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 […]

Naiive Bayes in scikit-learn

Posted on 1 CommentPosted in Machine Learning, 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 instance belongs to a class based on each of the feature value probabilities. One of the most successful applications of Naïve Bayes has been within the field of Natural Language […]

Decision Trees in scikit-learn

Decision Trees in scikit-learn

Posted on 6 CommentsPosted in Machine Learning, 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 advantage of this model is that a human being can easily understand and reproduce the sequence of decisions (especially if the number of attributes is small) taken to predict the […]

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 […]