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Rapidminer - Replacing missing value
 
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Replace the missing values in the excel file
Views: 8291 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: 2536 Evan Bossett
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".
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: 289 Ageng Rikhmawan
Seri Rapid Miner 9   Belajar Data Pre-processing 1   Bagaimana Menangani Missing Values
 
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Seri Rapid Miner 9 - Belajar Data Pre-processing 1 - Bagaimana Menangani Missing Values
Views: 32 Dian Sano
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: 1717 ironfrown
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: 3461 NIRMAL JOSE
Consuming REST APIs and Text Mining with RapidMiner
 
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Businesses today rely heavily on REST APIs to create and enrich their data sets, and to improve text mining model performance. Yet working with REST APIs in a data science workflow can be cumbersome and challenging. Plus creating topics that best describe natural text from chats or elsewhere adds insight, but with more complexity.
Views: 1007 RapidMiner, Inc.
aggregate - RapidMiner Data Mining
 
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Aggregate The Aggregate operator allows example sets to be restructured in many ways to summarise them in order to help understand the data better or to prepare for subsequent processing. The capabilities of this operator are similar to the SQL Group-By and Having clauses familiar from database queries. Topics covered Use default aggregation and default aggregation function Aggregration attributes Group by attributes Count all combinations Only distinct Ignore missing
Views: 299 Markus Hofmann
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: 11724 RapidMiner, Inc.
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: 6321 RapidMiner, Inc.
Pre-processing data dengan Rapidminer
 
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Vido ini menjelaskan bagaimana langkah-langkah menghilangkan missing value pada suatu dataset
Tips/Tricks Using Rapidminer - Balancing Data
 
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Sorry for the bad volume. Learn how to balance classes in RapidMiner.
Views: 4253 NeuralMarketTrends
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
Data Mining with RapidMiner - Discriminant Analysis (Thai)
 
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This video is about DiscriminantAnalysis
Views: 189 Damrongsak Naparat
RapidMiner Advanced Analytics Demonstration: Analyzing Fatality Data in Automobiles
 
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Introducing advanced analytics in RapidMiner through a product demonstration of RapidMiner Studio Professional.
Views: 4940 RapidMiner, Inc.
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 44119 Elsevier
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: 62715 Pallab Sanyal
Naive Bayes tutorial using RapidMiner
 
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vidio tutorial ini di bikin untuk memenuhi tugas data mining,
Views: 3076 febrianus pungky
RapidMiner Tutorial Basics (filtering and sorting)
 
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Data mining application RapidMiner tutorial basics "Filtering and Sorting" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/acoq2bqqiuz4zyo/Tutorial%20Basic%202.rmp?dl=0 www.rapidminer.com
Views: 1168 Evan Bossett
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: 1179 RapidMiner, Inc.
RapidMiner Stats (Part 1): Basics and Loading Data
 
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This is the beginning of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. This video briefly describes a data set to be used in the entire segment and shows how to read in a file in a CSV format and how to convert it into a RapidMiner data store. As this is the first video in the series, it also introduces some fundamental concepts of RapidMiner and the way you create analytic processes, manipulate operators and their parameters, open design and results views, and inspect the generated results. 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: 777 ironfrown
SAS Enterprise Miner Tip: Imputing Missing Values
 
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https://support.sas.com/edu/schedules.html?id=857&ctry=US Jeff Thompson, a statistical training specialist with SAS Education, provides an overview of the predictive modeling portion of the SAS training course "Applied Analytics Using SAS Enterprise Miner." Thompson also provides a tip on the imputation of missing values. To learn more about the SAS training course "Applied Analytics Using SAS Enterprise Miner," visit https://support.sas.com/edu/schedules.html?id=857&ctry=US
Views: 32585 SAS Software
Rapidminer 5.0 Video Tutorial #8 - Financial Time Series Data Exploration
 
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In this video I show the viewer how to use Rapid Miner's Time Series plugin to explore time series data. This is a prep for videos #9 and #10 that will teach the viewers how to make financial time series predictions.
Views: 17753 NeuralMarketTrends
Loop Operator in Rapidminer for processing Twitter Feeds
 
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Text Mining with Twitter
Views: 1718 Sean c
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: 10372 Ho Xiu Xian
RapidMiner: Setup and Project Repository
 
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This video explains how to setup RapidMiner Studio, one of the most popular data mining software. It covers getting the software, organizing the RapidMiner project repository, as well as, installing RapidMiner with some of the commonly used extensions. The links to data sets for the following lessons will be provided for each lesson, all of them can be found in the following location: * http://visanalytics.org/youtube-rsrc/rm-data/ As ore lessons are added, more data will be uploaded to this web directory. Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 847 ironfrown
RapidMiner Tutorial Basics (Changing Types and Roles)
 
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Data mining application RapidMiner tutorial basics "Changing Types and Roles" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/ovchlyzfxaw7lfy/Tutorial%20Basic%205.rmp?dl=0 www.rapidminer.com
Views: 778 Evan Bossett
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: 1134 ironfrown

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