Among many machine learning techniques, RandomForest is one of the most widely used ensemble supervised algorithm for both classification and regression tasks. Yes, it works smoothly for both categorical and continuous prediction labels.
Before diving into the code directly, let’s first discuss the theoretical aspects of RandomForest. It is an ensemble method (i.e. a collection or group of various other models) which is highly suitable for almost any kind of dataset.
RandomForest uses a collection of Decision Trees to predict an outcome. A Decision Tree Classifier contains a root node, followed by branch nodes, and end up with the leaf nodes as shown in the diagram below. 
Intuitively, a single decision tree on a large dataset would perform really poorly as there are many different ways an outcome can be reached. This is where RandomForest comes in. The principle behind RandomForest is that it takes down a part of the dataset (also called bootstrapping) and forms a decision tree by further taking a random subset of the features (or variables) only. Such decision trees are formed in large numbers, each time with a bootstrapped dataset and a random subset of features. Once all the trees are tuned and ready, prediction is made by taking out the average of outcomes from each tree.
This method surprisingly works for almost all kinds of dataset. The reason behind this is that every tree overfits on its own bootstrapped dataset (though learns something about that portion of the data), and when outputs from the trees are averaged, the final result does not come from an overfitted model. Hence, this method builds a highly generalizable machine learning model.
For evaluating the model, a technique called out-of-bag is used. Each time a decision tree is trained on a bootstrapped dataset, it does not see all the data in that set. So, the data that are not seen by the tree can be used to evaluate the performance for the tree. These performances can be added for all the trees and a final accuracy metric can be evaluated.
This concludes a really quick overview of how RandomForest works under-the-hood. Now, let’s get into implementing our own model using a famous machine learning library in Python called scikit-learn. The code for implementing a RandomForest model is discussed in the second part of this post: RandomForest - Part 2