsequent sections.

VGSoM, IIT Kharagpur Page 2

Regression using Weka

Simple regression involving two variables
Regression involves building a model to predict the dependant variable based on one or more
independent variables. A simple example of regression would be to predict the body weight of a
mammal given the brain weight. Here, the body weight is the dependant variable and brain weight
is the independent variable:

Figure 1: Brain weight v Body weight

The data is imported into weka in the native (Attribute-Relation File Format) arff format. Weka supports
imports of the ubiquitous .csv formats too. This is done by clicking on ‘Explorer’ in the Weka Gui
Chooser suite and then going to ‘Open File..’ under the preprocess tab.

Figure 2: Opening a file in Weka Suite


Once the file is loaded, a variety of pre-process operations can be done on the data. The data can be
edited using the ‘Edit’ option too. In the left section of the Explorer window, it outlines all of the
columns in the data (Attributes) and the number of rows of data supplied (Instances). By selecting
each column, the right section of the Explorer window will also give information about the data in
that column of your data set. There’s a visual way of examining the data, which we can see by
clicking the ‘Visualize All’ button.

The next step would be to perform the regression analysis. For this, we go to the ‘Classify’ tab and
click on the ‘Choose’ button. Since we are running a ‘simple linear regression’, we need to go to the
‘Classifiers.functions.simplelinearregression’ and click on it. Once this is done, we need to supply
the test options for building the regression model. The following options are available:

 Use training set. The classifier is evaluated on how well it predicts the class of the instances
it was trained on.
 Supplied test set. The classifier is evaluated on how well it predicts the class of a set of
instances loaded from a file. Clicking the Set... button brings up a dialog allowing you to
choose the file to test on.
 Cross-validation. The classifier is evaluated by cross-validation, using the number of folds
that are entered in the Folds text field.
 Percentage split. The classifier is evaluated on how well it predicts a certain percentage of
the data which is held out for testing. The amount of data held out depends on the value
entered in the % field.

Choose one of these for a model, make sure that the dependant variable is shown in the field below
as ‘body weight (kg)’ and click on start. This is the output we get:

Figure 3: Output of simple regression


It gives the model summary and the details of the regression. Thus, simple linear regression model
has been built using the weka suite.

Multiple Linear regression with many variables
In multiple regression, there is one dependant variable which depends on many independent
variables. Many of the real world situations are multiple regression models where one variable
depends on a lot of other variables. Here, we use a famous example data to demonstrate regression
using Weka.

Data used for multiple regression
This data set is taken from the UCI’s machine learning repository and regresses automobile mileage
against certain basic attributes of the model. The data can be downloaded from the URL
<http://archive.ics.uci.edu/ml/datasets/Auto+MPG> and a corresponding ARFF file be created.
This sample data file attempts to create a regression model to predict the miles per gallon (MPG)
for a car based on several attributes of the car (this data is from 1970 to 1982). The model includes
these possible attributes of the car: cylinders, displacement, horsepower, weight, acceleration,
model year, origin, and car make. Further, this data set has 398 rows of data.

Data Set Number of
Multivariate 398
Characteristics: Instances:

Attribute Categorical, Number of
8
Characteristics: Real Attributes:

Missing Yes… 8 instances of the variable horsepower are
Associated Tasks: Regression
Values? removed because they have unknown value

This data set is loaded into the Weka suite using the ‘Open file…’ syntax as explained before. This is
how the window looks like when the data is imported.

Figure 4: Imported data in Weka


The first seven attributes are all independant variables, while the eighth one, ie, CLASS is the
dependant variable for which we try and build a predictive model. Before doing so, we can use as
many visualizations on the data as necessary to see the relevant information in each attribute.

Figure 5: Visualize the data in Weka

The next step is to perform the regression. Go to the Classify tab and on the choose button, go to
classifiers -> functions -> linear regressions. Once this is done, we need to supply the test options for
building the regression model, in the same manner which we did for simple linear regression. We
initially give a ‘Percentage split’ of 80% of the test data and see the output:

Figure 6: Run information shown by Weka


Figure 7: The regression model ouput by Weka

Figure 8: Regression model details

This model might appear as complex for beginners but it is not. For example, the first line of the
regression model, -2.2744 * cylinders=6,3,5,4 means that if the car has six cylinders, you would
place a 1 in this column, and if it has eight cylinders, you would place a 0. We could use a test set
and see the deviation from the expected results and calculate the error.

Example data:
data = 8,390,190,3850,8.5,70,1,15
class (aka MPG) =
-2.2744 * 0 +
-4.4421 * 0 +
6.74 * 0 +
0.012 * 390 +
-0.0359 * 190 +
-0.0056 * 3850 +
1.6184 * 0 +
1.8307 * 0 +
1.8958 * 0 +
1.7754 * 0 +
1.167 * 0 +
1.2522 * 0 +


2.1363 * 0 +
37.9165

Expected Value = 15 mpg
Regression Model Output = 14.2 mpg

So, we see that the regression model output is pretty near the expected value and thus we have a
predictive model for beginners. We could continue to improve on this model to improve the
accuracy. We can also go for visualization to plot each of the independent variable against the
dependent one and see how the variation occurs. A sample plot of horsepower versus ‘Miles per
gallon’ is shown. The relationship can be found to be inversely proportional.

Figure 9: Visualizing the regression output

Classification using Weka
In classification, different attributes of a product are analysed to classify the product into one of the
predefined classes. For example, a cricket player can be classified as batsman, bowler, wicket
keeper or allrounder depending on the attributes like ‘Can bat?’, ‘Can bowl?’ etc.

TrainSet: The trainset is that data which is used to train the software. Here, the classification is
already made based on few attributes. The machine just observes the patterns and tries to create a
rule which can be used to explain how the training set data is classified. If the model built by the
machine in first instance is not reliable, intelligent algorithms might be used to make the model
more robust.

TestSet: The test set or data set is the actual data where the classification is not yet made. Once the
trainset is used to build a satisfactory model, we can feed the test set and get the classification of
the data set.


Data used for classification
The data used is the ‘Contraceptive Method Choice’ Data set available from the UCI’s machine
learning repository and can be downloaded from the following URL:

< http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice>

The samples are married women who were either not pregnant or do not know if they were at the
time of interview. The problem is to predict the current contraceptive method choice (no use, long-
term methods, or short-term methods) of a woman based on her demographic and socio-economic
characteristics. Some of the attributes like Wife’s age, Wife’s education, Husband’s education,
Number of children ever born’, etc are used to predict the current contraceptive method choice.

Data Set Characteristics: Multivariate Number of Instances: 1473

Attribute Characteristics: Categorical, Integer Number of Attributes: 9

Associated Tasks: Classification Missing Values? No

Use the ‘Open file..’ syntax to import the arff file into weka suite as instructed before. The tenth
attribute, ie, the contraceptive method used’ is the predicted variable and the data looks like this:

Figure 10: CMC data imported into Weka


Next, go to the classify tab, and use the ZeroR algorithm to run the classification model. ZeroR is the
basic classification model and it does not do anything but classify all the instances into one class.
We ask weka to run the model using the entire training set without splitting it into test and
trainsets. This can be done by giving the choice as ‘Use train set’ under ‘Test options’ as explained in
the case of regression before. As expected, the model will be inaccurate. This is the output of the
Weka file.

Figure 11: Classification Output using ZeroR algorithm

Of particular importance is the Confusion matrix which shows the correctly and incorrectly
classifcied instances. Here, we see that all samples have been classified as ‘a’ and the 333 samples
which should have been ‘b’ and the 511 samples which should have been classified as ‘c’ are also
incorrectly classified as ‘a’. Thus, the accuracy of the model is only 42% (629 out of 1473 samples)

We could now go for more accurate algorithms like NaïveBayes or NaiveBayesUpdateable to
improve the accuracy of the predictions. Here is the ouput of the NaiveBayes simple classification
scheme:


Figure 12: Classification output using Naive Bayes algorithm

Here we see that the accuracy of this model, although under acceptable limits has improved over
the previous model. Thus, we can start training the software to be more accurate by using better
algorithms.

Various visualization schemes are present which will help visualize the independent and dependant
variables.

Conclusion
In this term paper, two simple techniques which can be used to get started with Weka –regression
and classification are presented. In regression, we have demonstrated how Weka can be used to
build a regression model with one dependant variable and many independent variables. The live
example used was the automobile miles per gallon based on many independent attributes in a car.
In classification, we have demonstrated how Weka can be trained to classify the given data set
based on observations in a training set. The live data used was the choice of contraceptive method
based on a number of demographic factors.

Though the outputs are not intriguing, the real power of Weka lies in the fact that the algorithms
can be trained to produce better results. Since the source code is open for everyone, anyone can
download the same and simple manipulations can be done on the existing algorithms with ease to
produce more accurate algorithms. Hence, Weka is used by many researchers in their study.


References
1. Weka reference manual pdf available at their website
2. http://www.cs.waikato.ac.nz/ml/weka/
3. http://archive.ics.uci.edu/ml/datasets.html
4. http://www.ibm.com/developerworks/opensource/library/os-weka1/index.html#N100F6


Data Mining using Weka

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