IoT Occupancy Estimation

This repository contains the implementation of an IoT-based occupancy estimation system. Using advanced data preprocessing techniques and machine learning models, it aims to estimate occupancy in real-time based on environmental sensor data. The primary focus is to improve the accuracy and efficiency of predictions, making it suitable for smart building applications.

Table of Contents

• Features
• Installation
• Usage
• Repository Structure
• Data
• Modeling
• Contributing
• License

Features

• Preprocessing of environmental sensor data (e.g., temperature, humidity, light).
• Implementation of advanced machine learning algorithms, including XGBoost.
• Tools for feature engineering and model evaluation.
• Real-time prediction capabilities.
• Modular and extensible codebase.

Installation

1. Clone the repository:

   git clone https://github.com/pepperumo/IoT_Occupancy_Estimation.git

2. Navigate to the project directory:

   cd IoT_Occupancy_Estimation

3. Create a new conda environment:

   conda create --name iot_occupancy_env python=3.10
   conda activate iot_occupancy_env

4. Install the required dependencies:

   conda env update --file conda_environment_requirements.yml

Usage

1. Prepare the dataset by placing it in the data/ directory.

2. Open the Jupyter notebook for preprocessing and training:

   jupyter notebook notebooks/Preprocessing_and_XGBoost.ipynb

3. Follow the steps in the notebook to preprocess the data, train the model, and evaluate it.

4. The trained model is saved as models/xgboost_model.pkl and can be used for predictions.

Repository Structure

IoT_Occupancy_Estimation/
├── data/
│   └── Occupancy_Estimation.csv    # Dataset used for training and evaluation
├── models/
│   └── xgboost_model.pkl           # Trained XGBoost model
├── notebooks/
│   └── Preprocessing_and_XGBoost.ipynb  # Jupyter notebook for preprocessing and modeling
├── conda_environment_requirements.yml   # Conda environment configuration file
├── requirements.txt               # Additional Python dependencies
├── README.md                      # Project documentation
└── LICENSE                        # License information

Data

The dataset used for this project includes:

• Environmental features such as temperature, PIR, and light intensity.
• Occupancy labels indicating whether a room is occupied or not.

Ensure the dataset is properly formatted and placed in the data/ directory.

Modeling

The primary model used for occupancy estimation is XGBoost. The pipeline includes:

• Feature engineering
• Model training
• Hyperparameter optimization
• Evaluation metrics (accuracy, precision, recall, F1-score)

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push your changes:

   git push origin feature-name

5. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.