Loan Approval Prediction: Case Study

Download the dataset below to solve this Data Science case study on Loan Approval Prediction. (Dataset Source: Kaggle)

Loan approval prediction refers to the use of machine learning techniques to predict the likelihood of a loan application being approved or denied by banks and financial institutions. By using advanced algorithms and predictive models, banks can streamline their loan approval processes and make informed decisions for the benefit of both lenders and borrowers.

The dataset mentioned here was submitted by Amit Parjapat on Kaggle. Your task is to create a predictive model that accurately determines whether a loan application should be approved or denied based on the given loan approval prediction dataset. The dataset includes various details about loan seekers including their personal and financial information, such as gender, marital status, education level, income, loan amount, loan term, credit history, property size and loan approval status.

Create a model that can accurately predict whether a loan request will be approved or denied, based on the details provided by the applicant. The model should have high accuracy in determining loan approval outcomes.

References to Solve this Data Science Case Study