vedantkadam/FireDetection_IOE

IoT Fire Classification Project

Overview

This project leverages Internet of Things (IoT) technology and machine learning to classify fires based on smoke and their intensity. It integrates Blynk IoT for device connection, Google Sheets for data logging, Jupyter Notebook for data analysis, and Power BI for visualization.

Table of Contents

• Project Overview
• Hardware Setup
• Software Components
• Data Collection
• Machine Learning
• Data Analysis
• Visualization
• Contributing
• License

Hardware Setup

1. IoT Devices: Set up IoT devices (e.g., Raspberry Pi, Arduino) with smoke detectors and fire intensity sensors.

2. Blynk IoT: Connect your IoT devices to Blynk IoT to monitor and control them remotely.

3. Wiring: Properly wire the sensors and devices, ensuring accurate data collection.

Software Components

• Blynk IoT: Use Blynk IoT platform to establish a connection with your IoT devices.

• Google Sheets: Log data from IoT devices into Google Sheets for storage and easy access.

• Jupyter Notebook: Perform data analysis and machine learning model development in Jupyter Notebook.

• Machine Learning Libraries: Utilize machine learning libraries such as Scikit-Learn, TensorFlow, or PyTorch for classification.

• Power BI: Create interactive dashboards and visualizations using Power BI to monitor fire incidents and their intensity.

Data Collection

1. Set up Google Sheets to log data from your IoT devices.

2. Configure your IoT devices to send data to Google Sheets using appropriate APIs or libraries.

Machine Learning

1. Collect and preprocess data from Google Sheets for training and testing machine learning models.

2. Choose suitable machine learning algorithms for fire classification based on smoke and intensity.

3. Train and evaluate the chosen machine learning models using the collected data.

4. Fine-tune and optimize the models for better accuracy.

Data Analysis

1. Use Jupyter Notebook for in-depth data analysis, feature engineering, and model evaluation.

2. Visualize data distributions and correlations to gain insights into the fire classification problem.

3. Share the analysis findings and any necessary adjustments to the machine learning model.

Visualization

1. Create interactive dashboards and visualizations using Power BI.

2. Connect Power BI to your Google Sheets data source to display real-time fire classification results.

3. Share the Power BI dashboard with stakeholders for monitoring and decision-making.