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(Continued from previous scenario) Your next task is to demonstrate to the board of directors about the ability of applying business intelligence in the company's current business processes. To demonstrate BI, you need to prepare a presentation about BI and related tools & techniques and a demonstration on real company dataset. For the presentation, you need: - Explain general concept of what is BI - Introduction to some tools / techniques for BI and their application in general For the demonstration, you need: - A (some) data set(s) extracted from the company's business processes. Explain the dataset. - Show how you pre-process data for later analysis, explain each step and it purpose - Design dashboards to show your analysis on pre-processed data. Explain clearly purpose of dashboards and charts. Suggestions should be made after analysis During the demonstration, you need collect feed-back and comments from users to review how well your dashboards design meet user or busine
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Qualification BTEC Level 5 HND Diploma in Computing
Unit number
and title
Unit 14: Business Intelligence
Submission
date
Date Received 1st
submission
Re-submission
Date
Date Received 2nd
submission
Student Name Nguyen Dan Que Student ID GCS
Class GCS0905A Assessor name Nguyen Xuan Sam
Student declaration
I certify that the assignment submission is entirely my own work and I fully understand the
consequences of plagiarism. I understand that making a false declaration is a form of malpractice.
Student’s signature
Grading grid
Student Name/ID Number Nguyen Dan Que / GCS
Unit Number and Title 14: Business Intelligence
Academic Year 2018
Unit Tutor
Assignment Title Assignment 2: Apply BI tools & techniques and their impact
Issue Date
Submission Date 11/03/
IV Name & Date
Submission Format
Part I: Project submission. This should be a zip / rar folder of your project, including all
necessary files to run your project. There should be a link to your Tableau work on Tableau
Public cloud.
Part II: The submission is in the form of a group written report. This should be written in a
concise, formal business style using single spacing and font size 12. You are required to make
use of headings, paragraphs and subsections as appropriate, and all work must be supported
with research and referenced using the Harvard referencing system. Please also provide a
bibliography using the Harvard referencing system.
Part III: Team needs to present their point of view about how business intelligence tools can
contribute to effective decision-making as well as the legal issues involved in exploiting user
data for business intelligence. You may need to research for specific examples of organizations
that use BI tools to enhance or improve their business and evaluate how they can use BI tools
for extend their target audience and make them more competitive within the market.
Unit Learning Outcomes
LO3 Demonstrate the use of business intelligence tools and technologies
Assignment Brief
(Continued from previous scenario)
Your next task is to demonstrate to the board of directors about the ability of applying business
intelligence in the company's current business processes. To demonstrate BI, you need to
prepare a presentation about BI and related tools & techniques and a demonstration on real
company dataset.
For the presentation, you need:
For the demonstration, you need:
dataset.
of dashboards and charts. Suggestions should be made after analysis
During the demonstration, you need collect feed-back and comments from users to review how
well your dashboards design meet user or business requirement and what customization needed
for future use.
Team needs to present their point of view about how business intelligence tools can contribute
to effective decision-making as well as the legal issues involved in exploiting user data for
business intelligence. You may need to research for specific examples of organizations that use
BI tools to enhance or improve their business and evaluate how they can use BI tools for extend
their target audience and make them more competitive within the market.
To summary, you need to submit a report in PDF includes 4 parts: your presentation, result of
demonstration and review of user feedback, point of view on BI contribution and legal issues.
Assignment 2 answers
1. Introduction
1. 1 Overview of problems
Nowadays, the progress of the economy and human needs are constantly changing, buying a
house is no exception. However, the prices of houses are not stable and are always affected
by many different factors. On the other hand, the needs of home buyers are difficult to meet.
Therefore, the purpose of the model to predict the house price is based on many factors such
as architecture, location, quality, ...
Figure 1: Housing price factors
In machine learning, we can predict the trend of price based on available information, this is
applicable in most of the applications around us such as online shopping, entertainment, ...
Similarly, this is also applied to price prediction.
1. 2 Motivations
With the increasing demand for housing, meeting the needs of customers is also becoming
increasingly difficult. When deciding to buy a house, customers always want to find a house
with a convenient location, reasonable spaciousness, and many other requirements, but
especially, the price of the house must be cheap or at least reasonable. Applying machine
learning to house price prediction will significantly reduce the pressure on real estate. At the
same time, customers will also find it easier to find a suitable home for them at a reasonable
price. In this report, I will predict home prices in King County, United State.
1.3 Objectives
In this report, there will be some objectives that we’re focusing on:
on it?
on it?
are located
3. Proposed model
3.1 Correlation
A correlational research strategy looks into correlations between variables without allowing
the researcher to control or manipulate any of them. (Bhandari P, 2021)
A correlation is a measurement of the intensity and/or direction of a link between two (or
more) variables. A correlation’s direction might be either positive or negative. (Bhandari P,
Table 2: Correlation explanation
Positive correlation Both variables change in the same way.
Negative correlation Variables shift in opposing directions.
Zero correlation There is no connection between the variables.
Figure 2 : correlation coefficient
There are also many types of correlation coefficients, the most common one is Pearson’s due
to its strong inferences. (Bhandari P, 2021)
2
2
2
2
xy
is strength of the correlation between variables x and y
n is sample size
∑ is sum of what follows
X is every x-variable value
Y is every y-variable value
XY is the product of each x-variable score and the corresponding y-variable score
3.2 Linear regression
Linear regression is one of the most popular modeling techniques because, in addition to
explaining the relationship between variables (like correlation), it also gives an equation that
The coefficient of determination (R²) measures how well a statistical model predicts an
outcome. (Turney S, 2022)
The lowest possible value of R² is 0 and the highest possible value is 1. Put simply, the better
a model is at making predictions, the closer its R² will be to 1. (Turney S, 2022)
You can choose one out of two formulas to calculate the coefficient of determination:
Formula 1
2
2
r is Pearson correlation coefficient
Formula 2
2
RSS is sum of squared residuals
TSS is total sum of squares.
Adjusted R-squared
The Adjusted Coefficient of Determination (Adjusted R-squared) is a Coefficient of
Determination modification that takes the number of variables in a data set into account. It
also penalizes you for points that do not correspond to the model. (Vogt, 2005)
𝐴
2
2
n is sample size
k is number of independent variables
2
is coefficient of determination
3.5 Model estimation
To measure model accuracy, we use Mean absolute (MAE), Mean square error (MSE) and
Root mean square error (RMSE).
Mean absolute error (MAE)
The degree of inaccuracy in your measurements is expressed as absolute error. It represents
the discrepancy between the measured and "actual" values.
𝑖= 1
| 𝑥
𝑖
−𝑥
|
𝑛
n is the number of errors,
Σ is summation symbol (which means “add them all up”),
|x i
Mean square error (MSE)
The degree of inaccuracy in statistical models is measured by mean squared error (MSE). The
average squared difference between the observed and expected values is calculated. When
there is no error in a model, the MSE is 0. When model inaccuracy rises, so does its value. The
mean squared deviation is another name for the mean squared error (MSD). (Frost J)
𝑖
− ŷ i)
2
y i
is the i
th
observed value.
ŷ i
is the corresponding predicted value.
n = the number of observations.
Root mean square error (RMSE)
The standard deviation of the residuals is defined as the Root Mean Square Error (RMSE)
(prediction errors). Residuals are a measure of how far away data points are from the
4. Simulating scenario and results
4.1 Package installation
Also, I’m using Tableau for my project. I downloaded the program on https://www.tableau.com/..
After downloaded and run the program, you’ll see the main page as below.
Figure 3 : Tableau main page
Looking on the left side, is the taskbar of the main page. At the “To a file” section, I chose
“More…” and simply add the dataset in.
Figure 4 : Tableau work space
After adding it in, you’ll see the working space. Here, I can start working with my project.
4.2 Correlation
For the correlation of this dataset, I used the heat map below:
4.3 Scenarios
4.3.1 Dashboard
Figure 6 : Dashboard
Dashboard helps with the data visualization. It provides the user with an overview of the data.
Looking at the dashboard above, we can see the interaction between prices with different
components like square feet living, bathrooms, bedrooms and grade.
4.3. 2 Price and number of bedrooms
How can numbers of bedrooms affect the house prices?
Figure 7 : Price versus Bedrooms
The regression line shows that the relationship between number of bedrooms and the
house prices is linear positive. As the number of bedrooms increases, house prices will also
increase. However, the price of most houses with 5 rooms is not too different from that of
houses with 4 rooms. On the other hand, most houses with 3, 4, and 5 bedrooms have
approximately the same price. There are even bigger houses with lower prices. When buying
a house, customers are often afraid of the problem that if the house has many bedrooms,
the price will often be expensive. But based on the model, it can be seen that the number of
rooms does not affect the price of the house too much. Not much influence does not mean
that it is not an issue that needs attention. I think this feature still need to be focused on
when buying a house.
