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Random Forest Tutorial: Predicting Crime in San Francisco

Learn how random forests, an ensemble of decision trees, can help predict where and when a crime will happen in San Francisco, California.

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k-Nearest Neighbors & Anomaly Detection Tutorial

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.

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Artificial Neural Networks Introduction (Part II)

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.

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Time Series Analysis with Generalized Additive Models

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...

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Layman’s Guide to A/B Testing

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.

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Self-Organizing Maps Tutorial

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.

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Kernel Density Plots

Visualizing soccer data, we identify hot spots where most shots occur, shot preferences across players, and comparisons between different teams like Liverpool and Manchester United.

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Random Forest Tutorial: Predicting Goals in Soccer

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.

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Mapping Global Cuisine with Word Embeddings

How can we systemically identify analogous food items across cultures? Learn how using word embeddings.

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Gain an Edge in Lottery with Math

Is it possible to beat lottery? In this post, we conduct data analysis of Toto, a Singapore lottery with twice-weekly draws.

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