Loan Approval Prediction Based On The Decision Tree And Random Forest Methods.
Level of Education of Students Involved
Graduate
Faculty Sponsor
Hugh Gong
College
Arts and Sciences
Discipline(s)
Machine Learning
Presentation Type
Poster Presentation
Symposium Date
Spring 4-27-2023
Abstract
Two of the most popular decision-making techniques used in machine learning are Decision Trees and Random Forests. In this project, I will be analyzing the differences between Decision Trees and Random Forests and will discuss the advantages and disadvantages of each technique in detail. In particular, I have computed the F1-score of each technique for the same data set in order to compare their performance. The F1-score is a weighted average of the precision and recall, with 1 representing the best score possible and 0 the worst score possible. In addition, I compare the predictions made by Decision Trees and Random Forests and create graphical visualizations of how much importance each technique gives to each variable in the data set.
Recommended Citation
Nimmagadda, Ravi Kiran, "Loan Approval Prediction Based On The Decision Tree And Random Forest Methods." (2023). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1170.
https://scholar.valpo.edu/cus/1170
Biographical Information about Author(s)
Myself Ravi Kiran Nimmagadda pursuing my Masters in Analytics and Modeling and pursued my undergraduate in Computer Science and Engineering and I’m from India. I always love to work and learn the different concepts and techniques in Data mining and Analytics. I worked on a project using supervised machine-learning techniques during my undergraduate studies.