One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item.
You can perform either classification or regression tasks here. For example, identifying fraudulent…Continue
Nonlinear Regression and Generalized Linear Models:
Regression is nonlinear when at least one of its parameters appears nonlinearly. It commonly sorts and analyzes data of various industries like retail and banking sectors. It also helps to draw conclusions and predict future trends on the basis of user’s activities on the net.
The nonlinear regression analysis is the process of building a…
Added by Shreya Gupta on August 22, 2017 at 9:00pm — No Comments
Regression analysis is a statistical tool to determine relationships between different types of variables. Variables that remain unaffected by changes made in other variables are known as independent variables, also known as a predictor or explanatory variables while those that are affected are known as dependent variables also known as the response variable.
Linear regression is a statistical procedure…
Added by Shreya Gupta on August 20, 2017 at 9:00pm — No Comments
Now we are going to explain the various Graphical Models Applications in real life such as – Manufacturing, finance, Steel Production, Handwriting Recognition etc. At last, we will discuss the case study about the use of Graphical Models in the Volkswagen.…Continue
Added by Shreya Gupta on August 18, 2017 at 7:00pm — No Comments
This R tutorial is all about Contingency tables in R. First of all, we will discuss the introduction to R Contingency tables, different ways to create Contingency tables in R. This tutorial also covers the Complex Tables in R / Flat Tables in R, Cross Tabulation in R, Recreating original data from contingency tables in R, and everything related to R contingency tables.…Continue
Added by Shreya Gupta on August 17, 2017 at 7:00pm — No Comments
R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis and visualization.
Let us now look at some key capabilities of R:
Added by Shreya Gupta on August 11, 2017 at 11:30pm — No Comments
Manipulating and processing data in R
Data structures provide the way to represent data in data analytics. We can manipulate data in R for analysis and visualization.
One of the most important aspects of computing with data in R is its ability to manipulate data and enable its subsequent analysis and visualization. Let us see few basic data structures in R:
Added by Shreya Gupta on August 11, 2017 at 12:00am — No Comments
Importing Data in R
As we know c() function is used to concatenate or combine items in R as specified below:
Added by Shreya Gupta on August 8, 2017 at 1:30am — No Comments
Introduction to Apache Spark Streaming
A data stream is an unbounded sequence of data arriving continuously. Streaming divides continuously flowing input data into discrete units for further processing. Stream processing is low latency processing and analyzing of streaming data.
Spark Streaming was added to Apache spark in 2013, an extension of the core Spark API that provides scalable, high-throughput and fault-tolerant stream…Continue
Added by Shreya Gupta on August 5, 2017 at 2:00am — No Comments