Inspiration
Save banks time in determining their potential clients.
What it does
Predict whom is the likely to accept a loan based on their lifestyle.
How we built it
Backend code: Python and Jupiter notebook Frontend: HTML and CSS
Challenges we ran into
Overfitting from over dividing the leaves, missing data
Accomplishments that we're proud of
Get a beautiful tree!
What we learned
Machine learning model and front end development
What's next for Loan_Acceptance_Prediction
Be able to connect the front and backend more efficiently and include more variables.
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