This is machine Learning loan status classifier web application design & developed using Python, HTML, CSS, JavaScript & deployed in heroku cloud platform, with 86% accuracy. In this web application we can classifier the loan going to be accepted or not.
View Deployed Web Application In Cloud
- Model implementation & Evaluation : Click Here ( Download the HTML version of jupyter notebook in this path for better view of the model implementation & evaluation ).
- Exploratory data analysis & visualization.
- Identify the releationship between each attributes.
- Use feature engineering techniques to develop the model.
- Implement classification model & use hyper-parameter techniques to increase the accuracy.
- Evaluate the model.
- Deploy developed model in Heroku cloud platform using Flask web framework ( as a flask web application ).
This Dataset about,
- Gender
- Marital status
- Dependents
- Self employment status
- Education status
- Property area
- Applicant income
- Co-applicant income
- Credit history
- Loan amount
- Loan term ( Payback duration )
Python
- Flask | Scikit-learn | Pandas | Numpy | Matplotlib | Seaborn | Pickle | Gunicorn
Jupyter Notebook
Google Coloboration
Pycharm IDE
HTML
CSS
JavaScipt
- https://www.python.org/
- https://pypi.org/
- https://id.heroku.com/login
- https://jupyter.org/
- https://www.kaggle.com/
- https://colab.research.google.com/notebooks/intro.ipynb?utm_source=scs-index
- https://www.freepik.com/
- Feel free to use this for education purposes