Exploratory data analysis and success prediction of Google Play Store apps
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | أطروحة |
| اللغة: | English |
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BRAC University
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/11407 |
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10361-11407 |
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10361-114072022-01-26T10:19:59Z Exploratory data analysis and success prediction of Google Play Store apps Mueez, Abdul Ahmed, Khushba Islam, Tuba Iqbal, Waqqas Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Mobile app Google Play store Mobile apps. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (pages 64-65). Cataloged from PDF version of thesis. Mobile app distribution platform such as Google play store gets flooded with several thousands of new apps everyday with many more thousands of developers working independently or in a team to make them successful. With immense competition from all over the globe, it is imperative for a developer to know whether he is proceeding in the right direction. Unlike making a movie where presence of popular celebrities raise the probability of success even before the movie is released, it is not the case with developing apps. Since most Play Store apps are free, the revenue model is quite unknown and unavailable as to how the in-app purchases, in-app adverts and subscriptions contribute to the success of an app. Thus, an app’s success is usually determined by the number of installs and the user ratings that it has received over its lifetime rather than the revenue it generated. In this thesis, on a smaller scale, we have tried to perform exploratory data analysis to dive deeper into the Google Play Store data that we collected, discovering relationships with specific features such as how the number of words in an app name for instance, affect installs, in order to use them to find out which apps are more likely to succeed. Using these extracted features and the recent sentiment of users we have predicted the "success" of an app soon after it is launched into the Google Play Store. Abdul Mueez Khushba Ahmed Tuba Islam Waqqas Iqbal B. Computer Science and Engineering 2019-02-13T06:28:43Z 2019-02-13T06:28:43Z 2018 2018-12 Thesis ID 15101108 ID 15101020 ID 15141002 ID 15101109 http://hdl.handle.net/10361/11407 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 65 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Mobile app Play store Mobile apps. |
| spellingShingle |
Mobile app Play store Mobile apps. Mueez, Abdul Ahmed, Khushba Islam, Tuba Iqbal, Waqqas Exploratory data analysis and success prediction of Google Play Store apps |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Mueez, Abdul Ahmed, Khushba Islam, Tuba Iqbal, Waqqas |
| format |
Thesis |
| author |
Mueez, Abdul Ahmed, Khushba Islam, Tuba Iqbal, Waqqas |
| author_sort |
Mueez, Abdul |
| title |
Exploratory data analysis and success prediction of Google Play Store apps |
| title_short |
Exploratory data analysis and success prediction of Google Play Store apps |
| title_full |
Exploratory data analysis and success prediction of Google Play Store apps |
| title_fullStr |
Exploratory data analysis and success prediction of Google Play Store apps |
| title_full_unstemmed |
Exploratory data analysis and success prediction of Google Play Store apps |
| title_sort |
exploratory data analysis and success prediction of google play store apps |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11407 |
| work_keys_str_mv |
AT mueezabdul exploratorydataanalysisandsuccesspredictionofgoogleplaystoreapps AT ahmedkhushba exploratorydataanalysisandsuccesspredictionofgoogleplaystoreapps AT islamtuba exploratorydataanalysisandsuccesspredictionofgoogleplaystoreapps AT iqbalwaqqas exploratorydataanalysisandsuccesspredictionofgoogleplaystoreapps |
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1814309037076905984 |