Authors :
Simon Williams
Volume/Issue :
Volume 6 - 2021, Issue 5 - May
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3fys5MP
Abstract :
This study used a bank loan database to
check the applicability of the borrower classification
model and examined machine learning techniques. I
developed auxiliary vector machine models, decision
trees, and random forests, and compared their
prediction accuracy with benchmarks based on logistic
regression models. They analyzed the performance
indicators based on the overall ranking. My results show
that the performance of Random Forest is better than
other models. In addition, the performance of the
support vector machine model is poor when using linear
and non-linear kernels. My results show that banks have
the opportunity to create value. Improve standard
predictive models by researching machine learning
techniques.
Keywords :
Machine Learning, Artificial Intelligence, Supervised learning, Classification, Regression, Tensorflow.
This study used a bank loan database to
check the applicability of the borrower classification
model and examined machine learning techniques. I
developed auxiliary vector machine models, decision
trees, and random forests, and compared their
prediction accuracy with benchmarks based on logistic
regression models. They analyzed the performance
indicators based on the overall ranking. My results show
that the performance of Random Forest is better than
other models. In addition, the performance of the
support vector machine model is poor when using linear
and non-linear kernels. My results show that banks have
the opportunity to create value. Improve standard
predictive models by researching machine learning
techniques.
Keywords :
Machine Learning, Artificial Intelligence, Supervised learning, Classification, Regression, Tensorflow.
