Regression Algorithm: Background Study, Objective, Methodology, Results, and Conclusion

This document provides an overview of the regression algorithm, including its background study, objective, methodology, results, and conclusion. It explores how the algorithm can be applied in business analysis and discusses the benefits of using linear regression. The document also covers the process of data cleaning and its importance in data analysis. The results of the regression analysis are presented, along with suggestions for business management.

Running head: REGRESSION ALGORITHM
REGRESSION ALGORITHM
Name of the Student:
Name of the University:
Author Note:

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Table of Contents
Background Study...........................................................................................................................2
The Objective of the Project............................................................................................................2
Overview of the Report...................................................................................................................2
Business Analysis Problem..............................................................................................................3
The Analysis of the Business Problem............................................................................................3
Aim of the Business Analysis Tasks...............................................................................................3
Benefits............................................................................................................................................3
Methodology....................................................................................................................................3
Data cleaning...................................................................................................................................5
Conclusion.......................................................................................................................................8
References......................................................................................................................................10

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Background Study
The regression is one of the reliable method which helps in the identification of the
different variables which creates an impact on the selected topic (Harrell Jr 2015). The
regression helps in the identification of the different factors which is important and which can be
ignored for the topic. The regression analysis consists of two different variables which is the
dependent and independent variables. When the regression analysis is performed it is necessary
to identify the different dependent variable on which is influenced by the different independent
variables (Uyanık and Güler 2013). The regression analysis is helpful for the business
organization as it is capable of performing the predictive analysis. The advantage of the
regression analysis is used by the Boston Housing Authority.
The Objective of the Project
The main aim of the project is to get habituated with the different method of the
regression analysis when it is implemented and executed in the sector of the business
management. The objective is to study the different workflow and the steps that can be used in
the execution of the regression analysis for the business management. The project will identify
the different data mining process that can be used in the regression analysis.
Overview of the Report
The purpose of the report is to study the different business analysis issues that the BHA
faces during the regression analysis. The report also studies the different benefits that will be
achieved by the BHA after the introduction of the regression analysis in the task. The different
methods that are related to the data mining process, workflow and the algorithms which are used

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as a solution for the problems is described in the report. The data pre-processing is performed
and the outcome is stated in the report. A brief recommendation is also provided in the report.
Business Analysis Problem
The Analysis of the Business Problem
There are certain issues that are related with the BHA. The issues are linked with the
buildings portfolio management which is not processed properly. There are issues related with
the rents. The rents given by the residents is too high and they face problems when they pay the
rent bill (Ortiz‐de‐Mandojana and Bansal 2016).
Aim of the Business Analysis Tasks
The main purpose of the report is to identify the different problems that are there in the
system of project management. This indicates that if the BHA uses better and improve
management the efficiency will be increased and the BHA will gain lots of revenues.
Benefits
The benefits given by the process is for the clients. The benefit will help in keeping the
client which will help in the long run of the business (Vu et al., 2014). The revenue collected
will be beneficial for both the client and the BHA.
Methodology
In this case, linear regression algorithm is used. The association between object and more
than one predictors are found using linear regression. The functional process is facilitated by the

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integration of linear regression algorithm with data mining along with machine learning. The
gradient and descent is the main topic of discussion in this report.
Reasons for selecting linear regression algorithm
Liner regression algorithm is very helpful to establish a relation between two continuous
variables. This algorithm is related with machine learning and helps to improve the handling and
supervision of regression tasks. The most important utility of this algorithm is the differentiation
ability, which helps to differentiate target predictions, which are value based, with the free
variables. The accuracy of statistical relationship is very low for determining a relationship of
two unceasing variables. For this reason a best fit line is obtained for the data so that all the
prediction errors are very minor. The term error means the distance between the regression line
and the point. The important function used is cost function. The error is calculated in between the
real value and predicted value. It is expected that the error value should be minimum. Up
gradation of the intercept value plus the coefficient value are very important. The root mean
square values in between the forecasted values and actual values would become easy to manage
and this is a great advantage. One more advantage of updating the value is the reduction of
errors.
Gradient descent algorithm is very helpful to minimize a function by iterative movement
in the path of steepest descent and is referred to as the negative descent. Gradient descent is used
to reduce the cost function. RMSE value, the square root of the variance of the residuals, is also
reduced using the gradient descent.
Benefits of using this model:

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The main benefit of using the linear regression algorithm for this project is that it
provided with predicted outcomes. Prediction of the outcomes helps in identifying the risks and
by analyzing them the predicted risks can be reduced with proper management and there would
be less errors in the project. The inventory levels can also be determined using this algorithm and
thus, very helpful to understand the supply and demand curve of a project. Data analysis
increases the efficiency of the project by reducing the cost and providing better time
management. Sales predictions are also done using this algorithm and is very beneficial to
predict market trends and customer behavior. The organization is also becomes very productive.
Data cleaning
The data clearing process is used to remove unwanted observations and to free up
memory. Irrelevant observations and unwanted data is removed so that the management of the
collected data becomes easy and the duplicated or unwanted data are discarded. Combination of
datasets from different source results in duplication of data and hence takes excess memory
space. Fixed data sets are also included in this clearing process. Clearing of data results in free
space to store more informational data and which are useful by nature. Data processing also
becomes easy as data cleaning results in less conflict of data and reduces the time to search a
data. Data redundancy is also reduced by this method. By removing the corrupted data the
overall revenue is also increased for an organization. Consistency of data is very important for an
organization and data cleansing helps in the process. The number of returned mails are reduced
and accurate information is delivered to the customer. Duplicate data can drain the resources of
an enterprise as they will spend double the amount that is needed for a single customer.
Result and Discussion

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Present your results and findings
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.48372
516
R Square
0.23399
003
Adjusted
R Square
0.23247
0169
Standard
Error
8.05747
0672
Observatio
ns 506
ANOVA
df SS MS F
Signific
ance F
Regression 1 9995.18 9995.1 153.95 0.0000

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7265 87265 48831
Residual 504
32721.1
0815
64.922
83363
Total 505
42716.2
9542
Coeffici
ents
Standar
d Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
29.7548
9651
0.68344
5222
43.536
62234
6.705E
-173
28.4121
4398
31.0976
4904
28.4121
4398
31.0976
4904
indus
-
0.64849
0054
0.05226
4474
-
12.407
8557
4.9002
6E-31
-
0.75117
3125
-
0.54580
6982
-
0.75117
3125
-
0.54580
6982
Discuss your results
As per the data set that is generated it can be stated that the variables present are Multiple R with
the value of 0.48372516. The value that is generated via the R Square is 0.2339903. This is
considered as one of the major aspect that the data collected and hence the management of the
project will get better and the analysis process gets enhanced. The value that is gathered as per
the Adjusted R Square is 0.2324701 and the value that is gained as per the standard error is
8.0574. The percentage that is agreed includes 23.39%. Another aspect that is considered
includes differentiation of the Coefficients, Standard error, t Stat, P value and other values are
diversified into intercepts and the indus values. As per the performed analysis the main value of

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coefficients is 29.7548 and -0.64849 in case of the intercepts and indus respectively. The value
that is collected as per the standard error includes 0.68344 and 0.0522. t stat values that are kept
includes 43.5366 and -12.04. The value that is generated as per the intercept and indus includes
6.705E-173 and 4.90026E-31. The significance of the data collection set is at its highest.
Research Question 1: what is the percentage of the non-retail business acres in the town?
Research Question 2: What are the factors that will be enhancing the usage of the non retail
areas?
Provide any suggestion to business management
The suggestion that can be provided are as follows: -
 The rate of the non retail area needs to be diminished
 The taxation system needs improvement
Conclusion
Linear regression algorithm is implemented on the processing of the Boston housing
dataset. This report discusses about the implementation and the advantages of linear regression
algorithm. Different sections of houses present in Boston are included in this report. The
advantages of using data clearing and data analytics are also explored through this report. In
conclusion data mining and UCI machine learning process helps in the better organization and
management of the organizational data for better processing and better performance. Customer
relation and customer behavior analysis is improved and the revenue of the organization also gets
a boost up. For better predictive analysis a total of 506 samples and 13 features were included in

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the report. Predictive data analysis provides better processing and management of business
processes.

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