Notice Board :

Call for Paper
Vol. 14 Issue 4

Submission Start Date:
May 01, 2022

Acceptence Notification Start:
May 10, 2022

Submission End:
May 20, 2022

Final MenuScript Due:
May 22, 2022

Publication Date:
May 25, 2021
                         Notice Board: Call for PaperVol. 14 Issue 4      Submission Start Date: May 01, 2022      Acceptence Notification Start: May 10, 2022      Submission End: May 20, 2022      Final MenuScript Due: May 22, 2022      Publication Date: May 25, 2021




Volume XIII Issue VI

Author Name
Jyoti Jagriti, Chetan Agarwal, Priyanka Parihar
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 6
Abstract
By using machine learning the predictive model has increased in volume in recent years. The movie industry is still big with hundreds of new movies created every year and there are various factors which influence the movie success prediction such as critics, actors, directors, actress, and composer etc. For predicting movie success various authors propose or implemented different approach and algorithms such as KNN, SVM, Naïve Bayes Classifier, Logistic regression, random forest etc. In this paper, we present a literature on the prediction of movie success using different approaches of machine learning and also recommended for future work.
PaperID
2021/EUSRM/6/2021/61236

Author Name
Jyoti Jagriti, Chetan Agarwal, Prof. Priyanka Parihar
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 6
Abstract
The movie success factors depend on the critics, storyline, hero’s, music etc. To predict the movie success various data mining and machine learning techniques such as Guassian NB, Multinomial NB, Bernoulli NB, KNeighnors Classifier, Decision Tree, Logistic regression has been developed but, in this work, we use random forest classifier for the prediction of movie success with reduced cost and schedule. The random forest classifier selects the dataset randomly from the available dataset and the generate the decision tree of the selected dataset and then apply the voting on the prediction results and whose score and accuracy will be maximum that will indicates the success of movie. For the sample of IMDb dataset, we use online resource of kaggle and the experimental results is generated from the widely used machine learning programming language Python which helps in the analysis of the proposed methodology. The performance of proposed methodology is measured using the parameters such as
PaperID
2021/EUSRM/6/2021/61237