# Category: Methods

## Exploratory Data Analysis (EDA)

Exploratory Data Analysis is one of the critical processes of performing initial investigations on data analysis. Basic idea is to discover the patterns, anomalies, test hypotheses, and check the assumptions with the help of...

## Random Forest Feature Selection

Random Forest feature selection, why we need feature selection? When we have too many features or variables in the datasets and we want to develop a prediction model like a neural network will take...

## Linear Discriminant Analysis in R

Linear Discriminant Analysis in R, originally developed by R A Fisher in 1936 to classify subjects into one of the two clearly defined groups. It was later expanded to classify subjects into more than...

## Customer Segmentation K Means Cluster

Customer segmentation is the process of separation of customers into groups based on common characteristics or patterns so companies can market their products to each group effectively and significantly. In business-to-consumer marketing, most of...

## K Nearest Neighbor Algorithm in Machine Learning

K Nearest Neighbor Algorithm in Machine Learning, in this tutorial we are going to explain classification and regression problems. Machine learning is a subset of artificial intelligence which provides machines the ability to learn...

## Logistic Regression R- Tutorial

Logistic Regression R, In this tutorial we used the student application dataset for logistic regression analysis. Logistic regression is a statistical model that in its basic form uses a logistic function to model a...

## Self Organizing Maps in R- Supervised Vs Unsupervised

Self-organizing maps are very useful for clustering and data visualization. Self-organizing maps (SOMs) are a form of neural network and a beautiful way to partition complex data. In this tutorial, we are using college...

## Timeseries analysis in R

Timeseries analysis in R, in statistics time series, is one of the vast subjects, here we are going to analyze some basic functionalities with the help of R software. The idea here is to...

## 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...

## Handling missing values in R

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...

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