In this section, we will implement the various ML algorithms, check their precision score, and monitor their learning curve. There is a total of six ML algorithms that will be used to identify which one is the best suited for our application.
In this section, we will be implementing logistic regression, K-nearest neighbor, decision tree, random forest, Adaboost, and gradient descent. In order to implement this, we will be using the helper class that we built earlier. You can take a look at the code snippet given in the following screenshot:
Figure 3.68: Code snippet for performing training using various ML classifiers
We have already generated a precision score for all the classifiers. We can see random forest and gradient-boosting classifiers with great precision. However, we have still not checked their learning curve. First, we will check their learning curve and then conclude whether any classifier has been facing the over...