Loan-Payback-Prediction-Using-Keras-API

1.Introduction

LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California.[3] It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.

https://www.kaggle.com/wordsforthewise/lending-club

You can also use clone this project to download all the required plugins and datasets:

git clone https://github.com/lauvindra/Loan-Payback-Prediction-Using-Keras-API.git

2. Objective

• To build a model that can predict whether or nor a borrower will pay back their loan.
• To get a new potential customer that can assess whether or not they are likely to pay back the loan in the future.

3. Keras API

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

4. Feature Engineering

Features are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly.There are 2 main reasons for feature engineering :

• Preparing the proper input dataset, compatible with the machine learning algorithm requirements.
• Improving the performance of machine learning models.

5.References

• https://www.kaggle.com/wordsforthewise/lending-club
• https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
• https://keras.io/

Check my Notebook for implementation of the loan prediction model using LendingClub dataset from Kaggle.