Inspiration
To provide a quick and efficient solution that allows potential loan applicants to save time.
What it does
LoanTong provides an instant loan eligibility assessment given the user's current financial situation and the loan they wish to apply for. Using our trained model, LoanTong is able to provide accurate predictions of whether a loan would be approved or rejected.
How we built it
Frontend - React Backend - Django ML model - Jupyter notebook with scikit-learn
We utilized OpenAI API for user interactivity and conversion between user input, JSON, and pandas dataframe to input into our model.
Challenges we ran into
- Prompt engineering
- Connecting Django backend to external model
- Finding a suitable dataset
Accomplishments that we're proud of
- First hackathon in which we managed to deliver a finished product.
- Managed to create a useful and interactive chatbot.
What we learned
- Django
- Analytical skills
- scikit-learn
- Machine learning models
What's next for Team64_LoanTong
- Sleep
Built With
- django
- javascript
- jupyter
- matplotlib
- numpy
- openai
- pandas
- python
- react
- scikit-learn
- seaborn
- statistics
- tanstack
- typescript
- vite
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