🔌 Power Output Prediction using Machine Learning 📌 Project Overview This project predicts the full load electrical power output of a Combined Cycle Power Plant (CCPP) using Machine Learning. By analyzing environmental factors like temperature, exhaust vacuum, pressure, and humidity, the model estimates power output in megawatts (MW).

The project includes a Flask-based web application that allows users to input values and get real-time predictions.

🛠️ Technologies Used Programming Language: Python Machine Learning: Scikit-Learn (Random Forest Regressor) Web Framework: Flask Frontend: HTML, CSS Data Visualization: Matplotlib, Seaborn.

📥 Installation & Setup 1️⃣ Install Required Libraries Run the following command to install dependencies: pip install flask numpy pandas scikit-learn matplotlib seaborn pickle5

2️⃣ Run the Flask Application Start the web server by running: python app.py After running the command, open your browser and visit: http://127.0.0.1:5000/ 📊 How to Use the Application 1️⃣ Open http://127.0.0.1:5000/ in a web browser. 2️⃣ Enter input values for:

AT (Ambient Temperature in °C) V (Exhaust Vacuum in cm Hg) AP (Ambient Pressure in mbar) RH (Relative Humidity in %) 3️⃣ Click Predict to get the power output estimate. 📌 Sample Inputs & Expected Outputs AT (°C) V (cm Hg) AP (mbar) RH (%) Predicted Power Output (MW) 20.5 50.2 1010.3 60.5 455.01 MW 25.0 40.0 1005.0 55.0 460 MW 30.0 55.0 1015.0 65.0 430 MW 🔍 Model Training Details Dataset: 9568 records (from 2006-2011) Algorithm Used: RandomForestRegressor R² Score: ~0.95 (High accuracy) Model File: CCPP.pkl 📌 Future Enhancements Deploy on AWS/Heroku Improve UI with Bootstrap/React.js Test with XGBoost & Deep Learning

📜 Author & Contact 👨‍💻 Bandi Bala Subrahmanyam 📧 balubandi83@gmail.com