An Approach of Comparative Investigation of Classification Algorithm for Prediction of Google App
Internet business and audit destinations are overflowing with a great deal of undiscovered information with an unmistakable potential to change over into significant experiences that can help with powerful dynamics. This paper investigates utilizing information science and AI procedures on information recovered from one such road on the web, the Google Play Store. In day to day life, Google is playing a major role, which will help to perform our daily work, office activities. Google has many products in every domain like medical, education, finance, News, Entertainment, and Sports. Every industry is always going for different techniques for attracting the customer to buy new products. The customer ratings and reviews will always be the best way to reach as many customers as in their business. In this proposed work, using data analytics based on Google Play Store Apps, generally, there are different categories of apps available and our aim is to find the top five apps used by the customer ratings and reviews. In this work also compared the accuracy of classification algorithms like Random Forest Regressor, Bagging Regressor, KNN Regressor and Linear Regression based on mean square error value as per generated results, the random forest algorithm produced more accuracy in term of prediction.