This section contains tutorials and tips on the machine learning cycle, from data cleansing, scaling, encoding, features engineering, training and testing. Reproducible code is used where possible. Any feedback is always welcome so please do get in touch.
80% in Kaggle’s Titanic competition in 50 lines of R code
A simple step-by-step guide to achieving over 80% accuracy in Kaggle’s Titanic competition in just 50 lines of R code.
Ethics of machine learning in education
Avoiding bias in machine learning in education.
One-hot encoding in R: three simple methods
Three quick and simple methods to apply one-hot encoding in R.
Standardisation & Normalisation
Eliminate units of measurement and boost machine learning algorithms.
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