This repository contains the implementation of a Sensor Detection system using Machine Learning (ML). The system is designed with a modular code structure and utilizes pipelines for efficient processing of sensor data and making predictions based on the learned patterns during the training phase.
Sensor Detection using ML is a project that leverages machine learning techniques for analyzing and interpreting sensor data. The modular code structure enhances maintainability and extensibility, while the implementation of pipelines ensures efficient processing of data. The system can be applied to various domains such as industrial automation, environmental monitoring, healthcare, and more. The provided models are capable of detecting patterns and anomalies in sensor readings, contributing to improved decision-making processes.
- Modular Code Structure: Well-organized codebase with modular components for easy maintenance and extension.
- Pipeline Processing: Implementation of pipelines for efficient handling and processing of sensor data.
- Machine Learning Models: Implementation of ML models for sensor data analysis.
- Anomaly Detection: Identification of anomalies or unusual patterns in sensor readings.
- Scalability: Easily scalable for integration with different types of sensors and data sources.
- User-Friendly Interface: A simple and intuitive interface for users to interact with the system.
To install the necessary dependencies, use the following commands:
pip install -r requirements.txt
## Usage
- **Data Preparation**: Prepare your sensor data in the required format. Refer to the documentation for guidelines on data formatting.
- **Configuration**: Adjust configuration settings in the config.yaml file to match your specific use case and data characteristics.
- **Run the System: Execute the main script to start the sensor detection system.