By: Ajda Pretnar, May 18, 2019
Business Case Studies with Orange
At the latest workshop in Italy we taught the participants how to identify relevant business use cases and how to predict, which employees are most likely to resign in the future.
Orange Blog
By: AJDA, May 3, 2018
Data Mining Course at Higher School of Economics, Moscow
Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of Applied Statistics. This is a new Master’s course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry. It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.
By: AJDA, Nov 17, 2017
Data Mining for Business and Public Administration
We’ve been having a blast with recent Orange workshops. While Blaž was getting tanned in India, Anže and I went to the charming Liverpool to hold a session for business school professors on how to teach business with Orange. Related: Orange in Kolkata, India Obviously, when we say teach business, we mean how to do data mining for business, say predict churn or employee attrition, segment customers, find which items to recommend in an online store and track brand sentiment with text analysis.
By: AJDA, Jan 13, 2017
Data Preparation for Machine Learning
We’ve said it numerous times and we’re going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there’s no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.
By: SALVACARRION, Aug 19, 2016
Making recommendations
This is a guest blog from the Google Summer of Code project. Recommender systems are everywhere, we can find them on YouTube, Amazon, Netflix, iTunes,… This is because they are crucial component in a competitive retail services. How can I know what you may like if I have almost no information about you? The answer: taking Collaborative filtering (CF) approaches. Basically, this means to combine all the little knowledge we have about users and/or items in order to build a grid of knowledge with which we make recommendation.
By: AJDA, Jul 5, 2016
Rehaul of Text Mining Add-On
Google Summer of Code is progressing nicely and some major improvements are already live! Our students have been working hard and today we’re thanking Alexey for his work on Text Mining add-on. Two major tasks before the midterms were to introduce Twitter widget and rehaul Preprocess Text. Twitter widget was designed to be a part of our summer school program and it worked beautifully. We’ve introduced youngsters to the world of data mining through social networks and one of the most exciting things was to see whether we can predict the author from the tweet content.
By: AJDA, Apr 25, 2016
Association Rules in Orange
Orange is welcoming back one of its more exciting add-ons: Associate! Association rules can help the user quickly and simply discover the underlying relationships and connections between data instances. Yeah! The add-on currently has two widgets: one for Association Rules and the other for Frequent Itemsets. With Frequent Itemsets we first check frequency of items and itemsets in our transaction matrix. This tell us which items (products) and itemsets are the most frequent in our data, so it would make a lot of sense focusing on these products.