In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix . By the end of this course, you will be able to effectively summarize your data, visualize your data, detect and eliminate missing values, predict futures outcomes using analytical techniques described above, construct a confusion matrix, import and export a data.

One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item. You can perform either classification or regression tasks here. For example, identifying fraudulent transactions using credit cards would be a classification task while forecasting prices of stock would be regression task. Decision tree technique is used to detect the criteria for dividing individual items of a group into n predetermined classes (Often, n 2 represents a balanced tree, which means a largest of two child nodes for each parent node.)

I developed what I thought was an extremely clever method for detecting "bad" training instances. Each instance was scored, and those with the lowest scores could be removed before running C4.5 to build a decision tree with the remainder. I ran an experiment in which I removed the bottom 10 percent of the instances in a University of California, Irvine (UCI) data set. The resulting tree was smaller and more accurate (as measured by 10-fold CV) than the tree built on the full data set. Then I removed the bottom 20 percent of the instances and got a tree that was smaller than the last one and just as accurate.

When it comes to today's machine learning algorithms, they are being used in a variety of ways as well as in different fields. Healthcare, businesses, research are just a few examples where artificial intelligence is being applied to solve problems that either humans could not do by themselves or would take massive amounts of time to solve. However, everything in life has its strengths and weaknesses; modern machine learning algorithms are unfortunately no exception to this rule. Earlier this year, an article posted on the Elite Data Science website focused on several types of today's modern machine learning algorithms as well as their strengths and weakness. Basically, there is no super algorithm that can solve every problem and that it would be an innovative idea to try a variety of algorithms to resolve the problem at hand.

This approach to predictive analytics applications can be illustrated by an example. Let's consider an e-commerce company that wants to boost its profits by growing sales to existing customers. The objectives might be to increase both the number of items bought by individual customers and the average amount they spend overall in purchase transactions. A typical strategy to accomplish those goals involves using a recommendation engine to try to influence customers to add items to their online cart as they shop. There are a variety of different analytics methods that the online retailer can incorporate into its recommendation engine to assign similar customers to groups so the engine can suggest products that they might be inclined to buy.

The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree allowing you to easily present your visitors yes/no type questions and walk them down your decision tree. The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree (DT). Easily create your own trees with the easy to use DT editor. The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree (DT). Easily create your own trees with the easy to use DT editor.

This post is part five of a series of posts examining the features of the Redis-ML module. The first post in the series can be found here. The sample code included in this post requires several Python libraries and a Redis instance with the Redis-ML module loaded. Detailed setup instructions for the runtime environment are provided in both part one and part two of the series. Decision trees are a predictive model used for classification and regression problems in machine learning.

Click to learn more about author Alejandro Correa Bahnsen. There are a variety of Machine Learning algorithms, and each has its own strengths and weaknesses. In this second article in a series on Machine Learning algorithms, I introduce Random Forests, a supervised algorithm used for classification and regression. If you missed my Introduction to Machine Learning and Decision Trees, I encourage you to read that article first, as it provides a foundation that I'm building on. Before we dig into Random Forests, you must first understand the concept of an ensemble-learning model.

Decision Tree learning is used to approximate discrete valued target functions, in which the learned function is approximated by Decision Tree. To imagine, think of decision tree as if or else rules where each if-else condition leads to certain answer at the end. You might have seen many online games which asks several question and lead to something that you would have thought at the end. A classic famous example where decision tree is used is known as Play Tennis. If the outlook is sunny and humidity is normal, then yes, you may play tennis.

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. As the name goes, it uses a tree-like model of decisions. Though a commonly used tool in data mining for deriving a strategy to reach a particular goal, its also widely used in machine learning, which will be the main focus of this article. For this let's consider a very basic example that uses titanic data set for predicting whether a passenger will survive or not.