Volume 11 - Volume 11
Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction
Abstract
Aim: The main motto of the study is to detect the frauds in mobile money transactions using logistic
regression and random forest algorithms and comparing their accuracy. Materials and Methods:
Logistic regression (N=10) and random forest algorithm(N=10) was iterated 20 times and detected
the frauds. Results and Discussion: Random forest has significantly better accuracy (99.6%)
compared to logistic regression (92.6%). The statistical significance of random forest algorithm
(p<0.018 Independent sample T-test) is high. Conclusion: Within the limits of this study, random
forest algorithm offers better accuracy to detect frauds in mobile money transactions.
Paper Details
PaperID: 2182
Author's Name: G. Manoj Kumar and Dr.M. Nalini
Volume: Volume 11
Issues: Volume 11
Keywords: Logistic Regression (LR), Random Forest (RF), Novel Money Transactions Fraud Detection, Machine Learning.
Year: 2021
Month: June
Pages: 1228-1240