Energy-Consumption-Prediction-using-Machine-Learning

machine-learning model to predict the energy consumption of a steel factory based on various features of the properties. i attempted to build a regression model that can accurately predict the energy consumption of a steel factory given its features. i started by preprocessing the data by handling missing values, scaling the features , and splitting it into training and test sets. Next, i implemented and trained a regression algorithm such as Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, or Elastic Net Regression. then i evaluated the performance of my model using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared score on the test set. Finally, i selected the best-performing model and fine-tuned its hyperparameters using techniques such as Grid Search and Random Search. then evaluated the final model on the test set .