For safe and secure lending experience, it's important to analyze the past data. In this project, a deep learning model is built to predict the chance of default for future loans using the historical data. This dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
- Load the dataset that is given to you
- 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
pip install --upgrade pip
pip install -r requirements.txt
pip list