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Algorithm Name PCA/T-SNE Description Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances Type Dimension Reduction

Algorithm Name Recommender system Description Help to define the relevant data for making a recommendation. Type Clustering

Algorithm Name Hierarchical clustering Description Splits clusters along a hierarchical tree to form a classification system. Can be used for Cluster loyalty-card customer Type Clustering

Algorithm Name Gaussian mixture model Description A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters) Type Clustering

Algorithm Name K-means clustering Description Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans) Type Clustering

Unsupervised learning In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns) You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you

Algorithm Gradient-boosting trees Description Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it. Type Regression Classification

Algorithm AdaBoost Description Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome Type Regression Classification

Algorithm Random forest Description The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction. Type Regression Classification

Algorithm Support vector machine Description Support Vector Machine, or SVM, is typically used for the classification task. SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver. Type Regression (not very common) Classification

Algorithm Naive Bayes Description The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event. Type Regression Classification

Algorithm Decision tree Description Highly interpretable classification or regression model that splits data-feature values into branches at decision nodes (e.g., if a feature is a color, each possible color becomes a new branch) until a final decision output is made Type Regression Classification

Algorithm Logistic regression Description Extension of linear regression that’s used for classification tasks. The output variable 3is binary (e.g., only black or white) rather than continuous (e.g., an infinite list of potential colors) Type Classification

Algorithm Linear regression Description Finds a way to correlate each feature to the output to help predict future values. Type Regression

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