In this blog, we will be predicting NBA winners with Decision Trees and Random Forests in Scikit-learn.The National Basketball Association (NBA) is the major men’s professional… Read More »Predicting NBA winners with Decision Trees and Random Forests in Scikit-learn
Bitcoin has been in news quite a bit lately with the price soaring. It was named the top performing currency four of the last five… Read More »Modeling Bitcoin’s Market Capitalization
This countvectorizer sklearn example is from Pycon Dublin 2016. For further information please visit this link. The dataset is from UCI. In [2]: messages = [line.rstrip() for line… Read More »Countvectorizer sklearn example
Principal Component Analysis (PCA) is an orthogonal linear transformation that turns a set of possibly correlated variables into a new set of variables that are… Read More »Principal Component Analysis in scikit-learn
Naïve Bayes is a simple but powerful classifier based on a probabilistic model derived from the Bayes’ theorem. Basically it determines the probability that an… Read More »Naiive Bayes in scikit-learn
Decision trees are very simple yet powerful supervised learning methods, which constructs a decision tree model, which will be used to make predictions. The main… Read More »Decision Trees in scikit-learn
continued from part 1 In [8]: print_faces(faces.images, faces.target, 400) Training a Support Vector Machine Support Vector Classifier (SVC) will be used for classification The SVC implementation… Read More »Support Vector Machine in scikit-learn- part 2
Support Vector Machines has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the… Read More »Support Vector Machine in scikit-learn – part 1
