DESCRIPTION
Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement
For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Tasks

• Feature Transformation : Transform categorical values into numerical values (discrete)

• Exploratory data analysis of different factors of the dataset.

• Additional Feature Engineering : You will check the correlation between features and will drop those
  features which have a strong correlation

• This will help reduce the number of features and will leave you with the most relevant features

• Modeling : After applying EDA and feature engineering, you are now ready to build the predictive
  models.

• In this part, you will create a deep learning model using Keras with Tensorflow backend.

Full Project :- Click Here