This presentation introduces a word prediction application, how it was developed, and how to use it. It is the Capstone project for the data science specialization on Coursera, taught by John Hopkins University professors, in collaboration with Swiftkey.

In this report, an actual model and a word predictor are built. First, the proces of data retrieval, cleaning and tidying is repeated from the exploratory data analysis in a more efficient manner. Then, multiple models with different parameters are trained on a training set and tested on a validation set. Some accuracy of the best performing model was given up in favor of performance.

This exploratory analysis is meant to explore some twitter, blog and news corpora - retrieved via Coursera - to create a SwiftKey application. We want to get a first sense how the word counts are distributed, and what the most frequent words (unigrams), pairs (bigrams), triples (trigrams) and quatriples (quadgrams) are. This will serve us to develop an application that suggests, i.e. predicts, the next word while typing.

This is the proof of the fact that the martingale is a zero sum game with an honnest roulette. Written in Dutch.

This is the part II assignment for the sixth course - Statistical Inference - of the specialization Data Science on Coursera.org

This is the part I assignment for the sixth course - Statistical Inference - of the specialization Data Science on Coursera.org

This is the second assignment for the fifth course - Reproducible Research - of the specialization Data Science on Coursera.org

This is the first assignment for the fifth course - Reproducible Research - of the specialization Data Science on Coursera.org

This is the second assignment for the fourth course - Exploratory Data Analysis - of the specialization Data Science on Coursera.org

This is the first assignment for the fourth course - Exploratory Data Analysis - of the specialization Data Science on Coursera.org