Credbet is a predictive modeling project that classifies whether a loan will be approved or not, utilizing machine learning algorithms, specifically the Random Forest Classifier.
The project leverages datasets containing information from past loan applicants, considering various features to determine the loan status.
The random forest algorithm improves the flexibility and decision-making capacity of individual trees. It is another machine learning algorithm incorporating the ensemble learning theorem as its foundation, combining results from various decision trees to optimize training. In some use cases of loan and credit risk prediction, some features are more important than the rest or, more specifically, some features whose removal would improve the overall performance. Since we know the fundamentals of decision trees and how they choose features based on information gain, random forests would incorporate these benefits to give superior performance,
The machine learning model employed in this project is the Random Forest Classifier, well-suited for classification problems.
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Click on the "Predict" Button: Open the web browser and navigate to http://localhost:5000. Click on the "Predict" button on the website.
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Fill in the Required Form: Complete the form with information about the loan applicant. Input necessary details such as credit score, loan amount, lifestyle, career, and assets.
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Click on the "Submit" Button: After filling in the form, click on the "Submit" button to get the prediction.
DEMO-1
DEMO-2
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Clone the repository:
git clone https://github.com/m-rishab/Credbet.git
python app.py