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Rattle - Data Mining in R
 
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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: 35748 Melvin L
Download R and RStudio
 
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Download R and RStudio
Views: 7856 DWR447
Datamining project using R progamming part1
 
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code in R programming and ppt . Project:Stock predictor for pharmacy(Tablets). Data mining in R Studio
Views: 10247 Saiprasad Shettar
Data Mining Tool:Rattle R GUI
 
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Link to download R Console: https://cran.r-project.org/
Views: 3105 Chandrakala Badaga
Text Analytics With R | How to Connect Facebook with R | Analyzing Facebook in R
 
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In this text analytics with R tutorial, I have talked about how you can connect Facebook with R and then analyze the data related to your facebook account in R or analyze facebook page data in R. Facebook has millions of pages and getting emotions and text from these pages in R can help you understand the mood of people as a marketer. Text analytics with R,how to connect facebook with R,analyzing facebook in R,analyzing facebook with R,facebook text analytics in R,R facebook,facebook data in R,how to connect R with Facebook pages,facebook pages in R,facebook analytics in R,creating facebook dataset in R,process to connect facebook with R,facebook text mining in R,R connection with facebook,r tutorial for facebook connection,r tutorial for beginners,learn R online,R beginner tutorials,Rprg
How to Install Packages in R (R Tutorial 1.12)
 
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Learn how to find and install packages for R, using the "install.packages" command or menu options in RStudio. This video is a tutorial for programming in R Statistical Software for beginners. Here is an overview of the topics discussed in this video. You can click on the time stamp to jump to the specific topic. 0:00:12 what are packages in R and why use them 0:00:46 how to access help menu for packages in R when using the "install.packages" command 0:01:00 how to install packages in R using "install.packages" command when you know the name of the package 0:01:53 how to install packages in R using "install.packages" command when you don't know the name of the package 0:02:48 how to load the library of commands and functions for a package in R 0:03:17 how to access and download packages for R from 'R-project.org' website 0:04:30 how to access help menu for a specific package in R 0:04:57 how to remove a package from R using "remove.packages" command 0:05:11 how to install packages in R using the menus in RStudio
R - Install R packages from CRAN
 
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General discussion on R packages from CRAN, how to install them, and how to load them in an R session.
Views: 17136 Jalayer Academy
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 413118 edureka!
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( 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: 58715 edureka!
Install RStudio on Windows - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 19754 Udacity
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 905312 David Langer
R - Twitter Mining with R (part 1)
 
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Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates. Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.
Views: 64418 Jalayer Academy
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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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: 28964 Tatvic Analytics
Getting Tweets, Trends, and User Timeline from Twitter using R
 
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Includes working with r for, - getting tweets from twitter - saving data in a csv file - getting worldwide and local twitter trends - getting user timeline Machine Learning videos: https://goo.gl/WHHqWP R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 20639 Bharatendra Rai
Rattle for Data Mining - Using R without programming (CRAN)
 
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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: 42451 Learn Analytics
Import Data, Copy Data from Excel to R CSV & TXT Files | R Tutorial 1.5 |MarinStatsLectures
 
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Import Data, Copy Data from Excel (or other spreadsheets) to R CSV & TXT Files; Practice with Dataset: (https://bit.ly/2rOfgEJ) More Statistics and R Programming Tutorials: https://bit.ly/2Fhu9XU How to Import CSV data into R or How to Import TXT files into R from Excel or other spreadsheets using function in R ►How to import CSV data into R? We will be using "read.table" function to import comma separated data into R ► How to import txt data file into R? You will learn to use "read.delim" function to import the tab-delimited text file into R ► You will also learn to use "file.choose" argument for file location, "header" argument to let R know the data has headers or variable names and "sep" argument to let R know how the data values are separated. ►►Download the dataset here: https://statslectures.com/r-scripts-datasets ►►Like to support us? You can Donate https://bit.ly/2CWxnP2 or Share the Videos! ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►R Tutorials for Data Science https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA series https://bit.ly/2zBwjgL ►Linear Regression Concept and with R https://bit.ly/2z8fXg1 ►Puppet Master of Statistics: https://bit.ly/2RDAAv4 ►Hypothesis Testing: Concepts in Statistics https://bit.ly/2Ff3J9e ◼︎ Table of Content 0:00:17 What are the two main file types for saving a data file (CSV and TXT) 0:00:36 How to save an Excel file as a CSV file (comma-separated value) 0:01:10 How to open a CSV data file in Excel 0:01:20 How to open a CSV file in a text editor 0:01:36 How to import CSV file into R? using read.csv function 0:01:44 How to access the help menu for different commands/functions in R 0:02:04 How to specify file location for R? using file.choose argument on read.csv function 0:02:31 How to let R know our data has headers or variable names when importing the data into R? By using the “header” argument on read.csv function 0:03:22 How to import CSV file into R? using read.table function 0:03:38 How to specify the file location for the read.table function in R? using file.choose argument 0:03:46 How to specify how variables/columns are separated when importing data into R? the "sep" argument on read.table function will do that; for example if you don't specify that your data is comma separated, R ends up reading it all in as one variable 0:04:10 How to save a file in Excel as tab-delimited text (TXT) file 0:04:50 How to open a tab-delimited (.TXT) data file in a text editor 0:05:07 How to open a tab-delimited (.TXT) data file in excel 0:05:20 How to import tab-delimited (.TXT) data file into R? using read.delim function 0:05:44 How to specify the file path for read.delim function in R? using file.choose argument 0:06:06 How to import tab-delimited (.TXT) data file into R? using read.table function 0:06:23 How to specify that the data has headers or variable names when importing the data into R? using header argument on read.table function This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Downloading Data from Google Trends And Analyzing It With R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu In this video, I introduce Google Trends by querying it directly through the web, downloading a comma-delimited file of the results, and analyzing it in R. Full walkthrough and code: http://amunategui.github.io/google-trends-walkthrough/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 21318 Manuel Amunategui
Web Data Mining com R
 
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Web Data Mining com R
Views: 1453 Antonio Correa
R Tutorial #1 - Download, Installation, Setup - Statistical Programming Language R
 
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Part 1 of my R Tutorial - Download, installation and setup. R is a programming language and software environment for statistical computing and graphics. The R language has become a de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis. R is part of the GNU project. The website for R is http://www.r-project.org/ Also the Wikipedia article for R is useful to get a first impression of R.
Views: 127132 Tutorlol
Get Twitter Data into R
 
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Tutorial on how to download twitter data into R. Link to code below. Brackets aren't allowed in YouTube descriptions (which means I can't include the R code in here, link to code - http://storybydata.com/get-twitter-data-into-r-tutorial/
Views: 3581 Story by Data
Social Network Mining
 
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Social Network Mining Using R tool. termDocMatrix.rdata link:http://www.rdatamining.com/data If you are not able to install package using r tool then you can directly download the r package from below link. R data mining packages link:http://cran.r-project.org/web/packages/available_packages_by_name.html From this site download the .zip file of the package and after downloading the package open R tool and click on "packages" and select "install packages from local zip file". After successful installation you need to load the package.For loading the package click on "packages" and select "load packages" and then select the package you want to load. Get Great Deals on Amazon: https://goo.gl/jgZR7W Get Great Deals on Flipkart : https://goo.gl/MwgBfS Get Great Deals on Paytm : https://goo.gl/1XBQHr
Views: 5761 LetsGetGyan
Oracle Data Miner/SQL Developer + R Integration via SQL Query node
 
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This presentation and demo shows the integration capabilities of Oracle Data Miner/SQL Developer + Oracle R Enterprise integration.
Views: 9253 Charlie Berger
Apriori Algorithm with R Studio
 
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This is a video for RMD Sinhgad School of Engineering (BE-Computer) as a demonstration for one of the assignments of Business Analytics and Intelligence. Important Links: Ubuntu 16.04.2 LTS Download: https://www.ubuntu.com/download/desktop R installation instructions: https://www.datascienceriot.com/how-to-install-r-in-linux-ubuntu-16-04-xenial-xerus/kris/ R studio Download: https://www.rstudio.com/products/rstudio/download/ R Tutorial: http://tryr.codeschool.com/
Views: 6530 Varun Joshi
R Programming Import Data from URL
 
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Learn how to Import Data from URL in R Programming Language.
Views: 8198 DevNami
Hands-on dplyr tutorial for faster data manipulation in R
 
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dplyr is a new R package for data manipulation. Using a series of examples on a dataset you can download, this tutorial covers the five basic dplyr "verbs" as well as a dozen other dplyr functions. Watch the follow-up tutorial: http://youtu.be/2mh1PqfsXVI View the R Markdown document: http://rpubs.com/justmarkham/dplyr-tutorial Download the source document: https://github.com/justmarkham/dplyr-tutorial Read about why I love dplyr: https://www.dataschool.io/dplyr-tutorial-for-faster-data-manipulation-in-r/ Tutorial contents: 1. Introduction to dplyr (starts at 0:00) 2. Loading dplyr and the example dataset (starts at 2:29) 3. Understanding "local data frames" (starts at 3:23) 4. Verb #1: `filter` (starts at 5:17) 5. Verb #2: `select`, plus `contains`, `starts_with`, `ends_with`, `matches` (starts at 7:54) 6. Using chaining syntax for more readable code (starts at 9:34) 7. Verb #3: `arrange` (starts at 12:53) 8. Verb #4: `mutate` (starts at 13:55) 9. Verb #5: `summarise`, plus `group_by`, `summarise_each`, `n`, `n_distinct`, `tally` (starts at 15:31) 10. Window functions: `min_rank`, `top_n`, `lag` (starts at 26:47) 11. Convenience functions: `sample_n`, `sample_frac`, `glimpse` (starts at 32:44) 12. Connecting to databases (starts at 34:21) == RESOURCES == Reference manual and vignettes: http://cran.r-project.org/web/packages/dplyr/index.html July 2014 webinar: http://pages.rstudio.net/Webinar-Series-Recording-Essential-Tools-for-R.html July 2014 webinar code: https://github.com/rstudio/webinars/tree/master/2014-01 Tutorial by Hadley Wickham: https://www.dropbox.com/sh/i8qnluwmuieicxc/AAAgt9tIKoIm7WZKIyK25lh6a GitHub repo: https://github.com/hadley/dplyr List of releases: https://github.com/hadley/dplyr/releases == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 154457 Data School
R Tutorial 01  Installing R
 
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R Tutorial 01: Installing R This lesson shows you how to get started with R by installing the software in a windows pc / computer. R is a programming language for statistical computing. Download and Install your R console. This video demonstrates a customized setup so as to enable easy maneuver while coding. Visit www.rstatistics.net for written R tutorials and more. The software that is used for data mining / machine learning / data science / statistical computing and mathematical problem solving.
Views: 64 Gladwin Analytics
Handling Class Imbalance Problem in R: Improving Predictive Model Performance
 
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Provides steps for carrying handling class imbalance problem when developing classification and prediction models Download R file: https://goo.gl/ns7zNm data: https://goo.gl/d5JFtq Includes, - What is Class Imbalance Problem? - Data partitioning - Data for developing prediction model - Developing prediction model - Predictive model evaluation - Confusion matrix, - Accuracy, sensitivity, and specificity - Oversampling, undersampling, synthetic sampling using random over sampling examples predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 12245 Bharatendra Rai
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming in which I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
Windows Tutorial on downloading and installing R and RStudio
 
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Download Links - R-project: http://cran.r-project.org/bin/windows/base/ RStudio: http://www.rstudio.com/ide/download/desktop
Views: 2456 TheCoatlessProfessor
Partitioning data into training and validation datasets using R
 
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Link to download data file: https://drive.google.com/open?id=0B5W8CO0Gb2GGUVNyZ1JqMW1NZjA Includes example of data partition or data splitting with R. - Shows steps for reading CSV file into R. - Illustrates developing linear regression model using training data and then making predictions using validation data set in r. - Discusses regression coefficients - Provides application example using an automobile warranty claims dataset R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 27559 Bharatendra Rai
R Programming, Data Mining
 
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R Programming, Data Mining
Views: 506 ScholarsPro
Spatial Regession in R 1: The Four Simplest Models
 
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We run OLS (with spatial diagnostics), SLX, Spatial Error and Spatial Lag Models. We also run the spatial Hausman test. Along the way, we discover a bug in the R SLX code in the spdep package, and get it fixed. Very exciting! Download the file with the data and commands here: http://spatial.burkeyacademy.com/home/files/R%20Spatial%20Regression1.zip Link to the entire Spatial Statistics Playlist: https://www.youtube.com/playlist?list=PLlnEW8MeJ4z6Du_cbY6o08KsU6hNDkt4k See more at http://spatial.burkeyacademy.com My Website: http://www.burkeyacademy.com/ Support me on Patreon! https://www.patreon.com/burkeyacademy Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/
Views: 8683 BurkeyAcademy
R Tutorial - 4 - Install R and R Studio For Beginners [4/13]
 
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R Tutorial - 4 - Install R and R Studio For Beginners [4/13] Official Link :- https://cran.r-project.org/ https://www.r-project.org/ Download R :- https://cran.r-project.org/bin/windows/base/ Download R Studio:- https://www.rstudio.com/products/rstudio/download/ In this video , We will learn about Installation of R and R Studio. This is Window installation video in which I used R Studio Free Version. It is best for Developers. What is R ? R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. Currently top most world wide compniese hiring for R developers. According to Data Science survay 2015,2016 R developer demands are very high. Subscribe to our YouTube channel at https://www.youtube.com/channel/UC6U2o83YHhhtM9W7IzWAPRA Also please like my facebook page :- https://www.facebook.com/learnetix/?ref=bookmarks Please comment down if you getting any issue regarding technical contain and also please share with others. Keywords :- r (programming language), r tutorial, introduction to r, data science, getting started with r, software tutorial, learning r, data analysis, statistical data analysis, machine learning (software genre), learn r, reading data into r, data mining (software genre), r-software, r analytics tutorial, r analytics training, r analytics, r predictive analytics, r analysis tutorial, data analysis (media genre), data scientist, data scientist tutorial -~-~~-~~~-~~-~- Please watch: "Apache Pig Tutorial | 1. Introduction to Apache Pig | Hadoop Pig Tutorial For Beginners" https://www.youtube.com/watch?v=RXAuNhho5do -~-~~-~~~-~~-~-
Views: 378 Nandan Priyadarshi
Data science : R Predictive analytics with Decision Tree {in தமிழ்}
 
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R - Decision Tree. Advertisements. Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R. Video list in Tamil https://goo.gl/Pz2BPn Video list in English https://goo.gl/26f6T1 Data Download - http://atozknowledge.com/downloads/r/data1.csv YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 1037 atoz knowledge
Learning Data Mining with R : Example – Using a Single Line of Code in R | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lXhDAx]. The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on. • Learn about the structure of the internal mtcars dataset • Filter on the dataset • Aggregate on the dataset For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 639 Packt Video
The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 158440 Siraj Raval
Scraping Web Data in R - Rvest Tutorial
 
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A short video tutorial for downloading website data into R using the Rvest package. I have used it countless times in my own RStats web scraping projects, and I have found it to be especially useful for R webscraping projects that involve a static HTML webpage. This guide will also cover installing/using the Selector Gadget tool. The Rvest package is available on CRAN. Visit http://www.selectorgadget.com for more information on Selector Gadget. In this video, we will download web data using RStudio and Google Chrome.
Views: 13064 R You Ready For It?
IS 640 R Data Mining Project Solutions
 
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SOLUTION LINK: http://libraay.com/downloads/is-640-r-data-mining-project-solutions/ Use Random Forests, Neural Networks and Support Vector Machines to predict loan status (default or not). Understand the difference between in-sample fitting and out-of-sample predictive performance. Use two cross-validation methods to assess analytic model performance. Save this file on your desktop as yourlastname_640DM.docx. Load the Loan.csv data set into R. It lists the outcome of 850 loans. The data variables include loan status, credit grade (from excellent to poor), loan amount, loan age (in months), borrower’s interest rate and the debt to income ratio. Code loan status as a binary outcome (0 for current loans, 1 for late or default loans). Display the column names from the loan data set. Fit the loan data set using random forest function. Copy the trained random forest model and the confusion matrix from R and paste it below. [10 points] Randomly select 750 out of 850 loans as your training sample. Use the remaining 100 loans as your test set. Train the 2nd random forest model using the training set. Apply the 2nd model to the test set to predict loan status. Compare your predictions to the true loan statuses (using table function). Display the confusion matrix below. Based on this confusion matrix, what’s the overall misclassification rate? [10 points] Fit the loan data set using an artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare in-sample predictions to the true loan statuses. Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build the 2nd artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build a model of support vector machine. Use table function to compare the SVM’s out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Randomly shuffle the loan data set. Run 10-fold cross-validation to evaluate the out-of-sample performance of Random Forest, ANN and SVM. Based on your cross-validation results, which model has the best out-of-sample performance? Please briefly explain why. [30 points] Run leave-one-out cross-validation to evaluate the performance of random forest algorithm in predicting loan status. Why does it take much longer to run leave-one-out cross-validation than to run ten-fold cross-validation? Based on the result of your leave-one-out cross-validation, how many loans are misclassified by the random forest model?[20 points] Please save your word file as a pdf file named yourlastname_640DM.pdf. Submit the pdf file through the drop box in your Canvas account.
Views: 110 Libraay Downloads
Sample Data in R | Sample datasets for data mining | sample data sets for statistical analysis
 
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Sample or in built datasets in R for beginners to quickly learn the tools without worrying much about real time data. In this video I've one through the some of the sample datasets available in R and how you can use it. Sample Data in R | Sample datasets for data mining | sample data sets for statistical analysis
Retrieve and analyze a gene expression data set from NCBI GEO in R
 
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R script is available at: https://github.com/hongqin/RCompBio/blob/master/ncbigeo/ncbiGEO2012Nov14-demo-youtube.R SBIO386, Spelman College, Fall 2012
Views: 22612 Hong Qin

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