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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Author(s): Ritu Khandelwal, Hemlata Goyal* and Rajveer Singh Shekhawat

Volume 15, Issue 3, 2022

Published on: 04 September, 2020

Article ID: e180322185587 Pages: 12

DOI: 10.2174/2666255813999200904164539

Price: $65

Abstract

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood, which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average, or Flop. For this, Machine Learning techniques (classification and prediction) will be applied. To make a classifier or prediction model, the first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that, different rules are generated, which helps to make a model and predict future trends in different types of organizations.

Methods: All the techniques related to classification and prediction, such as Support Vector Machine (SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN, will be applied and efficient and effective results will be obtained. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate.

Results: To make a classifier or prediction model, the first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm, and after that, different rules are generated which helps to make a model and predict future trends in different types of organizations.

Conclusion: This paper focuses on comparative analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits.

Discussion: Data Mining is the process of discovering different patterns from large data sets, and from that, various relationships are also discovered to solve various problems that come in business and help to predict the forthcoming trends. This prediction can help Production Houses for Advertisement Propaganda, and also, they can plan their costs, and by assuring these factors, they can make the movie more profitable.

Keywords: Decision tree, machine learning, prediction, orange, support vector machine (SVM), random forest.

Graphical Abstract

[1]
S. Pramod, and A. Joshi, "Prediction of movie success for real-world movie data sets", Int. J. Adv. Res. Ideas Innov. Tech., vol. 3, no. 3, pp. 455-461, May 2017.
[2]
J. Ahmad, P. Duraisamy, A. Yousef, and B. Buckles, "Movie success prediction using data mining", In 2017 8th International Conference on Computing, Communication and Networking Technologies, 2017, pp. 1-4.
[3]
V.R. Nithin, M. Pranav, B. Sarath, and A. Lijiya, "Predicting movie success based on IMDB data", Int. J. Data Mining Tech. Appl., vol. 3, no. 2, pp. 365-368, Dec 2014.
[4]
P. Sagar, "Analysis of prediction techniques based on classification and regression", Int. J. Comput. Appl., vol. 163, no. 7, pp. 47-51, Apr 2017.
[5]
M. Babita, K. Chaitali, M. Swati, and M. Grish, "Movies popularity prediction using social media and conventional features", Int. J. Innov. Res. Sci. Eng. Tech., vol. 6, no. 4, pp. 1-5, 2017.
[6]
"IMDb", Ratings, reviews, and where to watch the best movies & TV shows," Imdb.com. [Online].. Available at: http://www. imdb.com/ [Accessed: 14-Dec-2021].
[7]
"Lists of bollywood films", [Online]. Available at: http://en.wikipedia.org/wiki/Lists_of_Bollywood_films [Accessed: 14-Dec-2021].
[8]
"Box office India", [Online]. Available at: http://boxofficeindia.com/. [Accessed: 14-Dec-2021].
[9]
"Rotten tomatoes", Movies," Rottentomatoes.com. [Online]. Available at: http://www.rottentomatoes.com/. [Accessed: 14-Dec-2021].
[10]
"Screen daily", Screendaily.com. [Online].. Available at: http://www.screendaily.com/box-office/analysis/ [Accessed: 15 Sep-2021].
[11]
"Kernel functions-introduction to SVM kernel & examples", [Online]. Available at: https://data-flair.training/blogs/svm-kernelfunctions/. [Accessed: 15 Sep-2021].
[12]
M.T. Lash, and K. Zhao, "Early predictions of movie success: The who, what, and when of profitability", J. Manage. Inf. Syst., vol. 33, no. 3, pp. 874-903, July 2016.
[http://dx.doi.org/10.1080/07421222.2016.1243969]
[13]
Y. Zhong, "The analysis of cases based on decision tree", In 2016 7th IEEE International Conference on Software Engineering and Service Science, 2016, pp. 142-147.
[14]
R. S. DeFries, and J. C. Chan, "Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data", Remote Sens. Environ., vol. 74, no. 3, pp. 503-515, Dec 2000.
[http://dx.doi.org/10.1016/S0034-4257(00)00142-5]
[15]
M. Belgiu, and L. Drăguţ, "Random forest in remote sensing: A review of applications and future directions", J. Photogramm. Remote Sens., vol. 114, pp. 24-31, Apr 2016.
[http://dx.doi.org/10.1016/j.isprsjprs.2016.01.011]

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