The objective of this project is to predict if a customer will get a loan given applicant income, loan amount, loan amount term, credit history, education status, self-employment status, property area, etc. A model is trained using the training data on previous customers’ loan approval history. A web service is created, which runs the trained model in the background. The service presents an interface through which any user can request to get an automated decision/prediction (i.e., chances of approval or rejection). In addition, the web service also provides a REST API, which can be used to get all the customer information. Later, additional customer information can be leveraged to train a bigger model.

• The web service is developed using the Django framework & REST API.
• To build the model, Tensorflow 2.0 is used.
• SMOTE is used to oversample the minority class (i.e., loan rejection).
• The data is normalized and preprocessed.
• The model is trained using a FFN with 4 dense layers (adding L1 regularization) and 2 dropout layers.
• The model has an accuracy of 80%.

This Project’s GitHub Repository

• Classification
• Application
• Django
• ANN