RSK World - IoT Sensors Dataset | Time Series Analysis | Anomaly Detection | Sensor Fusion | RSK World

Time Series Anomaly Detection Sensor Fusion Predictive Maintenance Python IoT Analytics

This project features a comprehensive IoT Sensors dataset designed for professional time series analysis, anomaly detection, sensor fusion, and predictive maintenance applications. The dataset includes multi-sensor IoT data with temperature, humidity, pressure, motion sensor readings, and anomaly labels from multiple devices. Includes powerful Python scripts: analyze_sensors.py for data analysis, anomaly_detection.py for anomaly detection algorithms, sensor_fusion.py for sensor fusion, ml_prediction.py for machine learning models, real_time_simulator.py for real-time data simulation, and api_server.py for RESTful API. Also includes interactive demo website with Chart.js visualizations. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on IoT analytics, predictive maintenance, anomaly detection, and sensor fusion projects.

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Dataset Overview

Complete IoT sensors dataset with temperature, humidity, pressure, motion sensor readings, and anomaly labels from multiple devices for IoT analytics.

Multi-sensor IoT data with temperature, humidity, pressure, and motion readings
Multiple devices (DEV001-DEV005) with unique device identifiers
Anomaly labels for supervised learning and detection tasks
Motion detection indicators (0 = no motion, 1 = motion detected)
Time series ready format with ISO format timestamps
Ready for machine learning and anomaly detection
CSV and JSON formats available
Multiple analysis scripts included
Perfect for IoT analytics, predictive maintenance & sensor fusion applications

Dataset Structure & Files

Well-organized project structure with IoT sensor data, Python scripts for time series analysis, anomaly detection, sensor fusion, machine learning, and interactive demo.

iot_sensors.csv - Main dataset file with temperature, humidity, pressure, motion, and anomaly data
iot_sensors.json - JSON format with structured data
analyze_sensors.py - Comprehensive IoT sensor analysis script
anomaly_detection.py - Anomaly detection algorithms (Z-Score, Isolation Forest, LOF)
sensor_fusion.py - Sensor fusion algorithms (Weighted Average, Kalman Filter, Bayesian)
ml_prediction.py - Machine learning models for classification and regression
real_time_simulator.py - Real-time data simulator and generator
api_server.py - RESTful API server for sensor data
index.html - Interactive demo website with Chart.js visualizations
README.md - Comprehensive project documentation
PROJECT_INFO.md - Project details and metadata
requirements.txt - Python dependencies (pandas, numpy, matplotlib, seaborn, scikit-learn, flask)
LICENSE - MIT License file
.gitignore - Git ignore configuration
Consistent naming convention across all files
Easy to load with pandas (pd.read_csv)
Time series ready format
Multiple export formats (CSV, JSON)
Device-based analysis capabilities
Time-based pattern analysis
Anomaly detection analysis
Sensor correlation analysis
Visualization generation support

IoT Analytics & Data Analysis

Complete analysis pipeline with support for time series analysis, device-based analysis, anomaly detection, sensor fusion, and IoT analytics.

Basic Statistics - Overview of dataset including total records, date ranges, devices
Device Analysis - Sensor patterns by device with aggregated statistics
Time Analysis - Sensor patterns by time of day, temporal trends
Anomaly Analysis - Anomaly detection and distribution patterns
Correlation Analysis - Relationships between temperature, humidity, pressure, and motion
Visualization Generation - Multiple chart types for data exploration
Export functionality - Summary reports and data exports
Statistical summaries - Mean, max, min, standard deviation calculations
Device comparison - Compare sensor patterns across different devices
Time-based patterns - Identify temporal trends and sensor behavior
Anomaly distribution - Analyze anomaly frequency and patterns
Sensor type analysis - Patterns by sensor type (temperature, humidity, pressure, motion)
Motion analysis - Motion detection patterns and trends
Temperature analysis - Temperature patterns and variations
Humidity analysis - Humidity patterns and trends
Pressure analysis - Atmospheric pressure patterns and variations
Chart generation - Time series plots, bar charts, correlation heatmaps
Data export - Export analysis results to CSV or JSON
Summary reports - Comprehensive analysis summaries

Multiple File Formats

Dataset available in multiple formats for maximum compatibility with different data science tools and ML frameworks.

CSV format - iot_sensors.csv (comma-separated values, UTF-8 encoded)
JSON format - iot_sensors.json with structured data
JSON Structure - Nested format with metadata, statistics, and sensor records
Pandas DataFrame ready - Direct loading with pd.read_csv()
NumPy array compatible - Easy conversion to numpy arrays for ML
Scikit-learn compatible - Ready for train_test_split, preprocessing, models
XGBoost/LightGBM ready - Compatible with gradient boosting frameworks
TensorFlow/PyTorch ready - Can be converted for deep learning models
Time series libraries ready - Compatible with statsmodels, Prophet, ARIMA
Standard data science formats - Widely supported formats
Easy to import and process - One-line data loading
Compatible with all ML libraries - Universal format support
Jupyter Notebook ready - Perfect for interactive analysis
Python pandas ready - Native pandas support
R compatible - Can be imported into R for analysis
SQL import ready - Can be imported into databases
API integration ready - JSON format for web services
Data validation support - Easy to validate and clean data
Time series ready - Proper datetime indexing for time series analysis

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive IoT sensor data explorer.

Interactive IoT Data Explorer - Chart.js powered dashboard
Multiple Visualization Charts - sensor trends, device patterns, time-based analysis
Sensor distribution charts - Visualize temperature, humidity, pressure patterns
Anomaly detection charts - Identify anomalous sensor readings
Device comparison charts - Compare sensors across different devices
Time series plots - Trend analysis over time
Correlation heatmap - Temperature, humidity, pressure, and motion relationships
Temperature analysis charts - Temperature patterns and variations
Humidity analysis charts - Humidity patterns and variations
Pressure analysis charts - Atmospheric pressure patterns
Motion detection charts - Motion patterns and frequency
Anomaly distribution charts - Anomaly frequency by device and time
Device-based visualization - Sensor patterns by device
Time-based visualization - Sensor patterns by time of day
Dataset statistics - Comprehensive summary statistics
Interactive Chart.js charts - Doughnut, Bar, and Line charts
Real-time chart updates - Dynamic filtering and visualization
Device and time filtering - Filter data by device and time period
Export functionality - Download analysis results and charts
Responsive design - Works on desktop, tablet, and mobile devices

Compatible Frameworks

Works with all major data science and time series analysis frameworks out of the box.

Scikit-learn ML library - Regression, clustering, preprocessing
Random Forest models - Ensemble learning for forecasting
Linear Regression - Fast and interpretable forecasting
Gradient Boosting - XGBoost, LightGBM, CatBoost support
Deep Learning - TensorFlow, PyTorch, Keras compatibility
Time series libraries - statsmodels, Prophet, ARIMA, SARIMA
NumPy numerical computing - Array operations and mathematical functions
pandas data manipulation - DataFrames, grouping, filtering, time series operations
matplotlib visualization - Static charts and graphs
seaborn statistical visualization - Advanced statistical plots
Statistical analysis (scipy) - T-tests, correlation, time series tests
Plotly interactive charts - Dynamic and interactive visualizations
Jupyter Notebook support - Interactive data science environment
Google Colab ready - Works in cloud-based notebooks
VS Code integration - Python extension support
PyCharm compatible - Full IDE support
IoT prediction models - Custom models for sensor data
IoT analytics tools - Time series analysis for sensor data management
Tableau/Power BI ready - Can be imported for business intelligence
R/RStudio compatible - Cross-platform data science
SQL databases - PostgreSQL, MySQL, SQLite import support

What You Get

Complete package with all files needed for professional IoT analytics, anomaly detection, sensor fusion, and time series analysis projects.

IoT sensor records with temperature, humidity, pressure, motion, and anomaly data
Multiple devices - DEV001, DEV002, DEV003, DEV004, DEV005
Python analysis script - analyze_sensors.py with comprehensive analysis functions
analyze_sensors.py - IoT sensor analysis with statistics, device analysis, time analysis, and visualizations
anomaly_detection.py - Anomaly detection algorithms with Z-Score, Isolation Forest, and LOF
sensor_fusion.py - Sensor fusion algorithms with Weighted Average, Kalman Filter, and Bayesian fusion
ml_prediction.py - Machine learning models for classification and regression
real_time_simulator.py - Real-time data simulator and generator
api_server.py - RESTful API server for sensor data access
iot_sensors.csv - Main dataset file (UTF-8 encoded)
iot_sensors.json - JSON format with structured data
index.html - Interactive demo website with Chart.js visualizations
Multiple Visualization charts - Time series plots, device patterns, anomaly analysis
Complete documentation - README.md, PROJECT_INFO.md
Documentation files - Comprehensive guides and project information
requirements.txt - All Python dependencies listed and versioned (pandas, numpy, matplotlib, seaborn, scikit-learn, flask)
LICENSE - MIT License (free for commercial and non-commercial use)
Ready-to-use code examples - Copy and run scripts immediately
Device-based analysis - Compare sensors across different devices
Time-based analysis - Sensor patterns by time of day and temporal trends
Anomaly detection analysis - Anomaly distribution and patterns
Correlation analysis - Relationships between sensor variables

Interactive Demo Website

Beautiful demo website with IoT sensor data explorer, IoT analytics dashboard, and comprehensive guide.

Modern animated design - Smooth transitions and visual effects
Interactive IoT Data Explorer - Filter and search sensor records
IoT Analytics Dashboard - Real-time statistics and metrics
Chart.js Integration - Interactive charts (Doughnut, Bar, Line charts)
Sensor Metrics - Visual representation of sensor data patterns
Filter by device and time period - Dynamic data filtering
Sensor feature visualization - Temperature, humidity, pressure, motion, anomaly visualization
Sensor distribution charts - Device-based, time-based breakdown
Dataset statistics display - Total records, devices, date ranges
Real-time chart updates - Charts update when filters change
Step-by-step usage guide - Comprehensive instructions
Dark theme with gradients - Modern, professional appearance
Fully responsive layout - Mobile, tablet, and desktop support
Data export options - Download CSV, JSON formats
Python scripts download - Access to all analysis scripts
Interactive filters - Filter by device, date range, time period
Sensor detail view - Individual record display
Statistics summary - Quick overview of dataset metrics
No backend required - Pure HTML, CSS, JavaScript
Cross-browser compatible - Works on Chrome, Firefox, Safari, Edge

Python Scripts Included

Professional Python scripts for IoT sensor analysis, time series analysis, anomaly detection, sensor fusion, machine learning, and data visualization.

analyze_sensors.py - Comprehensive IoT sensor analysis script
anomaly_detection.py - Anomaly detection algorithms (Z-Score, Isolation Forest, LOF)
sensor_fusion.py - Sensor fusion algorithms (Weighted Average, Kalman Filter, Bayesian)
ml_prediction.py - Machine learning models for classification and regression
real_time_simulator.py - Real-time data simulator and generator
api_server.py - RESTful API server for sensor data access
Basic statistics function - Overview of dataset including total records, date ranges, devices
Device analysis function - Sensor patterns by device with aggregated statistics
Time analysis function - Sensor patterns by time of day, temporal trends
Anomaly detection function - Anomaly detection and distribution patterns
Correlation analysis function - Relationships between temperature, humidity, pressure, and motion
Visualization generation - Automatic chart creation and saving
Time series analysis integration - Time-based pattern identification
IoT analytics utilities - Custom functions for sensor data processing
Dataset verification - Data type checking, missing value detection, range validation
Export functionality - Export analysis results to CSV or JSON
Report generation - Text-based analysis reports with insights
Error handling - Comprehensive error checking and informative messages
Code comments and documentation - Well-documented code for learning
Complete code examples - Ready-to-run scripts with examples
Modular design - Reusable functions for different analysis types
Best practices - Follows Python coding standards (PEP 8)
Chart generation - Time series plots, bar charts, correlation heatmaps
Summary statistics - Mean, max, min, standard deviation calculations