Built a Machine Learning model to predict the hourly electrical energy output of a Combined Cycle Power Plant using real operational data. The model achieved high accuracy (R² = 0.93), enabling intelligent energy forecasting and efficiency optimization.

This project uses an Artificial Neural Network (ANN) to predict the net hourly electrical energy output of a power plant using the Combined Cycle Power Plant dataset from the UCI Machine Learning Repository. Multiple ANN architectures were tested to compare performance. All models showed good prediction accuracy with reduced training loss.

The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the plant was set to work with full load. In this project first i separate the electrical energy column to apply some regression models on it. Then i apply sklearn machine learning model to classical machine learning algorithms in the tightly-knit world of scientific Python packages then i import train test split library to Split arrays and matrices into random train and test subsets. Then is use sklearn.ensemble module includes ensemble-based methods for classification, regression and anomaly detection to predict the model i use Gradient boosting Regressor it calculates the difference between the current prediction and the known correct target value. This prediction is to close.