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Here's a README.md for your NPM package that includes the advanced fraud detection using machine learning models:

Advanced Fraud Detection using Machine Learning

This package provides an advanced fraud detection system that leverages multiple machine learning models to predict the likelihood of a transaction being fraudulent. It uses ensemble learning techniques, combining predictions from various models to improve accuracy.

Installation

To use this package, you must first install it via npm:

npm install advance-fraud-detection-ml

Ensure you have TensorFlow.js installed, as it is a peer dependency:

npm install @tensorflow/tfjs-node

Usage

To use the fraud detection system in your project, you can import and call the advanceFraudDetectionUsingML function, passing in the transaction data as an input array.

const advanceFraudDetectionUsingML = require('<advance-fraud-detection-ml>');

const input = [/* your transaction data as an array */];
advanceFraudDetectionUsingML(input).then(result => {
  console.log(result);
});

Input Data Format

The input should be an array of numbers representing the features of your transaction data. These should match the feature set used when training the models.

Output

The function returns a promise that resolves to a string indicating whether the transaction is likely to be a fraud or not. The output is based on the average prediction score from all models used in the package.

Models

The package combines predictions from the following models:

• RandomForest
• XGBoost
• Gradient Boosting (GBBoost)
• Convolutional Neural Network (CNN)
• Long Short-Term Memory (LSTM)

These models should be pre-trained and saved in the specified directories within your project (./randomforest/model.json, ./xgboost/model.json, etc.).