WEKA is a free and open source data mining tool developed at the University of Waikato. It contains tools for data preprocessing, classification, regression, clustering, association rule mining, and visualization. This document demonstrates using WEKA for linear regression and clustering analysis. For regression, it builds a model to predict number of people employed based on economic factors. For clustering, it analyzes customer behavior data from a BMW dealership to identify patterns and group customers into five clusters to help the dealership improve sales.

1. WEKA- TOOL FOR DATA MINING
SUBMITTED BY :
DIVYA HAMIRWASIA
10BM60025

2. INTRODUCTION
Waikato Environment for Knowledge Analysis (WEKA) is a free and open source data mining
tool. Data mining is the transformation of large amounts of data into meaningful patterns and
rules, results of which could be used to take important business decisions. The ultimate goal of
data mining is to create a model, a model that can improve the way you read and interpret your
existing data and your future data. WEKA is the product of the University of Waikato (New
Zealand) and was first implemented in its modern form in 1997. It uses the GNU General Public
License (GPL). The software is written in the Java language and contains a GUI for interacting
with data files and producing visual results.
Advantages of Weka include:
free availability under the GNU General Public License
portability, since it is fully implemented in the Java programming language and thus runs
on almost any modern computing platform
a comprehensive collection of data preprocessing and modeling techniques
ease of use due to its graphical user interfaces
REGRESSION ANALYSIS
Linear regression is an approach to modeling the relationship between a scalar dependent
variable y and one or more explanatory variables denoted X.
The data we use here is trying to establish a relation between the number of people employeed
and:
the percentage price deflation
the GNP in millions of dollars
the number of unemployed in thousands
the number of people employed by the military
the number of people over 14
the year
REGRESSION IN WEKA:
Load the data by clicking on the preprocess tab. Click on the open file and choose the target
folder and then the requires .arff file. After selecting the file, your WEKA Explorer should look
similar to the screenshot:

3. To create the model, click on the Classify tab. The first step is to select the model we want to
build, so WEKA knows how to work with the data, and how to create the appropriate model:
Click the Choose button, then expand the functions branch.
Select the LinearRegression leaf.
Now that the desired model has been chosen, we have to tell WEKA where the data is that it
should use to build the model. Though it may be obvious to us that we want to use the data we
supplied in the ARFF file, there are actually different options, some more advanced than what
we'll be using. The other three choices are Supplied test set, where you can supply a different
set of data to build the model; Cross-validation, which lets WEKA build a model based on
subsets of the supplied data and then average them out to create a final model;
and Percentage split, where WEKA takes a percentile subset of the supplied data to build a final
model. These other choices are useful with different models, which we'll see in future articles.
With regression, we can simply choose Use training set. This tells WEKA that to build our
desired model, we can simply use the data set we supplied in our ARFF file.
Select number of people employed as the dependent variable. Click start. The result is as
follows:

4. The result is :
Number of people employed =206.3701 * percentage price deflation -1.2427 * number of
people unemployed -0.5971 * number of people employed by the military + 0.3079 * number
of people over 14 + 13699.5644
CLUSTER ANALYSIS
Clustering allows a user to make groups of data to determine patterns from the data. Clustering
has its advantages when the data set is defined and a general pattern needs to be determined
from the data. The data set used for clustering example focuses on the BMW dealership . The
dealership has kept track of how people walk through the dealership and the showroom, what
cars they look at, and how often they ultimately make purchases. They are hoping to mine this
data by finding patterns in the data and by using clusters to determine if certain behaviors in
their customers emerge. There are 100 rows of data in this sample.
CLUSTERING IN WEKA
Load the data into WEKA from the bmw.arff file. To do so click on the preprocess tab and then
click on the open file button. Select the target folder and select the needed file. Once the file is
opened all the attributes will be listed as follows:

5. Next, click on the cluster tab. Click Choose and select SimpleKMeans from the choices
that appear.
Finally, we want to adjust the attributes of our cluster algorithm by clicking SimpleKMeans.
The only attribute which needs to be adjusted is the numClusters field which lets us decide
how many clusters we want. We set this value as 5 here.

6. We click start and the clustering is done. The result is as follows:

7. INTERPRETATION OF THE RESULT:
Each cluster shows us a type of behavior in our customers, from which we can begin to draw
some conclusions:
Cluster 0 — The people in this group appear to wander around the dealership, looking
at cars parked outside on the lots, but trail off when it comes to coming into the
dealership, and worst of all, they don't purchase anything.
Cluster 1 — this group people tend to walk straight to the M5s, ignoring the 3-series
cars and the Z4. However, they don't have a high purchase rate — only 52 percent. This
is a potential problem and could be a focus for improvement for the dealership, perhaps
by sending more salespeople to the M5 section.
Cluster 2 — they aren't statistically relevant, and we can't draw any good conclusions
from their behavior. (This happens sometimes with clusters and may indicate that you
should reduce the number of clusters you've created).
Cluster 3 —they always end up purchasing a car and always end up financing it. Here's
where the data shows us some interesting things: It appears they walk around the lot
looking at cars, then turn to the computer search available at the dealership. Ultimately,
they tend to buy M5s or Z4s (but never 3-series). This cluster tells the dealership that it
should consider making its search computers more prominent around the lots (outdoor
search computers?), and perhaps making the M5 or Z4 much more prominent in the
search results. Once the customer has made up his mind to purchase the vehicle, he
always qualifies for financing and completes the purchase.
Cluster 4 — they always look at the 3-series and never look at the much more expensive
M5. They walk right into the showroom, choosing not to walk around the lot and tend to
ignore the computer search terminals. While 50 percent get to the financing stage, only
32 percent ultimately finish the transaction. The dealership could draw the conclusion
that these customers looking to buy their first BMWs know exactly what kind of car they
want (the 3-series entry-level model) and are hoping to qualify for financing to be able
to afford it. The dealership could possibly increase sales to this group by relaxing their
financing standards or by reducing the 3-series prices.
Other interesting way to examine the data in these clusters is to inspect it visually. To do this,
right-click on the Result List section of the Cluster tab and then click on the Visualize Cluster
Assignments. Change the X axis to be M5 (Num), the Y axis to Purchase (Num), and the Color
to Cluster (Nom). This will show us in a chart how the clusters are grouped in terms of who
looked at the M5 and who purchased one. We can see in the X=1, Y=1 point (those who looked
at M5s and made a purchase) that the only clusters represented here are 1 and 3. We also see
that the only clusters at point X=0, Y=0 are 4 and 0. It matches with our above result. Clusters 1
and 3 were buying the M5s, while cluster 0 wasn't buying anything, and cluster 4 was only
looking at the 3-series. Figure shows the visual cluster layout for our example.