Home
Search results “Introduction to data mining with rattle”
Rattle - Data Mining in R
 
25:47
Overview of using Rattle - a GUI data mining tool in R. Overview covers some of the basic operations that can be performed in Rattle such as loading data, exploring the data and applying some of the data mining algorithms on the data - all this without actually having to type any R code
Views: 34499 Melvin L
Rattle for Data Mining - Using R without programming (CRAN)
 
17:28
www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 41828 Learn Analytics
Doing predictive modeling using R - Rattle (Togaware)
 
02:11:19
This session covers equivalent of all SAS procedures using free software - R Rattle. Hypothesis testing, Linear and Logistic regression, Cluster Analysis. Introduction to Random Forests, SVM, Boosting etc. www.learnanalytics.in
Views: 25592 Learn Analytics
Datenanalyse/Data Mining mit Rattle
 
11:27
Datenanalyse mit Rattle. Rattle ist eine R-Erweiterung und bietet eine Oberfläche zur Datenanalyse/Data Mining an. Dieses Video basiert auf dem Buch "Data Mining with Rattle and R" (http://www.r-statistik.de/Literatur/literatur.html#Rattle). Zur Verfügung gestellt von Günter Faes (http://www.faes.de/) über das Ad-Oculos-Projekt (http://ad-oculos.faes.de/).
Views: 889 r-statistik
Data Mining with Rattle:Clustering  for beginners
 
08:45
PS: minute 04:06 on commencera PAR !! je me suis trompée :D
Views: 2031 Fatma Karoui
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
36:36
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 49854 edureka!
Data Mining Tool:Rattle R GUI
 
23:27
Link to download R Console: https://cran.r-project.org/
Views: 2945 Chandrakala Badaga
Tationem: Intro to Data Mining
 
10:46
A brief video describing data mining, more from the business intelligence perspective.
Views: 50 Tationem
Togaware Rattle -- Quick Intro
 
01:01
A one minute trip through Togaware Rattle
Views: 211 Math4IQB
Data Mining and Benford's Law analysis in R with Rattle package
 
09:16
Distribution analysis of first significant digits in data to discover suspected value in accounting process.
Views: 1462 Giuseppe Caferra
R Programming, Data Mining
 
01:13:15
R Programming, Data Mining
Views: 496 ScholarsPro
KEEL Data mining tool demo
 
34:02
KEEL Data minig tool Demo of installation and Working
Views: 3667 Manukumar K J
Rattle Tutorial - How to Open The Sample Weather Dataset in Rattle
 
01:54
This is a quick tutorial on how to open the sample weather.csv dataset in Rattle. This weather dataset is very helpful in learning basic R and Data Mining concepts from books and guides etc. If you don't have rattle make sure you get it by following the official set-up guide here: http://rattle.togaware.com/rattle-install-mac.html (For Mac) Please drop a comment if you want more tutorial in R, Rattle or Data mining and the required area.
Views: 1467 Spellogram
Introduction to Data Mining: Data Attributes (Part 2)
 
03:47
This video is part three of the introduction to the data mining vocabulary. Explaining important attribute classes -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Ljk0 See what our past attendees are saying here: https://hubs.ly/H0f8L-10 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 6996 Data Science Dojo
Google Analytics Data Mining with R (includes 3 Real Applications)
 
53:31
R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 28220 Tatvic Analytics
Data Mining with R & RStudio - KMeans Clustering and Visualization
 
05:14
Simple overview of data mining with R and RStudio.
Views: 2945 Gaurav Jetley
Introduction to R for Data Mining
 
01:00:34
Introduction to R for Data Mining
Views: 148 Timothy Kipkirui
Building a predictive model with R and Rattle
 
16:58
In this video I see exactly how to build a predictive model, test it and then improve upon it with R and Rattle. This way you can take a model based on current data and make it several percentage points better in accuracy quickly and easily. After all we want our predictive models to have the highest accuracy and levels of confidence possible. Example I use this video is from the University of California at Irving. The data set is there bike sharing data set which is publicly available to anyone through their website. If you want to see how to load this data set and do a correlation analysis or similar I have some other videos you should go see same channel. This specific video we will take the initial model from a 71% to a 77% accuracy or level of confidence. The business world that is a huge difference and that could easily mean the difference between a profit and a loss. Just ask any executive about the importance of a six point spread or what would happen if they could realize a 6% tangible gain based on a predictive analysis you are going to do for them. Predictive analytics is the wave of the future. Building predictive models as fast and accurately as possible through applications like Rattle and R is becoming super important. The best part is this required absolutely no coding or programming. I hope you found this interesting and beneficial. Please take the time to subscribe and like. Also be sure and leave me a comment I love to hear from my viewers and subscribers. Thanks again and have a great day.
Views: 1322 Tech Know How
Optimal Decision Tree with Rattle
 
09:04
Learn an easy way to build a decision tree with Rattle
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
32:10
Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
04:51
Part two of our introduction to similarity and dissimilarity, we discuss euclidean distance and cosine similarity. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 14342 Data Science Dojo
Finding the Gold in Your Data: An Overview of Data Mining
 
01:00:49
David Dickey presentation at SAS Global Forum 2013
Views: 3406 SAS Software
Basic Data Mining
 
12:43
A Guide to ShareScope's Data Mining (stock-screening) facility
Views: 2360 ShareScope | SharePad
Data Mining Software in Healthcare
 
11:58
This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4054 Joshua White
LASI14 Workshop: Introduction to Data Mining for Educational Researchers pt1 June 30, 2014
 
01:19:05
Learning Analytics Summer Institute (LASI 2014) June 30, 2014 Workshop: Introduction to Data Mining for Educational Researchers, pt1 Christopher Brooks (University of Michigan), Zach Pardos (UC Berkeley), Vitomir Kovanovic (Simon Fraser University), Srecko Joksimovic (Simon Fraser University) http://solaresearch.org/conferences/lasi/lasi2014/lasi-2014-program-monday/ https://sites.google.com/a/umich.edu/lak-2014-tutorial-introduction-to-data-mining-for-educational-researchers/lasi-2014
Data Integration UAS --- Tutorial Pentaho & Data Mining
 
36:52
I Kadek Putrawan-----13101167 Agus Putra Utama Yasa-----13101292 Ekky Agustana-----13101325
Views: 309 deks putrawan
Rattle R Gui  Tabs
 
00:51
Rattle is a Gui written as a data mining and training tool for the R statistical programming language. Rattle is used by government departments, not for profits, and within the business community. Rattle is an open source project, and is free available from http://www.togaware.com . Rattle is currently used in business, scientific, law enforcement, defense and environmental areas.
Views: 1419 OZg3n1u5
R and data mining
 
02:03
Short demo for using R in data mining.
Views: 166 Li Wang
Overview of Open Source Data Mining Tools: AWS, Hadoop, Apache Spark, Clusters, NoSQL
 
01:43:46
Adapted from Openwest and Intermountain Big Data conference talks about NoSql, Java, Machine Learning, Python, Data Mining and the myriad tools you need to know exist. Subject covered: Development environments -- why you need to know more tools than just a language Amazon AWS -- talk about free tier and look at management panel This will cover the basics of what an aspiring data scientist needs to know. The whole point of this is to give an overview of many different technologies. Your first battle in data science is simply knowing what exists and how it all relates. This is not a deep dive, it's an overview, so don't worry about taking notes, just grab a coffee and join me online for a whirlwind tour. This is going to be a do-over of the first half of a talk I gave at the Big Data conference a few weeks ago. I did not go a good job presenting my slides to the Google Hangouts viewers and will re-present this. The talks this is based on (this will be the first half, not covereing Shiny until a future Hangout on iPython/Jupyter and Shiny tbd) Utah Intermountain Big Data Conference Data Science and Machine Learning Tools from Python to R, with Hands-On R/Shiny project http://utahgeekevents.com/Sessions (click on "Data Technologies for Developers") Openwest 2015 "Hadoop, MapReduce, Weka & Python Pandas, Oh My? A Data Mining and Machine Learning Primer" http://www.openwest.org/schedule/#68
Views: 1057 Mega Learning
Advanced Data Mining with Weka (2.2: Weka’s MOA package)
 
04:29
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Weka’s MOA package http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2705 WekaMOOC
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
26:02
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 159377 Timothy DAuria
Data Mining with Weka (5.2: Pitfalls and pratfalls)
 
10:02
Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Pitfalls and pratfalls http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11733 WekaMOOC
Data mining tools
 
19:56
Views: 370 Prarinya Ekapho
Advanced Data Mining with Weka (3.3: Using R to plot data)
 
13:49
Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Using R to plot data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3387 WekaMOOC
Rattle R Gui  Tool Bar
 
00:26
Rattle is a Gui written as a data mining and training tool for the R statistical programming language. Rattle is used by government departments, not for profits, and within the business community. Rattle is an open source project, and is free available from http://www.togaware.com . Rattle is currently used in business, scientific, law enforcement, defense and environmental areas.
Views: 3737 OZg3n1u5
Rattle - How to Quickly Determine The Optimal KMeans Cluster Size of a Dataset
 
06:51
In this video I show you how to quickly and easily determine the optimal cluster size for pretty much any data set through Rattle. We use the KMeans clustering tool inside of Rattle and also walk you through how to load your data set. But if you want more on loading your data set using Rattle I have several other videos on my channel for you to watch that will walk you through that. The purpose of this video is to give you really simple and extremely quick method for determining the optimal cluster size for your data within minutes - seconds once you've loaded your data set in to Rattle. It's very simple you select your target and unique identifier in your data, select execute and then select cluster. Next you select KMeans and the number of possible clusters. I recommend to use a higher number like 20 or 25 is I show you in the video. This will give you a better graph and showing of your data and you'll build a more quickly determine from the some of means as to which cluster or clusters would be your optimal choices. With the data set that I use in this video, the bike share data set from the University of California at Irvine's Data Science Department, we can quickly see that three is the most optimal cluster size for this data set. There are some other good choices with larger clusters, but clearly three is the best. The lines actually touch it three which tells you there's a very very strong cluster or breakout of customer data here. That's all you have to do at this point and then you go and set it into either R, RStudio, Alteryx or a number of other programs or applications to grass the clusters and then append the cluster information data set for further analysis where you may find some really cool insights based on those clusters. I hope you found this video interesting and informational. I will be posting more clustering videos shortly which will show you exactly how to go and further analyze these clusters, graph them and get insights from them or use them for further analyses. Please take a moment to like and subscribe! Also please make check out my channel and all the other great videos I have on data science, data analysis, coding, creating reusable models, predictive analytics and so much more! Thanks again and God bless!
Views: 169 Tech Know How
Modelling and Data Mining
 
59:55
This Lecture talks about Modelling and Data Mining
Views: 715 Cec Ugc
Introduction to Data Science & Regression Models in R [Live from WeWork]
 
01:52:33
http://www.meetup.com/Boston-Data-Mining/events/225860085/ Agenda: 6:00 PM – Networking 6:30 PM – 8PM – Introduction to Data Science & Regression Models in R Prerequisite: A basic familiarity with R. Laptop users should have R installed Overview: Understanding statistical methods is key in the process of making sound business decisions. Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. In this seminar you will learn how to analyze data using regression methods, interpret results, and use them for prediction and hypothesis testing. We will also practice running regressions in the open-source statistical software program, R. About Speaker: Victoria Liublinska received a PhD in Statistics from Harvard University. Her research was focused on developing methods for sensitivity analyses of conclusions obtained from studies with missing data. It resulted in several publications in leading peer-reviewed journals. After finishing her degree, she stayed at Harvard and taught statistics for several years. This year she transitioned to a senior research position at Harvard University Institutional Research office. Victoria has a plethora of consulting experience in many different fields, including business and marketing (interned at Google, worked with Concentric, Inc.), clinical (assisted on multiple studies at NYU School of Medicine), people analytics (worked with HR at Biogen, Inc.), biology (worked with researchers at the Arnold Arboretum), and litigation (provided expert opinion on multiple cases).
Views: 1920 Open Data Science
Rattle R Gui  log tab
 
01:39
Views: 812 OZg3n1u5

Help with writing resume and cover letter
Uvm admissions essay sample
It faculty cover letter
Lanxess newsletter formats
Professional writing service