A machine learning approach to predict movie box-office success
This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
| Hauptverfasser: | , |
|---|---|
| Weitere Verfasser: | |
| Format: | Abschlussarbeit |
| Sprache: | English |
| Veröffentlicht: |
BRAC University
2018
|
| Schlagworte: | |
| Online Zugang: | http://hdl.handle.net/10361/9015 |
| id |
10361-9015 |
|---|---|
| record_format |
dspace |
| spelling |
10361-90152022-01-26T10:13:21Z A machine learning approach to predict movie box-office success Quader, Nahid Gani, MD. Osman Ali, Dr. Md. Haider Chaki, Dipankar Department of Computer Science and Engineering, BRAC University Movie industry Machine learning Vector machine SVM Neural network Sentiment analysis This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Cataloged from PDF version of thesis report. Includes bibliographical references (page 56). Making a prediction of society’s reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis. The motion picture industry is a multi-billion dollar business. And there is a huge amount of data related to movies is available over the internet and that is why it is an interesting topic for data analysis. Machine learning is a novel approach for analyzing data. Our paper proposes a decision support system for movie investment sector using machine learning techniques. In that case, our system will help investors related with this business to avoid investment risks. The system will predict an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomato, Box Office Mojo and Meta Critic. Using different machine learning algorithms, Natural Language Processing and other techniques the system will predict a movie box office profit based on some features like who are the cast and director members, budget, movie release time, various types of movie rating, movie reviews and then process that data for classification. Nahid Quader MD. Osman Gani B. Computer Science and Engineering 2018-01-11T04:05:49Z 2018-01-11T04:05:49Z 2017 2017 Thesis ID 13301019 ID 13301028 http://hdl.handle.net/10361/9015 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. 56 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Movie industry Machine learning Vector machine SVM Neural network Sentiment analysis |
| spellingShingle |
Movie industry Machine learning Vector machine SVM Neural network Sentiment analysis Quader, Nahid Gani, MD. Osman A machine learning approach to predict movie box-office success |
| description |
This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. |
| author2 |
Ali, Dr. Md. Haider |
| author_facet |
Ali, Dr. Md. Haider Quader, Nahid Gani, MD. Osman |
| format |
Thesis |
| author |
Quader, Nahid Gani, MD. Osman |
| author_sort |
Quader, Nahid |
| title |
A machine learning approach to predict movie box-office success |
| title_short |
A machine learning approach to predict movie box-office success |
| title_full |
A machine learning approach to predict movie box-office success |
| title_fullStr |
A machine learning approach to predict movie box-office success |
| title_full_unstemmed |
A machine learning approach to predict movie box-office success |
| title_sort |
machine learning approach to predict movie box-office success |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9015 |
| work_keys_str_mv |
AT quadernahid amachinelearningapproachtopredictmovieboxofficesuccess AT ganimdosman amachinelearningapproachtopredictmovieboxofficesuccess AT quadernahid machinelearningapproachtopredictmovieboxofficesuccess AT ganimdosman machinelearningapproachtopredictmovieboxofficesuccess |
| _version_ |
1814308307458850816 |