Abstract:
In recent days in Bangladesh the number of loan applicants for loans in commercial banks
are gradually increasing every year. Banking sectors always need a more accurate system
for handling many issues. In order to select the right applicant who can return the loan
amount within given time, the bank employees do a lot of analysis on the information
provided by the applicant and based on the analysis give a prediction. But it is very difficult
and time-consuming process for bank employees. To deal with this particular problem of
predicting the right applicant for loan request we use the EDA (Exploratory Data Analysis)
technique. A variety of machine learning models are used to aid in the task of loan
prediction. A dataset made up of loan projections is used to assess the study. Data cleaning
procedures were applied, such as deleting null columns, using the mean mode approach to
fill in missing values, and converting categorical values to numeric format. We employ
two different methods to provide the greatest outcomes from the feature selection process.
Traditional machine learning models employ distinct training and testing processes for both
features, which are derived from various feature selections. Bagging Classifier, out of all
the models, has attained the highest level of accuracy (88.00%), as well as a high recall and
F1 score. The method of univariate feature selection was used to achieve this. As a result,
the results suggest that Bagging Classifier might do very well when it comes to the task of
predicting loan defaults.
