Data Visualization and Business Intelligence: Crowd Funding & AirBNB

This report analyzes crowdfunding data using BigML and SPSS to identify key attributes for project success, advising a user named Penny on how to obtain funding for her creative projects. It also investigates AirBNB investment opportunities in Greater Melbourne for Rachel, focusing on worthwhile markets, best suburbs, and property types for maximum ROI, leveraging descriptive statistics in SPSS. The analysis includes data exploration, linear regression, and ensemble techniques to provide actionable insights for both crowdfunding and AirBNB investments, aiming to inform strategic decision-making.

University
Semester
Data visualization and business
intelligence
Student ID
Student Name
Submission Date
1

Table of Contents
Part 1 - Crowd funding..........................................................................................................................3
1. Data Explore..............................................................................................................................3
2. Determination of Most Important Attributes of the Project.......................................................8
3. Justification of Most Important Attributes of the Project.........................................................14
Part 2 - AirBNB...................................................................................................................................15
1. Questions.................................................................................................................................15
2. Data.........................................................................................................................................16
3. Data Analysis Method.............................................................................................................18
References...........................................................................................................................................22
Appendix.............................................................................................................................................23
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Part 1 - Crowd funding
This task refers to using BigML and SPSS to analyze the Kick starter data. A website
named kickstarter.com allows the users to create a project to obtain crowdfunding for creative
pursuits like video games, stage shows, files and so on. Penny Robinson is a very talented
young woman. She can paint, write, sing and more. In fact, she can do anything creative. But,
the only problem she has is, she does not have any money. So, she wants to obtain money via
crowdfunding to fund a creative project and she knows the spreadsheet full of data. She wants
to know if you can give her any advice about creating a crowd-funded project based on the
data in the file. Therefore, in this project, we are exploring the provided data analysis and
determine any information that will be useful to Penny, who attempt to determine the most
important attributes of the projects depending on whether a project succeeds. At last, present
the most important attributes and justify by using SPSS and BigML.
1. Data Explore
Here, we will explore the kick-starter data which has the following attributes and it is
illustrated below.
To explore the kick-starter, two business intelligence tool such as SPSS and BigML are
used and it is illustrated in the below section.
In SPSS business intelligence tool,
First, we will open SPSS tool and click on the file>Import>Excel to upload the kick-
starter data, which is illustrated below.
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The data exploration of Kick starter is successfully completed, which is demonstrated
below.
In BigML tool,
To explore the kick starter data on big ML, follow the below steps.
Go to BigML online.
Click on source to upload the provided data, which is illustrated below.
4

The source is successfully added as follows.
Once, the source is successfully added to BigML tool, we will configure the data set
to click the Configure data set for choosing a source file which is illustrated in the following
figure.
5

Then, enter the data set name and click on Create dataset. It is illustrated below.
6

The process of data set creation is illustrated below.
At last, the data is successfully explored which is illustrated below.
Next, we will determine the most important attributes of the project.
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2. Determination of Most Important Attributes of the Project
Here, we will determine the most important attributes of the project, which are used to
resolve Penny’s problem to obtain money via crowdfunding, so that she can fund a creative
project (Ahmed Sherif., 2016).
In SPSS,
Linear regression in SPSS is used to determine the most important attributes of the project.
To do linear regression on SPSS,
 Analyze > Regression > Linear.
 Next, we will choose the dependent and independent variable such as
highest_pledge_reward_$ and project_sucess.
The output of the linear regression is illustrated below.
The model output is represented below.
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 project_successb . Enter
a. Dependent Variable: highest_pledge_reward_$
b. All requested variables entered.
The model summary is represented below.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .064a .004 .004 3060.4280
a. Predictors: (Constant), project_success
b. Dependent Variable: highest_pledge_reward_$
The ANOVA table is represented below.
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 1093019119.
882
1 1093019119.
882
116.698 .000b
Residual 2664221170
61.036
28445 9366219.619
Total 2675151361 28446
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80.918
a. Dependent Variable: highest_pledge_reward_$
b. Predictors: (Constant), project_success
The coefficients output is represented below.
Coefficientsa
Model Unstandardized
Coefficients
Standardiz
ed
Coefficien
ts
t Sig
.
95.0% Confidence
Interval for B
B Std.
Error
Beta Lower
Bound
Upper
Bound
1 (Constant) 2413.3
95
25.5
32
94.5
25
.00
0
2363.3
52
2463.4
39
project_succ
ess
-
392.05
6
36.2
92
-.064 -
10.8
03
.00
0
-
463.19
1
-
320.92
1
a. Dependent Variable: highest_pledge_reward_$
The residual statistics is represented below.
Residuals Statisticsa
Minimum Maximum Mean Std.
Deviation
N
Predicted Value 2021.339 2413.395 2219.359 196.0213 28447
Residual -2412.3953 7978.6606 .0000 3060.3742 28447
Std. Predicted
Value
-1.010 .990 .000 1.000 28447
Std. Residual -.788 2.607 .000 1.000 28447
a. Dependent Variable: highest_pledge_reward_$
In BigML,
Click on supervised to choose the ensemble techniques for determining the most
important attributes of the project, which is illustrated in the following figure.
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Later, choose the objective field and type, and then click on create ensemble, which is
illustrated in the following figure.
The ensemble process is illustrated below.
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Next, we will view the output of the ensemble, as shown in the below figure.
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