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Rapidminer - Replacing missing value
 
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Replace the missing values in the excel file
Views: 8023 Mersal coc Clashers
RapidMiner Tutorial Data Handling (Handle Missing Values)
 
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Data mining application RapidMiner tutorial data handling "Handle Missing Values" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/7ch4yo60lwplnam/Tutorial%20DH1.rmp?dl=0 www.rapidminer.com
Views: 2277 Evan Bossett
Pre-processing data dengan Rapidminer
 
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Vido ini menjelaskan bagaimana langkah-langkah menghilangkan missing value pada suatu dataset
Data Cleaning Replace Missing Value with Average Values
 
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Data Mining Preprocessing Data Cleaning Replace Missing Value with Average Values in Rapidminer
Views: 201 Ageng Rikhmawan
Different preprocessing techniques on a given dataset using Rapid Miner.
 
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This video includes "Reading data", "Analyzing input", "Handling Missing values", "Discretization(binning)", "Normalization", "Sampling", "Correlation Determination".
How to process text files with RapidMiner
 
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In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 33952 Alba Madriz
Loop Operator in Rapidminer for processing Twitter Feeds
 
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Text Mining with Twitter
Views: 1641 Sean c
DATA PRE-PROCESSING using rapid miner 7.2.003
 
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Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors
Views: 3014 NIRMAL JOSE
RapidMiner Advanced Analytics Demonstration: Predicting Survival of the Titanic Accident
 
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Introducing advanced analytics in RapidMiner through a product demonstration of RapidMiner Studio Professional.
Views: 17420 RapidMiner, Inc.
RapidMiner Stats (Part 4): Working with Aggregates
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to use an aggregate operator to derive various statistics, such as mean, median, mode or standard deviation from a data sample, for both numerical and nominal attributes. It is explained how to group aggregates by a nominal attribute and thus produce the relevant statistics for each of the nominal attribute levels (possible values). Most importantly, the aggregate operator return all statistics in the form of data examples, which means they can be used by other operators as input to further processing. As several aggregates are produced in the course of this video, it is also shown how to create many copies of the same data set using a multiply operator. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1023 ironfrown
Rapidminer 5.0 Video Tutorial #13 - Parameter Optimization
 
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In this Rapidminer Video Tutorial I show the user how to use the Parameter Optimization operator to optimize your trained data. The example shows how Rapidminer iterates the learning rate and momentum for a Neural Net Operator to increase the performance of the trained data set. Video #14 will be about web mining financial text data.
Views: 19653 NeuralMarketTrends
10 Data Preparation
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 5412 RapidMiner, Inc.
Tips/Tricks Using Rapidminer - Balancing Data
 
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Sorry for the bad volume. Learn how to balance classes in RapidMiner.
Views: 4075 NeuralMarketTrends
CS2041 - RapidMiner for Clustering
 
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NTU WKWSCI CS2041 Project by Servers & Swag (Tom Samuelsson, Ho Xiu Xian, Scott Lai, Alex Goh, Benjamin Tan)
Views: 9824 Ho Xiu Xian
Pivoting in RapidMiner
 
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A short tutorial on how to pivot data in RapidMiner - an example of transaction data
Views: 746 Prof Pahor
How to use the new RapidMiner Time Series Extension ver 0.2.1
 
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Version 0.2.1 of the popular Time Series Extension for RapidMiner just got a lot better. Hear RapidMiner Researcher Fabian Temme explain the new features: Five new operators: Extract Aggregates, Replace Missing Values (Series), Forecast Validation, Windowing, Process Windows Plus new additions to the Time Series Extension samples folder and three new template process to work with the new operators in this extension (Create Model for Gas Prices, Investigate Gas Prices Data, and Forecast Validation of ARIMA Model for Lake Huron).
Views: 787 RapidMiner, Inc.
Different preprocessing techniques on a given dataset using Rapid Miner.
 
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To read and analyze data handle missing values using normalization,discretization,sampling and correlation determination
Exporting XLSX
 
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Como exportar una hoja de Datos de Rapidminer a Excel
Views: 1145 dataminingincae
04 Importing Data in RapidMiner Studio
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 9713 RapidMiner, Inc.
Naive Bayes tutorial using RapidMiner
 
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vidio tutorial ini di bikin untuk memenuhi tugas data mining,
Views: 2730 febrianus pungky
RapidMiner Classification (Part 5): Cross Validation
 
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In this lesson on classification, we introduce the cross-validation method of model evaluation in RapidMiner Studio. Cross-validation ensures a much more realistic view of the model performance. This is achieved by testing the model k times and each time the available data is split into k parts or folds, where k-1 folds are then used for model training and the remaining 1 fold is used for its validation. Subsequently the model average performance is returned. The video also mentions the Leave-One-Out method of validation where a single observation is used for testing and the rest of data is used to build the model, which is not suitable for large data sets, such as 3000 workers compensation claims used in this case of predicting the possibility of claim subrogation, i.e. recovery of insurance payout due to the claim irregularities. At the end of the lessons we explore different ways of improving the model accuracy, first by varying the k-NN model parameters (the number of neighbors "k") and then by replacing the k-NN model with the Gradient Boosted Trees. This video is best to watch as part of the series on classification: * https://www.youtube.com/watch?v=YXb1wZO-Evw&list=PLTNk8YAaQSkqI8v_NveKEOvdgm4OWYqCl&index=10 The data for this lesson appeared in a number of tutorials for text mining. However, in this video it will be used to predict various aspects of workers compensation claims based on structured variables only. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/workcomp.csv * http://visanalytics.org/youtube-rsrc/rm-data/workcompscore.csv The original source of the data does not seem to be available online anymore. Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 1500 ironfrown
Join - Operator - RapidMiner - Data Mining
 
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The Join operator. The Join operator joins two example sets together in a variety of ways. Its parameters allow example sets to be enriched with new attributes using data from other sources, filtered to include only examples of interest and combined with other example sets to form subsets or supersets. Topics covered Inner join Left join Right join Outer join The Id attribute parameter Double attributes
Views: 82 Markus Hofmann
RapidMiner Tutorial Data Handling (Normalization and Outlier Detection)
 
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Data mining application RapidMiner tutorial data handling "Normalization and Outlier Detection" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/obqxh61ea2ud6tk/Tutorial%20DH2.rmp?dl=0 www.rapidminer.com
Views: 1900 Evan Bossett
16 Finding the Right Model
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 3645 RapidMiner, Inc.
5. Basic RapidMiner : Data blending and cleansing
 
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Basic RapidMiner : Data blending and cleansing
Views: 139 pornthep rojanavasu
Data Preprocessing
 
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Data Preprocessing using Rapidminer The steps undertaken are : 1. Handling missing values 2. Binning 3. Sampling 4. Normalization 5. Correlation Determination
RapidMiner Tutorial - Data Transformations  (Data Mining and Predictive Analytics System)
 
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A tutorial discussing data transformations with RapidMiner, an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 6497 Predictive Analytics
012 Data reduction in RapidMiner
 
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Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 1737 Tukang Leding
RapidMiner Tutorial Data Handling (Pivoting and Renaming)
 
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Data mining application RapidMiner tutorial data handling "Pivoting and Renaming" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/h2y080lxuldahpu/Tutorial%20DH3.rmp?dl=0 www.rapidminer.com
Views: 573 Evan Bossett
RapidMiner Classification (Part 1): Introduction and Business Case
 
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This video starts a Segment on Data Mining / Classification in a series on RapidMiner Studio. The video gives a short (re-)introduction of RapidMiner Studio, it presents a Workers Compensation business case, and shows how to load and explore the relevant data for further processing, modelling and visualization. This video is best to watch as part of the series on classification: * https://www.youtube.com/watch?v=YXb1wZO-Evw&list=PLTNk8YAaQSkqI8v_NveKEOvdgm4OWYqCl&index=10 The data for this lesson appeared in a number of tutorials for text mining. However, in this video it will be used to predict various aspects of workers compensation claims based on structured variables only. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/workcomp.csv * http://visanalytics.org/youtube-rsrc/rm-data/workcompscore.csv The original source of the data does not seem to be available online anymore. Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 2198 ironfrown
RapidMiner Stats (Part 2): Simple Data Exploration
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to use RapidMiner "Statistics" tab to explore attributes of a loaded data set. It briefly explains different attribute types, such as numeric, polynomial and binomial, and then shows how to create 2D and 3D scatter plots of numeric attributes. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1488 ironfrown
1. Introduction to RapidMiner Studio
 
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This video (1) provides a brief introduction to the RapidMiner Studio 6.0 interface, (2) shows how to import datasets into RapidMiner, and (3) shows how to create, run and save a RapidMiner process.
Views: 59630 Pallab Sanyal
Data Mining with RapidMiner - Discriminant Analysis (Thai)
 
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This video is about DiscriminantAnalysis
Views: 145 Damrongsak Naparat