Data Analysis using Rapid Miner | Neural Networks and Sales

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

DATA ANALYSIS USING RAPID MINER1
Part 1
Neural Networks
Neural networks are series of the algorithms which endeavours in recognizing
the underlying relationships within data set through process which mimics the process in
which human brain works. In such sense, the neuron system is referred by the neural
networks, either artificial or organic in nature. The neural networks could adapt to the
changing input. Hence, network generates best result possible without needing in redesigning
criteria for output. Neural networks’ concept, whose roots are artificial intelligence, is
gaining popularity swiftly in development of the trading systems (Santoro et. al. 2017).
Neural networks assist in development of the process as modelling of credit risk, constructing
price derivatives and proprietary indicators, time-series forecasting and securities
classification. Working procedure of neural network is same as neural network of human
brain. Neural network’s neuron is mathematical function which classifies and collects
information as per specific architecture. Network bears strong resemblance for the statistical
methods like regression analysis and curve fitting.
Neural networks includes interconnected nodes’ layers. Every node is
perceptron and is same as multiple linear regression. Perceptron feeds signal that is produced
by multiple linear regression to activation function which might be nonlinear. Within multi-
layered perceptron (MLP), the perceptron is arranged within the interconnected layers. Input
patterns are collected by input layer. Output layer possesses output signals or classifications
to which the input patterns might map. Patterns might comprise quantities list for the
technical indicators for the security. Neural networks are used within variety of the
applications within financial services, from fraud detection and forecasting to risk assessment
and marketing research (Dong, Loy and Tang 2016). Neural network is used for prediction of

DATA ANALYSIS USING RAPID MINER2
market price of stock. Neural network is used for building training model through selection
40 data points randomly form both category 0 and category 1 and is tested for its
performance. Price data is evaluated by using neural network for making decisions that are
based on data analysis.
Support Vendor Machine
Support Vendor Machine (SVM) are the learning models which have learning
algorithms which analyse data that is used for regression analysis and classification. Training
algorithm of SVM builds model which assigns the values to a category, making it binary
probabilistic linear classifier. Model of SVM is representation of value as the points in the
space, mapped as values of separate categories could be divided by clear gap which is wide.
SVM is supervised algorithm of machine learning that could be used for regression problems
or classifications (Al-Yaseen, Othman and Nazri 2017). This uses technique known as kernel
trick for transforming the data and depending on the transformations, this finds optimal
boundary among possible outputs. It does few extremely complicated data transformations
and figures out the procedure to separate the data depending on outputs or levels as defined.
SVM has the capability to perform both classification and regression.
The data provided is taken and transformed by kernel trick. Algorithm of
SVM could compute more optimal hyperplane. Objective of SVM is finding hyperplane
within N dimensional space which classifies distinctly data points. For separating data points’
two classes, several possible hyperplanes are available which could be selected. Objective of
SVM is finding plane which has maximum margin, that is, maximum distance among data
points for both classes. Increasing distance of margin provides few reinforcement as for data
points in future could be classified with much more confidence. The hyperplanes are the
decision boundaries which help in classifying data points. The data points which fall on each
side of hyperplane could be attributed to separate classes (Suthaharan 2016). Dimension of

DATA ANALYSIS USING RAPID MINER3
hyperplane is dependent on amount of features. For instance, if input features’ number is 2.
Then hyperplane is one line. This is tough in imaging when quantity of the features crosses 3.
The support vectors are the data points which are closer with hyperplane as well as influence
orientation and position of hyperplane. By using such support vectors, margin of classifier
could be maximised. These are points which help in building SVM.
Decision Tree
There are many analogies in a tree in machine learning, which covers both
regression and classification. Within decision analysis, decision tree could be used for
explicitly and visually representing decision making and decision. It uses model of tree for
decisions. Though most used tool in machine learning to derive s strategy for reaching
particular goal. It is tool for decision tool which uses tree like model or graph of the decisions
and the possible consequences, which includes outcomes of chance event, utility and
resources costs. This is a way for displaying algorithm which contains only statements of
conditional control. Decision tree is structure like flowchart in where test on attribute is
represented by every internal node, outcome of test is represented by every branch and class
label is represented by every leaf node (Ke et. al. 2017). Paths to leaf from root represent the
classification rules.
Learning algorithms based on tree are considered as mostly used and one of
best learning methods. Methods based on tree empower the predictive models having high
stability, ease for interpretation and accuracy. Unlike the linear models, non-linear
relationships could be mapped by them as well. They could be adaptive for solving any type
of issue at hand. Algorithms of decision tree are referred as Classification and Regression
Trees (CART). Decision tree have natural construction of “if...then...else” which makes this
easily fit into programmatic structure (Frosst and Hinton 2017). It could be well suited for
categorization problems in where the features or attributes are checked systematically for