Weka_10BM60025_VGSOM

WEKA- TOOL FOR DATA MINING

SUBMITTED BY :DIVYA HAMIRWASIA10BM60025

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 ANALYSISLinear 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:

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:

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:

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.

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

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.