Data Science Simplified Part 7: Log-Log Regression Models

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In the last few blog posts of this series, we discussed simple linear regression model. We discussed multivariate regression model and methods for selecting the right model. Fernando has now created a better model. In this article will address that question. This article will elaborate about Log-Log regression models.


Named Entity Recognition: Milestone Models, Papers and Technologies

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Named Entity Recognition (NER), or entity extraction is an NLP technique which locates and classifies the named entities present in the text. Named Entity Recognition classifies the named entities into pre-defined categories such as the names of persons, organizations, locations, quantities, monetary values, specialized terms, product terminology and expressions of times. Named Entity Recognition is a part of a broader field called Information Extraction. According to Wikipedia, Information Extraction is the task of automatically extracting structured information from any kind of text, structured and/or unstructured. Natural Language Processing has observed a paradigm shift in accuracy through past few years.


R Linear Regression

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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 which is used to predict the value of a response variable, on the basis of one or more predictor variables. Some common examples of linear regression are calculating GDP, CAPM, oil and gas prices, medical diagnosis, capital asset pricing etc. R Simple linear regression enables us to find a relationship between a continuous dependent variable Y and a continuous independent variable X. It is assumed that values of X are controlled and not subject to measurement error and corresponding values of Y are observed.


Introduction about Logistic Regression Model

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Hello guys, we have learnt about Linear Regression model in my previous article. Today, in this article we will get to learn the basics of Logistic Regression and some tricks to find the relation between the variables. Do you know what type of variable is used in logistic regression… Don't worry, if you don't know then let me teach the variables: In simple linear regression the variables are one dependent and one independent, In multiple linear regression there are more than one independent variable. Understand one thing if your data is in continuous form then use only linear regression model, while on the other hand, if your data is in categorical form(e.g. In this model the data been code in binary form.


R Nonlinear Regression Analysis

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Nonlinear Regression and Generalized Linear Models: Regression is nonlinear when at least one of its parameters appears nonlinearly. 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 nonlinear function. On the basis of independent variables, this process predicts the outcome of a dependent variable with the help of model parameters that depend on the degree of relationship among variables. Generalized linear models (GLMs) calculates nonlinear regression when the variance in sample data is not constant or when errors are not normally distributed.