Work

Improving App Rating Predictions through Robust Machine Learning Models

Year: 2022

Type: article

Abstract: In this research, we develop a machine learning model to predict the ratings of Google Play Store applications using a comprehensive dataset. Our approach includes extensive data preprocessing, feature engineering, and model optimization using Random Forest Regressor and ensemble methods. The proposed model demonstrates significant improvements in prediction accuracy, emphasizing the importance of advanced preprocessing techniques and ensemble learning in regression tasks.

Source: International Journal of Innovative Research in Science Engineering and Technology

DOI: 10.15680/ijirset.2022.1101134

Authors: Prof. Zohaib Hasan, Prof. Abhishek Singh, Prof. Vishal Paranjape, Gourav Mourya, Arjita Sarkar

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4401551020

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