The ML-Pipeline for Mortgage Risk Assessment is a web-based application designed to provide users with a straightforward interface for assessing mortgage-related risks. The project incorporates both classification and regression models to address specific aspects of risk: the "Ever Delinquent" classification model and the "Prepayment Risk" regression model.
The primary goal of this project is to offer users a user-friendly platform for mortgage risk assessment. Users can interact with a simple HTML page where they input relevant information, such as mortgage details and borrower information. The two main functionalities include:
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Ever Delinquent Classification Model:
- Users can input mortgage-specific information.
- Clicking the "Classification Model" button triggers the underlying classification model, providing a prediction on whether the mortgage is likely to become delinquent.
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Prepayment Risk Regression Model:
- Similar to the classification model, users input necessary data.
- Clicking the "Regression Model" button invokes the regression model, offering a prediction on the prepayment risk associated with the mortgage.
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Input Data:
- Fill in the required details on the HTML page. This include mortgage amount, interest rate, borrower's credit score, and other relevant information.
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Ever Delinquent Classification:
- Click the "Classification Model" button to receive a prediction on whether the mortgage is likely to become delinquent.
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Prepayment Risk Regression:
- Click the "Regression Model" button to obtain a prediction on the prepayment risk associated with the mortgage.
run app.py to run the application
This project is the result of my internship at Technocolabs Software. I had the privilege of working alongside the talented professionals at Technocolabs, where I gained valuable insights and mentorship.
The ML-Pipeline-Using-Prepayment-factors-of-Mortgage-Loans has been built by Sara Salah