Loan Approval Prediction Based On The Decision Tree And Random Forest Methods.

Level of Education of Students Involved


Faculty Sponsor

Hugh Gong


Arts and Sciences


Machine Learning

Presentation Type

Poster Presentation

Symposium Date

Spring 4-27-2023


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.

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.

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