## Deviance and AIC for Logistic Regression in R

Introduction This is for you,if you are looking for Deviance,AIC,Degree of Freedom,interpretation of p-value,coefficient estimates,odds ratio,logit score and how to find the final probability from logit score in logistic regression in R. Course for Beginners: https://www.udemy.com/machine-learning-using-r/?couponCode=GREAT_CODE Importing libraries & Reading Data Importing the required libraries.MASS is used for importing birthwt dataset library(MASS) #### Storing the […]

## Avoid Over fitting & start crossvalidation

Introduction If you want to learn what is K-fold cross-validation and how is it done in R,then please follow along.Open your RStudio and have fun!! Course for Beginners: https://www.udemy.com/machine-learning-using-r/?couponCode=DISFOR123 What is Cross-validation A model is usually given a known data set(training data set) on which training is done and unknown dataset(testing data set) against which […]

## Plotting Categorical Variable vs continuous variables

Let’s begin Data visualizations from basic to more advanced levels where we can learn about plotting categorical variable vs continuous variable or categorical vs categorical variables.Let’s start RStudio and begin typing in ๐ For Best Course on Data Science Developed by Data Scientist ,please follow the below link to avail discount https://www.udemy.com/machine-learning-using-r/?couponCode=DISFOR123 #### Let’s store […]

## K-means Clustering for Data Analytics in R

Introduction Here we will know about “how to perform k-means clustering in R” and “how to find best value of k in k-means clustering” Importing library Let’s open RStudio and follow along !! Let’s import the ggplot2 library which is needed for ggplot visualization library(ggplot2) Reading Dataset Let’s import the data set named โirisโ into […]

## Finding patterns in Predictor Variables

Finding correlated variables is very important in order to remove multicollinearity in multiple regression models.Once you know the correlated variables you can choose few from them or may be just one from them.It depends on correlation value and other factors.Let’s begin to visualize the correlation in variables in a nice plot.Open your RStudio and begin […]

## Neural Networks for Data Analytics in R

Introduction Precise Demo on Neural Networks for Machine Learning and Data Analytics in R. Open your RStudio and follow along !! Importing libraries #### importing the library MASS for “Boston” dataset library(MASS) library(neuralnet) ## Loading required package: grid Reading data #### Setting the seed so that we get same results each time #### we run […]

## How to install R and RStudio

R is popular tool used in Machine Learning,data analytics,business analytics,statistical analysis,bioinformatics. To use R efficiently we need another tool which is called as integrated development environment (IDE). RStudio is an integrated development environment (IDE) for R. For using R,we need following 1.Install R 2.Install R-Studio 3.Install R-Packages Mac Users To Install R Go to www.r-project.org […]

## How to begin with R and RStudio??

This is for those who have just installed R and RStudio and wants to begin with R. There are data objects in R like dataframes,vector,matrices,lists ,factors and so on. If you want to know about how to write these data objects in R or just wants to get started with R,then follow this video to […]

## Use of apply,lapply and sapply functions in R

This is for those who know about dataframes in R.If you want to do something either rowwise or column wise in dataset(or dataframe) then you can use functions like apply,lapply and sapply. Say We are given a dataset or dataframe which has three columns like age,weight and height and let’s say you have 1000 rows […]

## Random Forest for Machine Learning in R

Random forests are supervised machine learning method for classification and regression. Random forest is way of averaging multiple decision trees which are trained on different parts of the same training set. In Decision tree we make splits on predictor variables to predict the target. In Random Forest,we build many decision trees and then take the […]