Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Title:Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Volume: 15 Issue: 3

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

Affiliation:

• Department of Computer and Communication Engineering, Manipal University, Jaipur, India

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

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.

Cite this article as:

Khandelwal Ritu , Goyal Hemlata *, Shekhawat Singh Rajveer , Comparative Analysis of Machine Learning Techniques Using Predictive Modeling, Recent Advances in Computer Science and Communications 2022; 15 (3) : e180322185587 . https://dx.doi.org/10.2174/2666255813999200904164539