Harshit1694

f <- function(toss=1){
  x <- sample(1:2, size=toss, replace=TRUE)
  y <- sample(1:2, size=toss, replace=TRUE)
  return(cbind(x,y))
}
set.seed(2500)
toss_times <- as.data.frame(f(2000))

library(plyr)

Harshit1694

#Splitting the data set into train and test
set.seed(2)

part <- sample(2, nrow(data), replace = TRUE, prob = c(0.7, 0.3))

train<- data[part == 1,]

test<- data[part == 2,]

Harshit1694

install.packages("glmnet")
library(glmnet)

train$Item_Weight[is.na(train$Item_Weight)] <- mean(train$Item_Weight, na.rm = TRUE)
train$Outlet_Size[is.na(train$Outlet_Size)] <- "Small"
train$Item_Visibility[train$Item_Visibility == 0] <- mean(train$Item_Visibility)
train$Outlet_Establishment_Year=2013 - train$Outlet_Establishment_Year


train<-train[c(-1)]

Harshit1694

#Calculating eigenvalues and eigenvectors
A<-matrix(c(30,31,40,41,50,51,60,61,70),nrow = 3,byrow = T)
e <- eigen(A)
e$values
e$vectors

Harshit1694

#Inverse of matrix

B<-matrix(c(30,31,40,41,50,51,60,61,70),nrow = 3,byrow = T)

A<-solve(B)
A

#Determinant of A
det(A)

Harshit1694

#Multiplication of matrix

A<-matrix(c(11,12,13,14,15,16,17,18,19),nrow = 3,byrow = T)
B<-matrix(c(20,21,22,23,24,25,26,27,28),nrow = 3,byrow = T)

A*B

Harshit1694

#Transpose of a matrix
t(A)

Harshit1694

A<-matrix(c(11,12,13,14,15,16,17,18,19),nrow = 3,byrow = T)
A

Harshit1694

data <- read.csv(file.choose())

#POPULATION PARAMETERS
pop_sd <- sd(data$Screen_size.in.cm.)*sqrt((length(data$Screen_size.in.cm.)-1)/(length(data$Screen_size.in.cm.)))
pop_mean <- mean(data$Screen_size.in.cm.)

z <- (9.5 - pop_mean) / pop_sd
z

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pnorm(80, mean=67, sd=13.7, lower.tail=FALSE)