What is feature selection, and how does it impact the model’s performance?

Feature selection involves selecting only the most relevant variables for the model, removing redundant or irrelevant features. This can lead to improved performance by reducing overfitting, speeding up training, and making the model easier to interpret. For example,In a dataset predicting house prices, removing features like "house color" which doesn’t affect the price can improve model efficiency and accuracy.