Nicholas is currently finishing an undergraduate course in commerce and economics in Melbourne. He has majored in actuarial studies, econometrics, and finance, and is tutoring first-year statistics. Nicholas is interested in probability networks, randomness, Bayesian statistics, and Brownian motion. He enjoys reading, good coffee, and juggling.
Project: The Probability of a Spike in Wikipedia Page Hits for a Given Individual
The goal of this project is to create a classifier that determines the probability of a spike in a Wikipedia page’s popularity. Popularity is synonymous with the number of page hits per week, and a spike in popularity is defined as a function of the historic volatility of the page’s popularity.
The motivation for this project is based around an application of the intersection between machine learning and actuarial science to the real world. Predicting increases in Wikipedia page popularity is an interesting way to pursue this. Furthermore, the study of virality is a growing and exciting area.
The approach shall be to build a model based on an appropriately large dataset that will draw conclusions from information taken from the page about increases in popularity.
The implications of this project are fairly broad. Using actuarial techniques, graph theory, and machine learning, the project should add to the understanding of what generates a spike in popularity, and the probabilities surrounding such an event. Ideally, this can be extended beyond Wikipedia page hits and into a broader study of popularity as a social phenomenon.
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