PROJECT_INFO.md

# IoT Sensor Dataset - Project Information

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IoT Sensor Dataset - Project Information
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Author: RSK World
-->

## Project Details

**Project ID:** 19
**Title:** IoT Sensor Dataset
**Category:** Time Series Data
**Difficulty:** Advanced

## Description

Internet of Things sensor dataset with temperature, humidity, pressure, and motion sensor readings for IoT analytics and anomaly detection.

### Full Description

This dataset contains multi-sensor IoT data with temperature, humidity, pressure, motion, and other sensor readings over time. Perfect for IoT analytics, anomaly detection, predictive maintenance, and sensor fusion applications.

## Technologies

- CSV
- JSON
- Pandas
- Time Series

## Features

- Multi-sensor data
- Temporal patterns
- Anomaly labels
- Multiple devices
- Sensor fusion ready

## Project Structure

```
iot-sensors/
├── index.html # Interactive demo page
├── README.md # Main documentation
├── PROJECT_INFO.md # This file
├── LICENSE # MIT License
├── requirements.txt # Python dependencies
├── .gitignore # Git ignore rules
├── data/
│ ├── iot_sensors.csv # CSV format dataset (121 records)
│ └── iot_sensors.json # JSON format dataset
└── scripts/
├── analyze_sensors.py # Comprehensive analysis script
└── anomaly_detection.py # Anomaly detection algorithms
```

## Files Description

### index.html
Interactive web-based demo page with:
- Real-time data visualization using Chart.js
- Statistics dashboard
- Interactive charts (Temperature, Humidity, Pressure, Device Distribution, Anomaly Detection)
- Data preview table
- Download links for datasets and scripts

### data/iot_sensors.csv
CSV format dataset with 121 sensor readings from 5 devices (DEV001-DEV005) over 2 hours with 1-minute intervals.

### data/iot_sensors.json
JSON format dataset with the same data structure for programmatic access.

### scripts/analyze_sensors.py
Comprehensive Python analysis script that provides:
- Basic statistics and summaries
- Temporal pattern analysis
- Device-wise comparisons
- Correlation analysis
- Visualization generation (PNG outputs)

### scripts/anomaly_detection.py
Advanced anomaly detection script implementing:
- Z-Score method
- Isolation Forest algorithm
- Local Outlier Factor (LOF)
- Method comparison
- Visualization of detected anomalies

## Usage

1. **View Demo:** Open `index.html` in a web browser
2. **Load Data:** Use `data/iot_sensors.csv` or `data/iot_sensors.json`
3. **Run Analysis:** Execute Python scripts in `scripts/` directory

## Contact Information

**RSK World**
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277

## Notes

- All files include contact information in comments/headers
- Dataset includes anomaly labels for supervised learning
- Suitable for time series analysis, anomaly detection, and sensor fusion projects
- Compatible with pandas, scikit-learn, and other Python data science libraries

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