Fraud App Detection of Google Play Store Apps Using Decision Tree
Komal A. Joshi, Sidharth Kumar, Jyoti Rawat, Ansita Kumari, Aayush Gupta, Neha Vaishnavi Sharma · 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) · 2022
Along the rise in the various mobile applications which are used in daily life, it's more necessary than ever to stay on top of things to decide which are safe and which don't. It is impossible to pass judgment. Our system is based on four parameter that include ratings, reviews, in app purchases and Contains ad to predict. Our system compares three models Decision Tree classifier, Logistic Regression and Naïve Bayes. These models were further analyzed on four parameters of F1 score, Recall, Precision and Accuracy. A good F1 score should be greater than 0.7 and a recall score greater than 0.5 is considered to be good with higher precision and accuracy. On analysis we found Decision tree model as a good model with accuracy of 85%, F1score of 0.815, Recall value of 0.85 and precision of 0.87