/Credit_Card_fraud_detection

Primary LanguageJupyter Notebook

  • ABOUT

This is a project on credit card fraud detection which is deployed on production using Flask API

  • PREREQUISITES

You must have Scikit Learn, Pandas (for Machine Leraning Model) and Flask (for API) installed.

  • PROJECT STRUCTURE

    This project has four major parts :

    • main.py

      This contains code for our Machine Learning model to predict that transaction is fraud or legit based on training data in 'creditcard.csv' file.

    • app.py

      This contains Flask APIs that receives input through GUI or API calls, computes the precited value based on our model and returns it either 0(not a fraud) or 1(fraud).

    • request.py

      This uses requests module to call APIs already defined in app.py and dispalys the returned value.

    • templates

      This folder contains the HTML template to allow user to enter he input accoding to dataset i.e 29 inputs

  • RUNNING THE PROJECT

    • :-

      • Ensure that you are in the project home directory.
      • Create the machine learning model by running below command 
        python main.py
      • This would create a serialized version of our model into a file credit_fraud.pkl

    • Run app.py using below command to start Flask API

      python app.py
      • By default, flask will run on port 5000.

    • :-

      • Navigate to URL http://localhost:5000
      • You should be able to view the homepage.
      • Enter valid numerical/float values in all 29 input boxes and hit <b>START ANALYSIS</b>.
      • If everything goes well, you should be able to see the predcited vaule(either 0 or 1) on the HTML page!

    • You can also send direct POST requests to FLask API using Python's inbuilt request module

      • Run the beow command to send the request with some pre-popuated values - 
        python request.py

  • THANK-YOU FOR VISITING 💯