Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
DESCRIPTION
For safe and secure lending experience, it's important to analyze the past data. In this project, you have to build a deep learning model to predict the chance of default for future loans using the historical data. As you will see, this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Objective: Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
Steps done:
⦁ Load the dataset
⦁ Check for null values in the dataset
⦁ Print percentage of default to payer of the dataset for the TARGET column
⦁ Balance the dataset if the data is imbalanced
⦁ Plot the balanced data or imbalanced data
⦁ Encode the columns that is required for the model
⦁ Calculate Sensitivity as a metrice
⦁ Calculate area under receiver operating characteristics curve
You can download the datasets from here - https://www.dropbox.com/s/smt43gz12eijbo6/loan_data%20%281%29.csv?dl=0