CRM Updated Report

1. Analysis of Google Apps Installation
Submitted by
Karthik Sundaresan.S
18136931
Abstract: Android phones have become
easily available for everyone in the world
nowadays. The market of android is so
vast and complicated, it has solution to
most needs of humans. The application is
useful for everyone in any aspects of both
professional part and personal part of
their life. Cell phones are becoming so
user friendly by the means of
applications that is being available to the
users based on their needs. This study is
conducted on various google playstore
apps that is being installed by various
users in various categories and genres
based on the rating; reviews; content
type; and its cost. So the obtained data is
pre-processed and the correlation
between the variables are analysed using
SPSS and the visualisation is done using
the tableau software. Various aspects of
google apps installation are considered
for the analysis in this scenario.
Keywords: Google playstore application
Analysis.
I. Introduction
Any application would be developed for
any particular need to be satisfied. The end
users could be able to download the
application in their end by the usage of their
google playstore. Many factors are
considered while downloading an
application like its reviews; rating; genre
and its cost if it is paid. By doing a proper
analysis it increases the other developers to
develop a suitable application that could be
more usable by the user. Nowadays there
are more android applications developed
daily by more android developers, each
application has its own usage on a particular
field. The application also resolves more
major difficulties of human beings, like
from knowing a share market price to their
health condition. Different applications
solves different types of needs, nowadays
people are in need to monitor their health
every now and then. So for doing that they
require medical apps which can be
downloaded from playstore, these apps
helps the user to track their biological
activities so that they can keep a track on
their health. So the reason for installing
medical apps is to keep checking your
health conditions. There are about a set of
more than 7000 mobile application
belonging to different categories. The rapid
growth of mobile applications is attracting
many developers to see a good profit from
developing apps of different categories.
II. Hypothesis
The null hypothesis is assumed as “There
is no correlation between the variables”.
While the alternate hypothesis is assumed
as “There is a correlation between the
variables”.
III. Literature Review
[1] States that the primary approach for
integrating advertisements in a playstore
application is through the usage of any one
of the ad libraries. So in this paper the
empirical examination of whether there is a
relationship between the ad libraries
installed and the app user’s rating is
integrated to check if there is a good success

2. rate in the success rate of the application is
done.
[2] States whether the fault proneness of
API’s affects the success rate of android
apps by investigating the apps with low
success and the apps with high success.
There is also an assumption that fault prone
APIs can be prone to more crash which
causes the lower installation of those
applications. Another research was done on
whether change of proneness of API’s
affects the success of the apps. It was found
from the result that instability of API and
fault proneness impacts the success rate of
applications and the apps average ratings in
playstore is also considered.
[3] Discusses about the malicious activities
that is carried out in the plasytore to identify
the applications those falls under these
categories. So machine learning based
technique is used to identify the malicious
apps. The machine learning will look for
patterns in different ways. There will be
war between attackers and attackers if
malware attackers start developing new
algorithms. The proposed system can be
implemented to perform into the cloud to
avoid this issue to some extent.
[4] states that the application developers
can create apps that can knowingly or
unknowingly leak the information to
outside world. So some steps for inter app
and intra app data transfer techniques are
discussed. The proposed approaches
connects by constructing a highly precise
call graph. Taint analysis method is
performed to identify the API calls those
are sensitive in nature.
[5] states about the medical oriented apps
by monitoring the respiration rate in most
diseases that too mainly useful for
respiratory diseases. The paper tells about a
respiratory rate monitor app. This paper
mentions that constantly monitoring the
breathing rate is very essential. This app is
very less costly with less power
consumption and robust performance. This
is the reason why this app is downloaded a
lot.
IV. Dataset
The survey dataset used for the process is
titled as Google Play Store data. It
consists of a total of 10,500 rows and 13
columns in it. The dataset is extracted from
kaggle which has apps of all categories; no
of installs; reviews. The upcoming session
tells about data pre-processing and the
various techniques.
V. Methods and Techniques
5.1 Data Pre processing
Data pre-processing plays an important role
in any analysis. The downloaded data is
always not in the expected manner. To
clean and process the data Rstudio has been
used in the following ways R studio is used
for cleaning the data:
(i) Out of the 13 available variables
10 are found useful so those
variables are extracted.
(ii) The next big thing extracting
only important data rather than
selecting all 10,000 rows.
(iii) Some columns are converted
into as factor(), since they were
available in string format.

3. Sample R Code 2
5.2 Methodology
The main motive of this project is to find
out the correlation between the variables.
So for finding the Pearson correlation
between the variables SPSS tool is used to
know about the effective correlation among
the variables.
Fig 1: Correlation between variables
So it illustrates about the installation with
respect to various factors and it can
observed that it has both positive and
negative correlation with the variables. The
variables like rating; type; price and content
rating has a negative rating while variables
like reviews and size has a positive
correlation.
Fig 2: Histogram of Installation
The above figure tells about the histogram
of installation, which tells clearly about the
how installation of various applications
have occurred so far.
Fig 3: Histogram of Installation with price
The above figure explains about how far
pricing plays an important role in the
installation of the application.
Fig 4: Installation with Size

4. The above figure says clearly about how
size has impact with the installation of
application. It can be seen that the plot has
more scatter with lower size, which depicts
that lower the application size more the
installations. Like the apps with size range
from 5 MB to 30 MB has more installations.
Fig 5: Installation with Reviews
The above figure explains about how far
installation and reviews has an impact. It
says that when number of reviews are less
it has more installation in it. Like the
application having a review within 100000
has more installations in it.
Fig 6: Installation based on category
The above figure clearly tells about
installation of applications with the
category like which category has more
installations.
Fig 7: Installation with content rating
The above figure tells about how much
installations varies with the content rating
or the relationship between the content
rating and installation.
Fig 8: Installation based on price and
rating by category
The above figure tells about the relationship
between the installation based on the price
and category.

5. Fig 9: Installation Based on Size
Fig 10: Installation based on Category
The above figure tells about the installation
based on category. Like it tells about which
category of apps has more installation.
VI. Analysis
The installation of an app depends majorly
on the size and its reviews. Which can be
inferred clearly in the correlation by having
a positive value for it. While the
dependency with respect to rating, price and
content has a negative correlation with it. It
can also be seen in the histogram and bar
graph visualisations that the category and
size has more scatter points in it. So the
installation majorly depends on the size of
the application and the reviews given by the
existing users.
Fig 10: Installation based on Category
It can be seen that the game category
applications are installed more while
communication has second highest
installation in it. While photograph and
entertainment ranks next in the interest of
installation among the users. So the most
preferred category for users is the category
of games.
Fig 11: Category and its Price
The above figure explains about the price of
different apps based on their category. It
can be seen that the medical category
applications has more prices for its
installation while the gaming category

6. application have low prices for its
installation. The second highest pricing
category applications are of business
category.
Fig 12: Category and Rating
The above figure explains about the scale of
rating of different categories given by the
users. The most high rated applications are
from the categories of education while the
photography related applications ranks next
by rating and the applications related to
communication are rated less by users.
Fig 13: Category and Reviews
The above figure tells about the relationship
between the category and reviews of the
application. It can be seen that game
oriented applications have more number of
reviews for it, while it can be seen that
applications related to communications
stands next by terms of reviews and
applications oriented to medical re
reviewed very least by users.
VII. Value
In present days mobile phones are trying to
make human works easier and faster. Like
it tries to reduce the human effort of even
using the computer for all activities. The
raising usage of internet is making in all
works simpler by just the usage in a small
devices. There are more mobile application
developed for various purposes solves most
human needs like from checking to emails
to paying bills and even all banking
activities and even the volatile share market
rates can also be seen in a mobile device by
downloading a reed app for its purpose. The
purpose involved in this study is to give
some added value on the analysis
performed on the obtained data. As
discussed earlier in the paper, it can be
observed that installation of an application
majorly depends on reviews and size.
Which also has high positive values of
correlation comparing with other values. To
support this there are various points to
support this point.
(i) The primary thing that an
application developer needs to
focus on it’s the application
size, lower the application size
higher the installation. So it can
be observed that application size
and installation are inversely
proportional. So the developers

7. need to make sure that the size
of the application can be made
as lower as possible, so that it
won’t require more memory for
its installation.
(ii) The reviews by users also plays an
important role in the
installation, if customers are
more satisfied and if there are
more reviews for an application
then it is installed more. So
developers must make sure that
the application performs well so
that it promotes users to express
their reviews about its
performance. Which helps other
users to decide whether to
download a particular
application.
(iii) In the context of pricing of
application the developers
should make sure that
application is available at a free
of cost or at some reasonable
cost. So that it is more easy and
affordable for the users in terms
of downloading. As it can be
noted that apps at free of cost
has more installations than paid
ones. If a paid app has same
features as that of free available
apps then it attracts users and
also it saves the users money as
they can get an application of
same genre and serving same
purpose at a free of cost than
paying.
VIII. Conclusion
In this study, an analysis is done on various
factors influencing the google playstore
application for a smartphone is done by
using the obtained data from kaggle after
pre-processing. The pre-processing of data
is done in R, while Pearson correlation of
variables is done using SPSS in which the
histogram and scatter points is also viewed.
Also for data visualisations tableau and
powerBI software is used for viewing about
installs with different factors and the
category or genre that is installed more and
its price and reviews. One thing that can be
concluded from the analysis is that size and
reviews has more impact in the installation
of the application. While price; number of
reviews plays an important in a categorical
application installation. So the customer
satisfaction of an application is more
important so that it motivates the existing
user to make a positive review about the
application which in turn increases the
number of users installing the application to
increase. So the developer should develop
an application that occupies less size of a
phone and performs so good with lower
affordable price or an application at free of
cost so that the installation raises to a
greater extent. Hence, in this case since we
have 2 positive correlation values we need
to reject our null hypothesis(H0) and
approve alternate hypothesis(H1). Which
states that there is a correlation between the
variables.
List of references:
[1I. Mojica Ruiz, M. Nagappan and B. Adams,
"Impact of Ad Libraries on Ratings of Android
Mobile Apps - IEEE Journals &
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[2] G. Bavota, M. Linares-Vásquez, C.
Eduardo Bernal-Cárdenas, M. Di Penta, R.
Oliveto and D. Poshyvanyk, "The Impact of
API Change- and Fault-Proneness on the User
Ratings of Android Apps - IEEE Journals &
Magazine", Ieeexplore.ieee.org, 2019
[3]S. K and J. RAYMOND V, 2019.
[4]G. Modi, V. Laxmi, S. Naval and M. Singh
Gaur, 2019. .

8. [5] R. Bhattacharya and N. Bandyopadhyay,
"Real time Android app based respiration rate
monitor - IEEE Conference
Publication", Ieeexplore.ieee.org, 2019