A critical step in maximizing the performance of machine learning models is hyperparameter tuning. The accuracy, speed, and generalization capacity of the model are greatly impacted by hyperparameters, which are set prior to training, in contrast to model parameters, which are learned during training. Finding the ideal hyperparameter values through a methodical process that balances computational economy and performance is essential to effective tuning. Data Science Course in Pune