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.
Algobeans
Layman Tutorials in Machine Learning
Latest posts
Mapping Global Cuisine with Word Embeddings
How can we systemically identify analogous food items across cultures? Learn how using word embeddings.
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.
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.
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.
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.
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 most pervasive analysis techniques because of its inextricable relation to time - we are always interested to foretell the future.
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.
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.
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.