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- W4379745793 abstract "The major purpose of this research is to evaluate the effectiveness of the Logistic Regression method in contrast to the Random Forest approach in terms of improving the accuracy of loan prediction. The results of the present research, a confidence interval of 95%, a threshold of 0.05 percent, and the mean and standard deviation were used to compile the data, which was obtained from a variety of sources. The G-power tool is scaled up by eighty percent, and two distinct kinds of algorithms are used in order to classify the data based on a sample size of n equaling ten. In contrast to the Random Forest approach, which has an accuracy of 75.6 percent, the Logistic Regression method has an accuracy that is very high at 81.30 percent. A difference of less than 0.05, or 0.001, represents a statistically significant gap between the two groups. It demonstrates that when calculating the amount of a customer's loan, the technique known as Logistic Regression seems to be more accurate than the alternative known as Random Forest." @default.
- W4379745793 created "2023-06-08" @default.
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- W4379745793 date "2023-04-06" @default.
- W4379745793 modified "2023-10-16" @default.
- W4379745793 title "Analyze the Lack of Accuracy in Loan Prediction using Logistic Regression Compared with Random Forest to Improve Accuracy" @default.
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- W4379745793 doi "https://doi.org/10.1109/iconstem56934.2023.10142597" @default.
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