Room Occupancy Detection

📌 Project Overview

This project predicts room occupancy based on sensor data using machine learning techniques. The goal is to improve energy efficiency in buildings by dynamically adjusting heating, lighting, and cooling based on occupancy status.

🚀 Features

• Data preprocessing and exploratory data analysis (EDA)
• Feature engineering and sensor data analysis
• Machine learning model development and evaluation
• Model interpretability and visualization

🛠 Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Jupyter Notebook

📂 Dataset

The dataset consists of sensor readings such as:

• Temperature
• Humidity
• Light Levels
• CO2 Levels
• Occupancy Status

📊 Machine Learning Models Used

• Logistic Regression
• Random Forest Classifier
• Support Vector Machine (SVM)
• XGBoost

🔥 Results

The models are evaluated based on accuracy, precision, recall, and AUC-ROC score. The best model provides reliable predictions for room occupancy.

📁 Repository Structure

📂 Room-Occupancy-Detection
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)

🖼 Code and Results

Include images of code and results in the images folder. Example:

📝 How to Run the Project

1. Clone the repository:

   git clone https://github.com/rohitinu6/Room-Occupancy-Detection.git

2. Navigate to the project folder:

   cd Room-Occupancy-Detection

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook or Python scripts to train and test models.

📡 Links

• GitHub Repository: Room Occupancy Detection
• Portfolio: Rohit Dubey
• GitHub Profile: rohitinu6
• LinkedIn: Rohit Dubey
• Twitter/X: @rohitdubey003

💖 Tags

Machine Learning Room Occupancy Sensor Data Data Science Python EDA

📝 License

This project is licensed under the MIT License.

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💡 For any queries or collaboration opportunities, feel free to connect! 🚀