neerajprasad209/credit_card_default_detection

Credit Card Fraud Detection

Overview

This is a classification model for a most common dataset, Credit Card defaulter prediction. Prediction of the next month credit card defaulter based on demographic and last six months behavioral data of customers.

Technical Aspect

This project is divided into two part:

1. Training a RandomForestClassifier classification model to predict defaulter as accurate as possible.
   • Cleaning the datasets, fixing all features
   • Apply Classification ML model
2. Building and hosting a Flask web app on Heroku.
   • Build the web app using Flask API
   • Upload the project on GitHub
   • Get the customer information from Web app
   • Display the prediction

Installation

The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries.

Software and Tools Required

1. Github account
2. AWS Account
3. VS code IDE
4. MongoDB
5. GitCli

Create a new environment of project

conda create -p venv python==3.8 -y

Technologies Used :

• Numpy
• Pandas
• Matplotlib
• Flask
• Seaborn
• Scikit-learn
• ipykernel
• MongoDB
• HTML
• AWS