## Regression analysis in R-Model Comparison

Regression analysis in R, just look at the Boston housing data and we can see a total of 506 observations and 14 variables. In this dataset, medv is the response variable, and the remaining...

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# Category: Methods

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Regression analysis in R-Model Comparison

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Handling missing values in R

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Social Network Analysis in R

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Cluster Analysis in R

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Decision Tree R Code

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Gradient Boosting in R

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Market Basket Analysis in R Data Mining

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Basic Functions in R

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Random Forest in R

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Linear optimization using R

Data Science Tutorials

Regression analysis in R, just look at the Boston housing data and we can see a total of 506 observations and 14 variables. In this dataset, medv is the response variable, and the remaining...

Handling missing values in R, one of the common tasks in data analysis is handling missing values. In R, missing values are often represented by the symbol NA (not available) or some other value...

Social Network Analysis in R, Social Network Analysis (SNA) is the process of exploring the social structure by using graph theory. It is mainly used for measuring and analyzing the structural properties of the...

Cluster Analysis in R, when we do data analytics, there are two kinds of approaches one is supervised and another is unsupervised. Clustering is a method for finding subgroups of observations within a data...

Decision Tree R Code, Decision trees are mainly classification and regression types. Classification means Y variable is factor and regression type means Y variable is numeric. Just look at one of the examples from...

Gradient Boosting in R, in this tutorial we are going to discuss extreme gradient boosting. Why is eXtreme Gradient Boosting in R? Popular in machine learning challenges. Fast and accurate Can handle missing values....

Market Basket Analysis in R, Market Basket Analysis is very popular. In this tutorial, the main idea is to identify the purchase pattern of the products, “what goes with what”. Based on this information...

Basic Functions in R, in this tutorial, we are going to discuss basic statistical or user-defined functions. Functions are very useful in R for faster and safe execution. Some will be inbuilt functions and...

Random Forest in R, Random forest developed by an aggregating tree and this can be used for classification and regression. One of the major advantages is its avoids overfitting. The random forest can deal...

Linear optimization using R, in this tutorial we are going to discuss the linear optimization problems in R. Optimization is everything nowadays. We all have finite resources and time and we want to make...