The course "Advanced Data Preprocessing for Data Mining with RapidMiner" is a compact two day introduction into the most important preprocessing techniques for Data Mining. It also provides a practical guide for the data preprocessing with the Data Mining software RapidMiner. The generation of successful models is often only possible after an appropriate preprocessing of the data set and hand. In almost all cases the quality of the prediction models can be further increased by a good preprocessing. This seminar presents techniques for the detection and removal of outliers, data cleansing, the selection of good indicators (feature selection), the construction of new, latent variables (feature construction and extraction) and numerous other automated methods for the optimization of the Data Mining results. Due to a high number of practical exercises, the participants will be able to transfer the gained knowledge to own data mining problems and solve them quickly and easily.

Details

• Course ID: 1302
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: users, analysts, developers, administrators
• Previous knowledge: basic knowledge of RapidMiner and Data Mining (otherwise we recommend the visit of one of our introduction courses)
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course is a compact introduction into the foundations of data mining and into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Introduction into basic preprocessing methods like normalization, standardization, joins etc.
  • Detection and removal of outliers and other data cleansing methods
  • Missing value replenishment
  • Methods for dimensionality reduction and feature selection, including genetic algorithms and greedy heuristics
  • Feature weighting and weight based selection
  • Evolutionary feature construction
  • Validation of preprocessing and preprocessing models
  • Logging the results and the success of preprocessing
  Extensive exercises on different data sets will be performed for all topics.

Prices

Value added tax (VAT) may have to be added to these prices.