Orange – Data Mining Fruitful & Fun

Mentors: Student: matevzkren

<p><strong>Rule induction</strong> from examples is recognised as a fundamental component of many machine learning systems. We propose to implement supervised rule induction algorithms and rule-based classification methods, established on a more general framework of replaceable individual components that can be fine-tuned to specific needs. For this purpose, the separate-and-conquer (also covering) strategy will be purposed.</p> <p>The addition to the Orange software suite should benefit both novice and expert users looking to advance their knowledge in a particular area of study through a better understanding of given predictions and underlying argumentation.</p>

Mentors: Student: Primož Godec

<p>Our idea is to implement several educational widgets which will be connected in Orange add-on. This widgets will be used for a demonstration of several key data mining and machine learning procedures in a classroom. The widgets will be useful for beginners to understand the inner working of key algorithms in the data mining and for teachers to be able to visually explain various methods.</p>

Mentors: Student: sstanovnik

<p>This project is concerned with migrating Orange.data.Table to pandas. This follows the current trend of the programming community of using pandas as the underlying data manipulation API for statistics and machine learning in Python. It is an API modernization and partial redesign and pays off a lot of technical debt to enable easier extensibility, more flexibility, and less maintenance.</p>

Mentors: Student: Salva Carrión

<p>Recommender systems are a crucial component in competitive retail services such Amazon, Netflix or Google. This success is partly due to Collaborative filtering (CF) approaches, which use known user ratings of items to predict user preferences in item selection. Here, an efficient implementation of an add-on for Orange3 is proposed in order to include support for scalable collaborative filtering solutions applied in large recommender systems.</p>

Mentors: Student: Alexey

<p>Extend existing add-on by adding/improving widgets.</p>