Linear Regression Machine Learning | Linear Regression in R | Linear Regression in Python

This “Linear Regression” Tutorial by Great Learning would help you to comprehensively learn all the underlying concepts of Linear Regression. You will also learn how to implement linear regression in both R language and Python language

1. 1:00 - Case Study to understand Need of Regression Analysis
2. 4:08 - Intro to Linear Regression
3. 9:39 - Multiple Linear Regression
4. 10:50 – Linear Regression in R
5. 29:39 – Linear Regression in python

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[AlokPratapSingh]

This is my 1st month of R programming (not from computer background). you made this all stuff easy., playing with data is actually a fun.... Thank you Sir.

library(dplyr)
data("diamonds")


2/3
caTools::sample.split(diamonds, SplitRatio = .75)-> split_tag
subset(diamonds, split_tag==T)->train
subset(diamonds, split_tag==T)
table(split_tag)
caTools::sample.split(diamonds, SplitRatio = .75)->split_tag
table(split_tag)
caTools::sample.split(diamonds$price, SplitRatio = .75)->split_tag
subset(diamonds, split_tag==T)->train
subset(diamonds, split_tag==F)->test
rm(diamonds)
model1 <- lm(price~carat)
model1 <- lm(price~carat, data= train)
summarise(model1)
summary(model1)
model1$residuals
predict(model1, train)->result1
as_tibble(cbind(actual= train$price, predict= result1))
as_tibble(cbind(actual= train$price, predict= result1)) %>% mutate(error= actual- predict, error2= error^2)
as_tibble(cbind(actual= train$price, predict= result1)) %>% mutate(error= actual- predict, error2= error^2)->datatrain
datatrain
sqrt(mean(datatrain$error2))
rmse1
ls()
as.data.frame(ls())
predict(model1, test)-> result2
as_tibble(cbind(actual= test$price, predict= result2))
as_tibble(cbind(actual= test$price, predict= result2)) %>% mutate(error= actual- predict, error2= error^2)
as_tibble(cbind(actual= test$price, predict= result2)) %>% mutate(error= actual- predict, error2= error^2)-> datatest
datatest
rmse2 <- sqrt(mean(datatest$error2))
rmse2
rmse1
as.data.frame(ls())
model1
model2 <- lm(price~ x+y+z, data= train)
model2
predict(model2, test)-> myresult
cbind(actual= test$price, predict= myresult)-> finaldata

finaldata
finaldata %>% mutate(error= actual- predict, error2= error^2)
finaldata %>% mutate(error= actual- predict, error2= error^2)->finaldata
rmse2
rmse3 <- sqrt(mean(finaldata$error2))
rmse2;rmse3

[mridhusharma]

god bless you sir. kindly prepare full tutorial of word2vec algorithim given by google. Grateful to you

[algorithmsguide]

00:09:28 How you got slope and intercept as 6.8 and 261..

[algorithmsguide]

In Multiple linear regression, how the equation is created. you have shown the equation and haven't explained how the weights or coefficients would be created.

[greatlearning]

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