Loan-Status-Classifier-ML-Web-application

Introduction

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 ).

Objectives

• 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 ).

About the Dataset

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 )

Tools & Technology

Python

• Flask | Scikit-learn | Pandas | Numpy | Matplotlib | Seaborn | Pickle | Gunicorn

Jupyter Notebook

Google Coloboration

Pycharm IDE

HTML

CSS

JavaScipt

Resources

• 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/

License

• Feel free to use this for education purposes