Predictive Analytics and Modeling using CART Algorithm

Learn Predictive Analytics Model using CART algorithm from a case study
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Learn about CART algorithm using data
How CART model can be used to make predictions on unseen data
Understand Predictive Analytics
Understand how to use predictive analytics tools to solve real time business problems


  • Prior knowledge of Quantitative Methods will be useful


Classification and Regression Trees or CART for short is a term refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems. CART is a predictive algorithm used in Machine learning and it explains how the target variable’s values can be predicted based on other matters. It is a decision tree where each fork is split into a predictor variable and each node has a prediction for the target variable at the end. Classically, this algorithm is referred to as decision trees, but on some platforms like R they are referred to by the more modern term CART. The CART algorithm provides a foundation for important algorithms like bagged decision trees, random forest and boosted decision trees.

As the name suggests, CART (Classification and Regression Trees) can be used for both classification and regression problems. The difference lies in the target variable. That is, With classification, we attempt to predict a class label. In other words, classification is used for problems where the output (target variable) takes a finite set of values, e.g., whether it will rain tomorrow or not. Meanwhile, regression is used to predict a numerical label. This means your output can take an infinite set of values, e.g., a house price.

Decision Trees are an important type of algorithm for predictive modeling machine learning. The classical decision tree algorithms have been around for decades and modern variations like random forest are among the most powerful techniques available. In this post you will discover the humble decision tree algorithm known by it’s more modern name CART which stands for Classification And Regression Trees. The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. This course includes learning decision tree modeling which are used by data scientists or people who inspire to be the data scientist.

The Decision Tree Algorithm is one of the popular supervised type machine learning algorithms that is used for classifications. This algorithm generates the outcome as the optimized result based upon the tree structure with the conditions or rules. The decision tree algorithm associated with three major components as Decision Nodes, Design Links, and Decision Leaves. It operates with the Splitting, pruning, and tree selection process. It supports both numerical and categorical data to construct the decision tree. Decision tree algorithms are efficient for large data set with less time complexity. This Algorithm is mostly used in customer segmentation and marketing strategy implementation in the business.

Who this course is for:

  • Anyone who wants to learn about data and analytics


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