Create a model that predicts whether or not a loan will be default using the historical data.
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.
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
-
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
