Predictive Power Management in IoT Devices 🔋📊

This project focuses on predicting power consumption in IoT devices using machine learning algorithms.

🔍 Overview

IoT devices often consume power inefficiently. This model helps optimize energy usage by accurately predicting power needs based on input parameters.

📁 Technologies Used

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

⚙️ Algorithms

• Decision Tree Regressor
• Random Forest Regressor
• KNN Regressor
• Support Vector Regressor

🧪 Dataset

• Preprocessed with feature selection and scaling

📈 Results

• Achieved over 99% accuracy in predictions
• Evaluated using MAE, MSE, RMSE, R² score

💡 Key Features

• Data cleaning and visualization
• Model training and evaluation
• Graphs showing prediction vs actual values

🚀 Future Improvements

• Try advanced ensemble methods (XGBoost, LightGBM)
• Integrate with a dashboard or IoT simulator

📎 How to Run

1. Clone the repo
2. Open predictive_power_management.ipynb in Jupyter Notebook
3. Run cells step-by-step after setting up dependencies