Exploratory Data Analysis and Prediction of Rating Of Various app on Google Play Store

Abstract

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency