Business Analysis & Data Visualization: Resolving Business Problems with Data Mining Techniques

In this ppt slide we will discuss about business analysis and below are the summaries point:- Project objective: Apply data mining techniques to resolve a business problem using the Boston housing dataset. Dataset analysis: Understand and discover relationships among the attributes in the Boston housing dataset. Business analysis tasks: List potential business analysis and provide effective solutions using classification and prediction algorithms.

Business Analysis & Data
Visualization Group
Assignment

Project Description
 Main objective of this project is to use the Boston housing
dataset to apply the data mining techniques to resolve a business
problem.
 Analysis the provided data set to provide the suitable business
solutions by using the Weka data mining tool.
 To analysis the provided data by review the current,
methodologies and algorithms for business analytics.

Understand the dataset
Analysis the provided data set, first user needs to understand the data
set. The provided Boston housing dataset is described as below.

Relationships discovery among
features
 In this task, user needs to discover the relationships existed
among all the attributes.
 Here, we are applying the normalization techniques to discover
the relationships among all the attributes in the Boston Housing
data.
 The normalization technique is used to remove the duplicates in
the data (Azzalini & Scarpa, 2012).

Business Analysis Tasks With
Your Justification
In this task, user requires to list the potential business analysis for a
provided data set. Here, we are using the classification and prediction
algorithm to resolve the business problem. And, also provide the
effective solutions for that problem. The effective results is used to
provides the following benefits for real estate consulting firm such as,
 Business benefits
 Improve the business process
 Support decision making
 Support strategy development.

ZeroR Classification
1. ZeroR is the most straightforward classification methods which
depends on the objective and predicts all Predictors.
2. ZeroR classifier essentially predicts the category which is class
(Witten, Frank & Hall, 2011).
3. Despite the fact that there is no consistency control in ZeroR, it
is helpful for deciding a standard execution as a benchmark for
other classification methods.
4. Algorithm Construct a recurrence table for the objective and
select it is most regular value.

ZeroR Classification
The ZeroR algorithm predicts the mean Boston House class values is
180921.19589041095. It must achieve an RMSE better than this value. The
ZeroR algorithm predicts the tested negative value for all instances as it is
the majority class, and achieves an accuracy of 82 % (Kaluža, 2013).

Multi Scheme Classification

Justification
1. In light of the above tables and figures, we can obviously observe that for the
Boston Housing data most significant accuracy is 100% and the least is 17.94
%. The other algorithm yields a normal accuracy of around 85%. In fact, the
most important accuracy has a place with the Multi scheme classifier. ZeroR
Classifier present at the base of the outline with percentage around 100%.
2. A normal of 1198 instances out of absolute 1460 instances is observed to be
effectively characterized with most elevated score of 262 occurrences
contrasted with 1460 instances, which is the least score (Maimon & Rokach,
2010). The total time required to build the model is likewise a basic parameter
in contrasting the classification algorithm. It is regular to recognize the
reliability quality of the data gathered and their legality.

Justification
 This analysis suggests a normally utilized pointer which is mean of
supreme errors and root mean squared errors. Then again, the
relative errors are additionally utilized.
 It is found that the most important error is found in ZeroR
Classifier with a normal score of around 0.821.
 A algorithm which has a lower error percentage will be favoured as
it has all the more powerful classification capability, so after
investigation we can say that ZeroR algorithm isn't appropriate for
a Data since it has most extreme number of errors and can't classify
the data effectively (Olson, 2017).

References
Kaluža, B. (2013). Instant Weka how-to. Birmingham: Packt
Pub.
Maimon, O., & Rokach, L. (2010). Data mining and
knowledge discovery handbook. New York: Springer.
Olson, D. (2017). Descriptive Data Mining. Singapore:
Springer Singapore.
Witten, I., Frank, E., & Hall, M. (2011). Data mining.
Burlington, Mass.: Morgan Kaufmann Publishers.