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Predict whether a loan will be repaid using automated feature engineering.

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Predicting whether an applicant is capable of repaying a loan

Featuretools

As a bank decides which applicants to provide loans, they may wish to predict if the applicant will default on the loan. Through automated feature engineering, we can identify the predictive patterns in the financial data that can be used to ensure that clients capable of repayment are not rejected.

In this tutorial, we show how Featuretools can be used to perform feature engineering on a multi-table dataset of 300 thousand applicant financial information provided by Home Credit to train an accurate machine learning model to predict what if an applicant will repay a loan.

Highlights

  • We automatically generate 1820 features using Deep Feature Synthesis.
  • We are able to generate features, check that we are content with those features, and create the feature matrix.
  • We develop are able to generate features in 1 hour vs 10 hours with manual feature engineering.

Running the tutorial

  1. Clone the repo

    git clone https://github.com/Featuretools/predict-loan-repayment.git
    
  2. Install the requirements

    pip install -r requirements.txt
    

    You will also need to install graphviz for this demo. Please install graphviz according to the instructions in the Featuretools Documentation

  3. Download the data

    You can download the data from Kaggle. After downloading, save the CSV to a directory called input in the root of this repository.

  4. Run the Tutorial notebook:
    Automated Loan Repayment

    jupyter notebook
    

Feature Labs

Featuretools

Featuretools is an open source project created by Feature Labs. To see the other open source projects we're working on visit Feature Labs Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.

Contact

Any questions can be directed to help@featurelabs.com

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Predict whether a loan will be repaid using automated feature engineering.

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