This repository contains scripts for building a machine learning pipeline to detect credit card fraud. The pipeline includes a machine learning model, preprocessing steps, and a random sample generator for testing purposes.

## Contents

1. [Machine Learning Model](#machine-learning-model)
2. [Pipeline](#pipeline)
3. [Random Sample Generator](#random-sample-generator)
4. [Purpose](#purpose)
5. [Contributors](#contributors)
6. [Contact Information](#contact-information)

## Machine Learning Model

The `Credit_card_fraud_detection.ipynb` script contains the implementation of the machine learning model for credit card fraud detection. It includes steps for data preprocessing, model training, hyperparameter tuning, and evaluation.

## Pipeline

The `pipeline.ipynb` script demonstrates the utilization of a machine learning pipeline for credit card fraud detection. It integrates preprocessing steps, such as feature scaling, with a trained Random Forest Classifier model to predict fraudulent transactions.

## Random Sample Generator

The `Random_sample_generator.ipynb` script generates random sample input data and makes predictions using the pre-trained machine learning model for credit card fraud detection. It allows for testing and validation of the model's performance.

## Purpose

The purpose of this repository is to provide an end-to-end solution for credit card fraud detection, from model development to deployment. By encapsulating preprocessing, model training, and testing steps within scripts, it simplifies the process of building and evaluating fraud detection models.

## Contributors

- Praveen Kumar K

## Contact Information

For inquiries or further information, please contact:
- Name: Praveen Kumar K
- Email: [email protected]