A loan application is used by borrowers to apply for a loan. Through the loan application, borrowers reveal key details about their finances to the lender. The loan application is crucial to determining whether the lender will grant the request for funds or credit.
The director of SZE bank identified that going through the loan applications to filter the people who can be granted loans or need to be rejected is a tedious and time-consuming process. He wants to automate it and increase his bank’s efficiency. After talking around a bit, your name pops up as one of the few data scientists who can make this possible within a limited time. Will you help the director out?
The idea behind this ML project is to build an ML model and web application that the bank can use to classify if a user can be granted a loan or not.
The dataset contains information about Loan Applicants. There are 12 independent columns and 1 dependent column. This dataset includes attributes like Loan ID, gender, if the loan applicant is married or not, the level of education, applicant’s income etc.
In this project sponsored by Foundation For Excellence through Coursera Project Works, you will:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry.
- Import key Python libraries, datasets, and perform Exploratory Data Analysis.
- Perform data visualization using Seaborn.
- Standardize the data and split them into train and test datasets.
- Build a machine learning model using logistic regression.
- Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs).
- Loan_Status: Loan granted or not (Y, N)