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.
Visualize large datasets and identify potential clusters with this special breed of neural networks that uses neurons to learn the intrinsic shape of your data.
Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time - we are always interested to foretell the future.
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.
Outliers can be detected by algorithms used for predictions. To illustrate, we use the k-nearest neighbor (kNN) clustering algorithm.