Learn how a random forest model can help us to predict the probability of a goal, with applications ranging from performance appraisal to match-fixing detection.
A/B tests help you decide between two options, A and B. Read this step-by-step guide on conducting your own A/B test to make the right decisions.
In the 2nd part of our tutorial on artificial neural networks, we cover 3 techniques to improve prediction accuracy: distortion, mini-batch gradient descent and dropout.
Do you know what gives red and white wine their colors? Use k-NN to discover the chemical make-up that defines typical types of wines, as well as to detect atypical ones.
Learn how random forests, an ensemble of decision trees, can help predict where and when a crime will happen in San Francisco, California.
Decision trees can be used to identify customer profiles or to predict who will resign. Using the Titanic dataset, learn about its advantages and pitfalls, as well as better alternatives.
You are exploring the nutritional content of food. How can food items be differentiated? How might they be classified? PCA derives underlying variables that help you slice your data for these insights.
Using weapons trade data, we map out who's against who in the complex arena of international politics.
You own a store. How do you discover purchasing patterns, such as which items tend to be bought together? Knowing this can improve your product placement and advertisement.
Modern smartphone apps allow you to recognize handwriting and convert them into typed words. We look at how we can train our own neural network algorithm to do this.
You have employees. But who should you pick to lead them? Learn how to predict leadership potential using multiple sources of personnel data, as well as pitfalls to watch out for.
You have customers. But how should you categorize them to target sales? How many of such categories exist? To answer these questions, we can use cluster analysis.
Outliers can be detected by algorithms used for predictions. To illustrate, we use the k-nearest neighbor (kNN) clustering algorithm.
Latent Dirichlet allocation (LDA) is a technique that automatically discovers topics that a set of documents contain. It is used to analyze large volumes of text efficiently. To find out how it works, check out this tutorial.