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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: 72152 Melvin L
Linear Discriminant Analysis in R | Example with Classification Model & Bi-Plot interpretation
 
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Provides steps for carrying out linear discriminant analysis in r and it's use for developing a classification model. Includes, - Data partitioning - Scatter Plot & Correlations - Linear Discriminant Analysis - Stacked Histograms of Discriminant Function Values - Bi-Plot interpretation - Partition plots - Confusion Matrix & Accuracy - training & testing data - Advantages and disadvantages linear discriminant 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: 11343 Bharatendra Rai
Machine Learning in R - Classification, Regression and Clustering Problems
 
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Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class. The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself? What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers! Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response. In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation. Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression! Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R. Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades. All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression. Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar. You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are. Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters. You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.
Views: 37273 DataCamp
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: 15532 Bharatendra Rai
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: 27692 Bharatendra Rai
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: 16575 Bharatendra Rai
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: 98790 Bharatendra Rai
Linear Discriminant Analysis in R | Multi Class Classification | Data Science
 
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In this video you will learn how to perform linear discriminant analysis in R. As opposed to Logistic Regression analysis, Linear discriminant analysis (LDA) performs well when there is multi class classification problem at hand. It assumes linear relationship between target and explanatory variables. For quadratic relationships you can used quadratic Discriminant analysis. It can well be used along with other classification algorithms like support vector machine, random forest, decision tree etc. ANalytics Study Pack : http://analyticuniversity.com/ Contact us for training/study packs [email protected] Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 4720 Analytics University
K-Nearest Neighbour (KNN) with R | Classification and Regression Examples
 
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Provides concepts and steps for applying knn algorithm for classification and regression problems. R code: https://goo.gl/FqpxWK Data file: https://goo.gl/D2Asm7 More ML 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: 3521 Bharatendra Rai
Data Science Fundamentals in R: Classification Trees with the Trees Package
 
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This video is a sample from Skillsoft's video course catalog. In this video, Steve Scott walks you through how to create a basic classification tree with the trees package in R. Steve Scott has been a software developer and IT Consultant for 16 years. Steve's career has been spent serving clients across the globe, responsible for building software architecture, hiring development teams, and solving complex problems through code. Now with a toolbox of languages, platforms, tools, and APIs, Steve rounds out his coding background with ongoing formal study in Mathematics and Computer Science at Mount Allison University. Skillsoft is a pioneer in the field of learning with a long history of innovation. Skillsoft provides cloud-based learning solutions for our customers worldwide, who range from global enterprises, government and education customers to mid-sized and small businesses. Learn more at http://www.skillsoft.com. https://www.linkedin.com/company/skillsoft http://www.twitter.com/skillsoft https://www.facebook.com/skillsoft
Views: 3814 Skillsoft YouTube
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: 52683 Bharatendra Rai
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: 34956 Bharatendra Rai
Classification and Regression Trees (CART) in R | Classification | Regression | StepUp Analytics
 
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CART undertakes the following situation: 1. Classification 2. Regression. In classification the target variable is categorical and tree gives classification in which tree predicts the class in which the instances will fall.
Views: 1026 StepUp Analytics
Decision Tree Analysis in R Example Tutorial
 
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Click here to download the example data set fitnessAppLog.csv: https://drive.google.com/open?id=0Bz9Gf6y-6XtTczZ2WnhIWHJpRHc
Views: 9121 The Data Science Show
Cross Validation using caret package in R for Machine Learning Classification & Regression Training
 
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1. Download cross validation using caret for machine learning classification and regression training example codes: https://drive.google.com/open?id=1uCUDvwJE0RYSmejg22aES6AmkXbLG--h 2. Download source data T2DRecords.csv link: https://drive.google.com/open?id=1MabU6pqYUacl2WbzwMuEfuQUw2_PAL2A 3. In caret package, if you meet "Error: package e1071 is required", simply execute the install.packages("e1071") to install the missing package. 4. Use as.factor and levels to transform numeric values into factors with different levels (Starting from 6:20 in the video). Related videos: 1. Use R to build ROC curve and measure a model's accuracy: https://www.youtube.com/watch?v=TZwI0XgcphM 2. Data partition with oversampling in R: https://www.youtube.com/watch?v=UFaZvynajtI 3. Cross Validation for Data with Imbalanced Classes: https://youtu.be/b1IAyZM6WAA
DSO 530: Decision Trees in R (Classification)
 
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This video shows you how to fit classification decision trees using R
Views: 106357 Abbass Al Sharif
Classifying and Clustering Data with R : Discriminant Analysis with 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/2xQrLB8]. This video shows how to do discriminant analysis in R. • Discuss iris data, correlations, and scatter plot • Show how to do data partition • Show how to do linear discriminant analysis 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: 2377 Packt Video
Linear Discriminant Analysis in R
 
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This video tutorial shows you how to use the lad function in R to perform a Linear Discriminant Analysis. It also shows how to do predictive performance and cross validation of the Linear Discriminant Analysis. This is an intermediate video. You should feel comfortable reading data in, subsetting data, regression or anova in R.
Views: 48098 Ed Boone
Logistic Regression / Classification in R
 
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Logistic Regression is one of the most widely used classification ML technique. This vlog introduces you to the concept and also helps you build your first model, score and judge it in R.
Views: 1267 Keshav Singh
Sentiment Analysis in R | R Tutorial | R Analytics | R Programming | What is R | R language
 
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This tutorial will deep dive into data analysis using 'R' language. By the end of this tutorial you would have learnt to perform Sentiment Analysis of Twitter data using 'R' tool. To learn more about R, click here: http://goo.gl/uHfGbN This tutorial covers the following topics: • What is Sentiment Analysis? • Sentiment Analysis use cases • Sentiment Analysis tools • Hands-On: Sentiment Analysis in R The topics related to ‘R’ language are extensively covered in our ‘Mastering Data Analytics with R’ course. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 43640 edureka!
R - Classification Trees (part 2 using rpart)
 
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Classification Trees are part of the CART family of technique for prediction. Here we use the package rpart, with its CART algorithms, in R to learn a classification tree model on the 'iris' data set available in all R installations. In this video I also compare our results from rpart to our results from C5.0 in the previous classification tree tutorial video called "
Views: 38860 Jalayer Academy
Multinomial Logistic Regression in R | Statistical Models | Multi class classification
 
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In this video you will learn about what is multinomial logistic regression and how to perform this in R. It is similar to Logistic Regression but with multiple values in the target variable. ANalytics Study Pack : http://analyticuniversity.com/ contact: [email protected] Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 8994 Analytics University
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: 72153 edureka!
Survival Ensembles: Survival Plus Classification for Improved Time-Based Predictions in R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Here we look at extracting AUC scores from survival models, blending and ensembling random forest survival with gradient boosting classification models, and measuring improvements on time-based predictions. Here is the blog post: http://amunategui.github.io/survival-ensembles/index.html 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: 2336 Manuel Amunategui
DSO 530: LDA, QDA, and KNN in R
 
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In this video: compare various classification models (LR, LDA, QDA, KNN).
Views: 16925 Abbass Al Sharif
R  - Regression Trees - CART
 
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Regression Trees are part of the CART family of techniques for prediction of a numerical target feature. Here we use the package rpart, with its CART algorithms, in R to learn a regression tree model on the msleep' data set available in the ggplot2 package.
Views: 37160 Jalayer Academy
Image classification with RandomForests using the R language
 
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RandomForests are currently one of the top performing algorithms for data classification and regression. Although their interpretability may be difficult, RandomForests are widely popular because of their ability to classify large amounts of data with high accuracy. In this video I show how to import a Landsat image into R and how to extract pixel data to train and fit a RandomForests model. I also explain how to conduct image classification and how to speed it up through parallel processing. See this post in my blog for more info: http://amsantac.co/blog/en/2015/11/28/classification-r.html This video shows how to implement this R-based RandomForests algorithms for image classification in QGIS: https://youtu.be/-6Hsase6xQw Remember to subscribe to my channel on Youtube for more videos!
Views: 21236 Alí Santacruz
Image Analysis and Processing with R
 
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Link for R file: https://drive.google.com/open?id=0B5W8CO0Gb2GGdjEwekZxZG5BdEE Provides image or picture analysis and processing with r, and includes, - reading and writing picture file - intensity histogram - combining images - merging images into one picture - image manipulation (brightness, contrast, gamma correction, cropping, color change, flip, flop, rotate, & resize ) - low-pass and high pass filter 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: 15497 Bharatendra Rai
KNN Algorithm Using R | KNN Algorithm Example | Data Science Training | Edureka
 
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** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka video on "KNN algorithm using R", will help you learn about the KNN algorithm in depth, you'll also see how KNN is used to solve real-world problems. Below are the topics covered in this module: (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 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 - - - - - - - - - - - - - - - - - #knn #datasciencewithr #datasciencecourse #datascienceforbeginners #knnalgorithm #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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 online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 1771 edureka!
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... 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/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 62701 Data Science Dojo
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: 49884 Bharatendra Rai
Random Forest with R : Classification with The South African Heart Disease Dataset
 
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Random Forest with R : Classification with The South African Heart Disease Dataset
Views: 1115 Dragonfly Statistics
Tutorial: Data Analytics with R: Classification and Regression Trees (CART)
 
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This video shows basic methods for developing and pruning classification and regression trees using the R programming language. The video takes viewers through a step by step approach to classification, demonstrating the approach with actual data. We cover the following topics: Classification: Discuss the purpose of classification and advantages and disadvantages of different methods Example Data: Review the dataset and classification objective for our example Tree Package: Execute classification using the popular “tree” package in R Pruning: Demonstrate how to reduce complexity in trees by “pruning” less-significant branches
Views: 55 Stephan Sorger
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: 56892 Melvin L
R - Sentiment Analysis and Wordcloud with R from Twitter Data | Example using Apple Tweets
 
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Provides sentiment analysis and steps for making word clouds with r using tweets about apple obtained from Twitter. Link to R and csv files: https://goo.gl/B5g7G3 https://goo.gl/W9jKcc https://goo.gl/khBpF2 Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of apple tweets before and after quarterly earnings report 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: 14579 Bharatendra Rai
K Nearest Neighbor(KNN) Classification in R | Data Science | Machine Learning
 
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IN this video you will learn how to perform the K Nearest neighbor classification R. You will also learn the theory of KNN. KNN is a type of classification algo like Logistic regression, decisions tree, SVM & random forest. However, this is a non-parametric technique ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA .
Views: 8392 Analytics University
K Nearest Neighbor (kNN) Algorithm  | R Programming | Data Prediction Algorithm
 
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In this video I've talked about how you can implement kNN or k Nearest Neighbor algorithm in R with the help of an example data set freely available on UCL machine learning repository.
Views: 36197 Data Science Tutorials
Neural Networks in R: Example with Categorical Response at Two Levels
 
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Provides steps for applying artificial neural networks to do classification and prediction. R file: https://goo.gl/VDgcXX Data file: https://goo.gl/D2Asm7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - neural network model - input, hidden, and output layers - min-max normalization - prediction - confusion matrix - misclassification error - network repetitions - example with binary data neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X. 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: 23560 Bharatendra Rai
Classifying and Clustering Data with R : Time Series Decomposition with 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/2xQrLB8]. This video shows how to do time series decomposition in R. • Discuss an example of time series data • Show how to do log transformation of data • Show how to do decomposition of additive time series 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: 3797 Packt Video
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 17496 Jalayer Academy
Neuroimaging Analysis in R: Image Preprocessing
 
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September Houston R Users Group main talk http://www.meetup.com/houstonr/events/232830049/
Views: 4317 Houston R Users
K-means using R
 
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Differentiating various species of flower 'Iris' using R
Views: 15574 Kanza Batool Haider
Intro to Text Mining Sentiment Analysis using R-12th March 2016
 
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Analytics Accelerator Program, February 2016-April 2016 batch
Views: 23991 Equiskill Insights LLP
Time Series Analysis - 1 | Time Series in R | Time Series Forecasting | Data Science | Simplilearn
 
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This Time Series Analysis (Part-1) in R tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data. Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs You can also go through the slides here: https://goo.gl/RsAEB8 A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R. Below topics are explained in this " Time Series in R Tutorial " - 1. Why time series? 2. What is time series? 3. Components of a time series 4. When not to use time series? 5. Why does a time series have to be stationary? 6. How to make a time series stationary? 7. Example: Forcast car sales for the 5th year To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-gj4L2isnOf8&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 15118 Simplilearn
Multinomial Logistic Regression with R: Categorical Response Variable at Three Levels
 
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Provides illustration of healthcare analytics using multinomial logistic regression and cardiotocographic data. R file: https://goo.gl/ty2Jf2 Data: https://goo.gl/kMAh8U Includes, - steps for preparing data for the analysis - use of nnet package in r - calculation of probabilities using coefficients from the model - estimating probabilities using the model - developing confusion matrix - calculation of misclassification error Logistic regression is an important tool for developing classification or predictive analytics models 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: 46118 Bharatendra Rai
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: 12362 Bharatendra Rai
ROC Curve Analysis in R Example Tutorial
 
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ROC Curve (Receiver Operating Characteristic Curve) and Random Oversampling Examples (ROSE Package) Analysis in R 1. Example Data Set LoanAnalysis.csv https://drive.google.com/open?id=1a6VBAvhoprYFayIVpsaMNCK4CLSQK35y 2. Analysis Code https://drive.google.com/open?id=1888o-tjgOkmAcpYfooqA8-GUOLrDSij5 3. Data Partition Analysis in R Lecture Video https://www.youtube.com/watch?v=UFaZvynajtI 4. Logistic Regression Analysis in R Lecture Video https://www.youtube.com/watch?v=eScK5w5JcHI 5. Decision Tree Analysis in R Example Tutorial Video https://www.youtube.com/watch?v=bJC5S_ViRCo
Views: 11835 The Data Science Show
How to do the Titanic Kaggle competition in R - Part 1
 
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As part of submitting to Data Science Dojo's Kaggle competition you need to create a model out of the titanic data set. We will show you how to do this using RStudio. Titanic Data Set: https://www.kaggle.com/c/titanic Download RStudio: https://www.rstudio.com/products/rstu... -- At Data Science Dojo, we're extremely passionate about data science. We've helped educate and train 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/H0f6y390 See what our past attendees are saying here: https://hubs.ly/H0f6wND0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 49879 Data Science Dojo
Text mining in R and Twitter Sentiment Analytics
 
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- Learn how to Analyse sentiments on anything being said on Twitter - Get your own Twitter developer app key and pull tweets - Understand what is sentiment analytics and text mining - Create impressive word clouds - Map sentiments on any topic and break them into bar graphs
Views: 21997 Equiskill Insights LLP

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