Learn Data Science using Python

Another post starts with you beautiful people! I hope after reading my previous post about  Linear and Logistic Regression   your confidence level is up and you are now ready to move one step ahead in Machine Learning arena. In this post we will be going over Decision Trees and Random Forests . In order for you to understand this exercise completely there is some required reading. I suggests you to please read following blog post before going further- A Must Read! After reading the blog post you should have a basic layman's (or laywoman!) understanding of how decision trees and random forests work.  A quick intro is as below- Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features . For instance, in the example below, decision trees learn from data to approximate a sin...