Predicting NBA winners with Decision Trees and Random Forests in Scikit-learn

Predicting NBA winners with Decision Trees and Random Forests in Scikit-learn

Posted on Leave a commentPosted in Machine Learning, Predictive Analysis, scikit-learn

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 basketball league in North America and is widely considered to be the premier men’s professional basketball league in the world. It has 30 teams (29 in the United States and […]

countvectorizer sklearn example

Countvectorizer sklearn example

Posted on 1 CommentPosted in Data Analysis Resources, Machine Learning, scikit-learn

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 in open(‘smsspamcollection/SMSSpamCollection’)] In [3]: print (len(messages)) 5574 In [5]: for num,message in enumerate(messages[:10]): print(num,message) print (‘\n’) 0 ham Go until jurong point, crazy.. Available only in bugis n great world la e […]

Support Vector Machine in scikit-learn

Support Vector Machine in scikit-learn- part 2

Posted on Leave a commentPosted in Machine Learning, 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 has different important parameters; probably the most relevant is kernel, which defines the kernel function to be used in our classifier In [10]: from sklearn.svm import SVC svc_1 = SVC(kernel=’linear’) print […]

Support Vector Machine in scikit-learn

Support Vector Machine in scikit-learn – part 1

Posted on Leave a commentPosted in Machine Learning, scikit-learn

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 greatest advantages of Support Vector Machines is that they are very effective when working on high-dimensional spaces, that is, on problems which have a lot of features to learn from. […]