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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 For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). 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: 69346 edureka!
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: 966076 David Langer
Data Mining Tool:Rattle R GUI
 
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Link to download R Console: https://cran.r-project.org/
Views: 3268 Chandrakala Badaga
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] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 468251 edureka!
Data Mining - Facebook part 1
 
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This tutorial shows how to access,use and communicate with Facebook API using graph API explorer.It gives a brief idea about what kind of data we can retrieve from Facebook.
Views: 26909 Vikash Khairwal
Prediction Analysis in R
 
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Using Earnings dataset downloaded from Infochips
Views: 22235 Ani Aghababyan
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 33041 DataCamp
Simple Web Scraping using R
 
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Simple example of using R to extract structured content from web pages. There are several options and libraries that can be considered. if your webpage has data in HTML tables you can use readHTMLTable however in this example the web pages doesnt use HTML tables so we use a straightforward XPath technique to extract page content. We will in the end turn content from web pages into a data frame in R
Views: 37592 Melvin L
R vs Python? Best Programming Language for Data Science?
 
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R vs Python. Here I argue why Python is the best language for doing data science. Answering the question 'What is the best programming language for' is never black and white and there will be circumstances when python is not the best choice. Python is, though, the best general programming language. It is well supported, comparatively simple to learn, has a wealth of libraries specifically designed for data science such as pandas for data analysis and matplotlib for data visualization, and is extremely versatile. You won't regret learning python for data science. My Python Course - https://www.youtube.com/watch?v=Aah3TmR-dHc&list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J
Views: 30736 Python Programmer
Decision Tree with R | Complete Example
 
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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. 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: 53684 Bharatendra Rai
Decision Tree Classification in R
 
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This video covers how you can can use rpart library in R to build decision trees for classification. The video provides a brief overview of decision tree and the shows a demo of using rpart to create decision tree models, visualise it and predict using the decision tree model
Views: 75689 Melvin L
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: 16014 R You Ready For It?
Intro to Machine Learning with R & caret
 
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Lecture starts at 3:00 The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package (short for [C]lassification [A]nd [RE]gression [T]raining) is a must-have tool in your toolbelt. The caret package provides capabilities that are ubiquitous in all stages of the data science project lifecycle. Most important of all, caret provides a common interface for training, tuning, and evaluating more than 200 machine learning algorithms. Not surprisingly, caret is a sure fire way to accelerate your velocity as a data scientist! In this presentation Dave Langer will provide an introduction to the caret package. The focus of the presentation will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work. Attendees will learn how to: • Create stratified random samples of data useful for training machine learning models. • Train machine learning models using caret’s common interface. • Leverage caret’s powerful features for cross-validation and hyperparameter tuning. • Scale caret via use of multi-core, parallel training. • Increase their knowledge of caret’s many features. R code and accompanying dataset: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Machine%20Learning%20with%20R%20and%20Caret caret website: http://topepo.github.io/caret/index.html Learn more about David here: https://www.meetup.com/data-science-dojo/events/239730653/ -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ from over 742 companies globally. This channel contains tutorials, community talks, and courses on data science and data engineering. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8wHn0 See what our past attendees are saying here: https://hubs.ly/H0f8wtJ0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 41798 Data Science Dojo
Apriori Algorithm using R tool
 
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watch and learn! for any query comment below
Views: 5741 Vinaykumar Pandey
SOCIAL NETWORKING SITES USING R TOOL
 
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MSC.IT PART 1 SEM 1 SUBJECT: DATA MINING Aim: Using R-Tool , show the analysis for social networking sites.
Views: 840 Priyanka Jadhav
Time Series In R | Time Series Forecasting | Time Series Analysis | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78595 edureka!
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 104360 Bharatendra Rai
Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Logistic Regression Tutorial shall give you a clear understanding as to how a Logistic Regression machine learning algorithm works in R. Towards the end, in our demo we will be predicting which patients have diabetes using Logistic Regression! In this Logistic Regression Tutorial video you will understand: 1) The 5 Questions asked in Data Science 2) What is Regression? 3) Logistic Regression - What and Why? 4) How does Logistic Regression Work? 5) Demo in R: Diabetes Use Case 6) Logistic Regression: Use Cases 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 For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ 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. "
Views: 85033 edureka!
Naive Bayes Classification with R | Example with Steps
 
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Provides steps for applying Naive Bayes Classification with R. Data: https://goo.gl/nCFX1x R file: https://goo.gl/Feo5mT Machine Learning videos: https://goo.gl/WHHqWP Naive Bayes Classification is an important tool 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: 19605 Bharatendra Rai
Association Rules or Market Basket Analysis with R - An Example
 
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Provides an example of steps involved in carrying out association rule analysis in R. Association rule analysis is also called market basket analysis or affinity analysis. Some examples of companies using this method include Amazon, Netflix, Ford, etc. Definitions for support, confidence and lift are also included. Also includes, - use of rules package and a priori function - reducing number of rules to manageable size by specifying parameter values - finding interesting and useful rules - finding and removing redundant rules - sorting rules by lift - visualizing rules using scatter plot, bubble plot and graphs 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: 18118 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. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video 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.
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in 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 #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka 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 For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ 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."
Views: 46670 edureka!
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 58608 Bharatendra Rai
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- 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/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 68405 Data Science Dojo
Image Recognition & Classification with Keras in R | TensorFlow for Machine Intelligence by Google
 
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Provides steps for applying Image classification & recognition with easy to follow example. R file: https://goo.gl/fCYm19 Data: https://goo.gl/To15db Machine Learning videos: https://goo.gl/WHHqWP Uses TensorFlow (by Google) as backend. Includes, - load keras and EBImage packages - read images - explore images and image data - resize and reshape images - one hot encoding - sequential model - compile model - fit model - evaluate model - prediction - confusion matrix Image Classification & Recognition with Keras is an important tool 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: 18455 Bharatendra Rai
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 66895 deltaDNA
How to Perform a Bottleneck Analysis With Process Mining
 
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One of the typical questions in a process mining analysis is about the performance of the process. For example, you may have a service level agreement (SLA) with respect to the overall throughput time of the process. With Disco, you can analyze the case duration distribution and you can filter your data to focus on the slow cases to find out where in the process they lose so much time. You can then use the animation to visualize the discovered bottlenecks to your co-workers. In this video, we show you how a typical bottleneck analysis works and we give you two additional tips that will help you perform better bottleneck analyses on your own data. You can read the following article for further details on how to perform a bottleneck analysis with process mining: http://fluxicon.com/blog/2017/01/how-to-perform-a-bottleneck-analysis-with-process-mining
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to Data mining using R language. 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
Using R to Build a Data Discovery Tool for Domain Experts (Data Dialogs 2017)
 
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Tarak Shah — Our team of researchers has, among their duties, to identify prospective donors for new research initiatives and academic programs. This involves both being able to write and run sophisticated queries on our database, as well as identifying patterns and associations in the data that can be used to surface the correct alumni and friends for a particular initiative. We wanted to put these abilities directly in the hands of research analysts, who are subject matter experts and can leverage that expertise in interesting ways when doing these searches, but who do not have SQL or data science backgrounds. We used R to build a tool called the "discovery engine" that meets both of those needs while remaining accessible for the research analysts. In this talk, we'll take a look at how we built this tool and how it is being used. Tarak Shah is the Assistant Director of Prospect Analysis at UC Berkeley in the University Development and Alumni Relations (UDAR), where he develops tools and analytics to support major gift fundraising. Find out more about him at https://tarakc02.github.io/. More info: https://datadialogs.ischool.berkeley.edu/2017
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions 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] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 351258 edureka!
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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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: 164641 Timothy DAuria
Data Mining and Benford's Law analysis in R with Rattle package
 
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Distribution analysis of first significant digits in data to discover suspected value in accounting process.
Views: 1520 Giuseppe Caferra
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #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 For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ 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. "
Views: 57208 edureka!
Rattle R Gui  Tool Bar
 
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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: 3768 OZg3n1u5
Linear Regression in R | Linear Regression Model in R | R Programming Tutorial | Edureka
 
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This R tutorial gives an introduction to Linear Regression in R tool. This R tutorial is specially designed to help beginners. View upcoming batches schedule: http://goo.gl/BJJn0B This video helps you understand: • What is Data Mining? • What is Business Analytics? • Stages of Analytics / data mining • What is R? • Overview of Machine Learning • What is Linear Regression? • Case Study The topics related to ‘Data Analytics with R’ have been widely covered in our course. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 35258 edureka!
Support Vector Machines (SVM) Overview and Demo using R
 
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Quick overview and examples /demos of Support Vector Machines (SVM) using R. The getting started with SVM video covers the basics of SVM machine learning algorithm and then finally goes into a quick demo
Views: 58646 Melvin L
Support Vector Machine (SVM) with R - Classification and Prediction Example
 
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Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool 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: 37799 Bharatendra Rai
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 26486 Esri Events
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool 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: 21498 Bharatendra Rai
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 4022 Manukumar K J
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 32447 GRIETCSEPROJECTS
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 42385 WekaMOOC
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 210228 Well Academy
R introduction/ Basic Analytical Techniques Using R tools
 
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Basic Analytical Techniques Using R tools. After completing this course you will be able to: 1. Get a basic introduction to R 2. Understand exploration of data 3. Explore data using R 4. Visualize data using R 5. Understand diagnostic analytics 6. Implementing diagnostic analytics using R 7. Understand these concepts with the help of case studies Introduction to R: R has become one of the most popular tool for Data mining. Here are some of the basic things about R. It is freely available language for graphics and statistical computations, it is published under the GNU public license, it is mainly used in data mining and statistical analysis, and it includes time series analysis, linear modeling and nonlinear modeling among others, very active community and package contributions, R needs very little programming language knowledge necessary, and it can be downloaded from http://www.r-project.org, R Studio – optional. Watch the entire video for more information. Techniques d'analyse de base Utilisation des outils de R . Après avoir terminé ce cours vous serez en mesure de: 1. Obtenir une introduction à R 2. Comprendre l'exploration des données 3. Explorer les données en utilisant R 4. Les données Visualiser en utilisant R 5. Comprendre l'analyse de diagnostic 6. La mise en œuvre des analyses de diagnostic utilisant R 7. Comprendre ces concepts à l'aide d'études de cas Introduction à R : R est devenu l'un des outils les plus populaires pour l'exploitation minière de données . Voici quelques-unes des choses de base sur R. Il est la langue librement disponible pour les graphiques et les calculs statistiques , il est publié sous la licence publique GNU , il est principalement utilisé dans l'extraction de données et l'analyse statistique , et il comprend l'analyse des séries temporelles , modélisation linéaire et la modélisation non linéaire entre autres , la communauté et l'emballage des contributions très actifs , R a besoin de très peu de connaissances du langage de programmation nécessaire, et il peut être téléchargé à partir de http://www.r-project.org , R studio - en option. Regardez la vidéo entière pour plus d'informations . Learn how to assign values to objects, perform basic arithmetic functions (+, -, *, /), and a few other handy things in R. You will also learn the "ls", "rm", "sqrt", "log", "exp", "abs", and "#" commands. This video is a tutorial for programming in R Statistical Software for beginners. 0:00:20 Two different ways of assigning values to an object in R 0:01:46 How to use "ls" command to see what is stored in R 0:01:57 How to remove an object using "rm" command 0:02:52 How to assign character values to objects in R 0:03:37 How to perform arithmetic operations in R 0:04:50 How to take the square root of an object using "sqrt" command 0:05:07 How to use the "log" command in R 0:05:12 How to take the exponent or anti-log using "exp" command 0:05:30 How to calculate the absolute value using the "abs" command 0:06:14 a few handy keyboard shortcuts in R 0:06:41 How to include comments and notes within code in R using # sign Learn how to create vectors and matrices and perform simple operations on them using the R programming language. You will learn how to use the "seq", "rep", "matrix", and [] (square brackets) commands. 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. 0:00:29 how to create vectors in R for both numbers or objects using the "c" or "concatenate" command 0:01:09 how to create a sequence of integer values in R using the colon (:) 0:01:20 how to use "seq" command in R to create sequences 0:01:55 how to use "rep" command in R to create a vector of repeated numbers or characters 0:03:34 how to perform basic arithmetic functions on the elements of one vector in R 0:04:02 how to preform arithmetic functions on the corresponding elements of two vectors in R 0:04:59 how to extract elements of a vector using square brackets[] in R 0:06:08 how to create a matrix using "matrix" command in R 0:06:21 how to set the number of rows and columns in a matrix using "nrow" and "byrow" commands in R 0:07:05 how to use the square brackets to extract certain elements from a matrix in R 0:08:06 how to perform element-wise arithmetic functions in a matrix in R Introducción al lenguaje de programación R, orientado a computación estadística. En este video tutorial veremos un vistazo a este software, y hablaremos para que sirve y quienes lo utilizan, asi como unos breves ejemplos de cosas que podemos hacer con el, como hacer algunos gráficos o algunas manipulaciones de datos basicas.
Views: 455 jawad ab
Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation
 
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Provides steps for carrying out principal component analysis in r and use of principal components for developing a predictive model. Link to code file: https://goo.gl/SfdXYz Includes, - Data partitioning - Scatter Plot & Correlations - Principal Component Analysis - Orthogonality of PCs - Bi-Plot interpretation - Prediction with Principal Components - Multinomial Logistic regression with First Two PCs - Confusion Matrix & Misclassification Error - training & testing data - Advantages and disadvantages principal component analysis is an important statistical tool 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: 30598 Bharatendra Rai