ME_Predict_server

Welcome to the ME_Predict_server repository! 🎉 This project includes the backend server and machine learning algorithms used for mouse usage analysis and prediction. 🚀

Overview

The Machine Learning Model

The project uses a KMeans clustering model to analyze mouse movement data. The model is trained using various features extracted from the raw data to identify different usage patterns. 🧠 The data used to build the model is sourced from Kaggle. data-source: https://www.kaggle.com/datasets/chaminduweerasinghe/stress-detection-by-keystrokeapp-mouse-changes

Data Representation

X and Y axes: Represent the coordinates on the screen. Z axis: Represents the speed of the mouse, calculated based on the change in position over time.

Features

• Backend Server: Built with Node.js and Express, handling API requests and data processing. 🖥️
• Machine Learning Models: Implemented in Python using scikit-learn, including a KMeans clustering model. 🧠
• Data Preprocessing: Scalers and data transformation scripts to prepare mouse usage data for analysis. 🔄
• RESTful API: Endpoint for data submission and prediction retrieval. 📡

Getting Started

Just Using the API

The server exposes an endpoint for interacting with the machine learning model and retrieving predictions.

POST /predict: Submit mouse usage data for prediction. The API expects x, y, and speed in the request body and returns the predicted cluster and user condition along with UI suggestions.

server: https://me-predict-server.onrender.com/

going for production

Prerequisites

Ensure you have the following installed:

• Node.js
• Python 3.x
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/sideffect263/ME_Predict_server.git
   cd ME_Predict_server

2. Install Node.js dependencies:

   npm install

3. Install Python dependencies:

   pip install -r requirements.txt

Running the Server

To run the backend server, use the following command:

node server.js