![]() ![]() ANN builds a model based on the understanding of how the human brain works by establishing a relationship between the input and the output signals. Support Vector Machine(SVM) models finds extensive applications in pattern recognition fields as it is highly dexterous in learning the complex patterns efficiently.Īrtificial Neural Network Algorithms (ANN) often referred to as ‘deep learning’ can be practised through the ‘neuralnet’ package. Image processing is perhaps one of the most difficult tasks involved considering the amount of noise present, the positioning and orientation and how the image gets captured. The typical machine has to be able to distinguish the letters accurately. OCR reads various characters using key dimensions. Such analysis can be done using CrossTable( ) function available in gmodels package, where the results are represented in a tabular format with rows indicating the levels of one variable and the columns indicating the levels of the other variable. To explain this, let’s consider 2 nominal variables, one being ‘Income groups’ (Levels=High, Medium, Low), and the other being ‘Highest level of Education’ (Levels= Undegraduation, Graduation, Post-Graduation).We might be interested to find out whether the Income has a significant relationship with the affordability of the level of education. In our example, if the probability of buying bread is high the retailer may formulate new strategies such as keeping bread and butter together or give some discount if bread and butter are bought together etc., to augment the store’s revenue.ĭuring statistical analysis, we may often want to compare relationship between two nominal variables. Such type of analysis requires conditional probability which can be made available using e1071 package which in turn helps in finding effective business solutions. Let us try to analyse a situation where retailers need to find out what is the probability of a customer to buy bread when he has already bought butter. Provides the function naiveBayes( ) based on the simple application of conditional probability. The knn( ) function uses the Euclidean distance method to identify the k-nearest neighbours k is a user-specified number. ‘class’ contains the knn( ) function which provides the food for constructing the k-nearest neighbours algorithm- an easy machine learning algorithm. Such analysis may help Airtel understand the reasons for the churn and so, Airtel would be able to attract the customers with some lucrative offers or act upon the reasons of dissatisfaction among such customers. For instance, Airtel may try to predict a set of customers who are likely to churn out from their network. This algorithm has widespread application for processes which needs to maintain transparency at all levels. Decision tree models have a structure similar to the flowcharts with decision node indicating the decision to be made on a particular attribute. Tools for AI with RĬ50 finds application in building decision tree algorithms. On top of it all, this clever tool is available for free, runs on common and server hardware using an interpretor that is open source and supported by a professional community of committed users and data scientists. This makes it easy to develop and test new idea and verify them with the wide range of graphic functions provided. While the R language has a few unusual ways of doing common things, a lot of work can be done with just a little code that is developed in an interactive environment. The language can even provide a experimental web interface so that users try their hand at data analysis creating a synergy between man and machine to tackle big data problems. The language has been ported to big data environments. The entire development process can be captured in a markdown document to facilitate the publication of findings and new techniques discovered. Monitor for changes in the nature of incoming data Use the results to develop predictions about new data Provide an analysis of the reliability and goodness of fit of the results Randomly split a large dataset into training and test setsĪpply various statistical and machine learning functions. Use matrix transformations and linear algebra to process the data ![]()
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