Data Analysis using RapidMiner: MITS5509 Assignment Solution

This document presents a solution to a data analysis assignment using RapidMiner, focusing on the application of neural networks and Support Vector Machines (SVM) for classification tasks. The assignment addresses the bankruptcy prediction problem, framing it as a classification challenge. The solution provides an overview of neural networks, explaining their function, architecture, and applications in financial services, particularly in market price analysis. It also details SVM, its use in regression analysis and classification, and its application of kernel methods for data transformation. The assignment includes diagrams illustrating kernel and process models, and weight tables, along with references to relevant research papers. The assignment is a group assignment for the MITS5509 course, and it requires the completion of a group participation form, emphasizing the collaborative nature of the project.

Running head: DATA ANALYSIS USING RAPID MINER
Data Analysis using Rapid Miner
Name of the Student
Name of the University
Authors Note:

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DATA ANALYSIS USING RAPID MINER
Part 1
Neural Networks
Neural Network stands out to be algorithm that helps in understanding the existing
relationship with set of data. All the given data works in similar way in which the brain of human
works(Dong, Loy and Tang 2016). Neuron system is completely referred by the help of neural
networks. Neural network can easily adapt to the changes that are done in the input. Network
aims to generate the best kind of positive value without the need of any criteria. Neural network
helps in development of the whole process that helps in credit risk, forecasting in time-series and
classification of different securities.
Neural network is inclusive of different layers of interconnected nodes. Each of the node
works in similar way like the multiple regression. Signal field is completely produced by linear
regression for activating different function that stand out to be non-linear in nature. In the multi-
layered protocol, all the perceptron’s are completely arranged in the given interconnected layer
(Han et al. 2016). All the inputs are collected by the input layer. On the contrary, the output layer
aims to output signal and classification of patterns that stands out to be helpful. Neural networks
are being used in variety of application that lies within different financial services ranging from
fraud detection to fraud detection to market research. Neural network stand out to be useful for
analyzing the market price for stock.
Support Vendor Machine
Support Vendor Machine is used for different learning models which have different
learning algorithms for data analyses. It is merely needed for complete regression analysis and its
classification. There is need for training algorithm for SVM building that assigns a specific value

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to a category. Model for SVM provides a complete value for point in space which has certain
values for regression issues (Santoro et al. 2017). The given technique is used like a kernel
method for data transformation. Based on the findings of optimal boundary all the possible input
are seen. It helps in carrying out complicated data transformation and analyzing the procedures.
SVM comes up with the capability for performing both regression and classification.
All the required data are taken and completely transformed by the help of kernel trick. All
the algorithm of SVM can help in computing all the given personal data. The main objective of
hyperlane provided within N dimension space can help in classifies different data points. In order
to separate the data point of the two given classes, there are many hyperlanes that can be selected
(Zhang et al. 2018). The main objective of SVM is all about analyzing the maximum margin that
highlights maximum distance in between the data points. Hyperlanes stand out to be boundaries
of decision that helps in classifying decision points. All the data points falling on the side of
hyperlane will ultimately result in separate kind of classes. Dimension of the hyperlane does not
depend on any amount of the given features. Support vector stand out to be data points that
works completely with the hyperlane.

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Fig 1: Kernel Model
(Source: Created By Author)

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Fig 2: Process Model
(Source: Created By Author)

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Fig 3: Weight Table
(Source: Created By Author)

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References
Dong, C., Loy, C.C. and Tang, X., 2016, October. Accelerating the super-resolution
convolutional neural network. In European conference on computer vision (pp. 391-407).
Springer, Cham.
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A. and Dally, W.J., 2016. EIE:
efficient inference engine on compressed deep neural network. ACM SIGARCH Computer
Architecture News, 44(3), pp.243-254.
Santoro, A., Raposo, D., Barrett, D.G., Malinowski, M., Pascanu, R., Battaglia, P. and Lillicrap,
T., 2017. A simple neural network module for relational reasoning. In Advances in neural
information processing systems (pp. 4967-4976).
Zhang, X., Zhou, X., Lin, M. and Sun, J., 2018. Shufflenet: An extremely efficient convolutional
neural network for mobile devices. In Proceedings of the IEEE conference on computer vision
and pattern recognition (pp. 6848-6856).

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