The best approach

The classifier model that we will be generating in this approach should give us the best possible accuracy. We have already discussed this approach. If you are new to ensemble ML models, then let me give you a basic intuitive idea behind it. In layman's terms, ensemble ML models basically use a combination of various ML algorithms. What is the benefit of combining various ML models together? Well, we know there is no single classifier that can perfectly classify all the samples, so if we combine more than one classifier, then we can get more accuracy because the problem with one classifier can be overcome by another classifier. Due to this reason, we will use a voting classifier that is a type of ensemble classifier.

Implementing the best approach

As you know, we use grid search and voting classifier APIs to implement the best approach. As discussed, first, we will use grid search to obtain the best possible hyperparameters and then use the voting classifier API. The step...