Artificial inteliggence and machine learning ppt

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The document provides an overview of exploratory data analysis using the 'mtcars' dataset in R, covering basic concepts such as variables, vectors, and data frames. It includes various data visualization techniques, including histograms, kernel density plots, bar plots, and box plots, along with methods for univariate, bivariate, and multivariate analysis. Key R functions are demonstrated for summarizing and visualizing the dataset, identifying patterns, and analyzing relationships between variables.

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MODULE 2 : EXPLORATORY DATA ANALYSIS
Basic R oblects
•Variables
•Vector
•Array
•Matrics
•Data Frame

BASIC FUNCTIONS
# To pull mtcars data in this session
data()
data("mtcars")
#To view the variables / data dictionary
mtcars # print mydata
#To view the variables / data dictionary
str(mtcars) #To view the variables / data dictionary
dim(mtcars) # dimensions of an object
names(mtcars)
class(mtcars) # class of an object (numeric, matrix, data frame, etc)

head(mtcars) #To view top records of mtcars
# print first 10 rows of mydata
head(mtcars, n=10)
tail(mtcars) #to view bottom records of mtcars
##Variable Identification - Inferences
nrow(mtcars)
ncol(mtcars)
mean(mtcars[,2])
median(mtcars[,9])
range(mtcars$mpg)
mean(mtcars$mpg)

1. UNIVARIATE ANALYSIS
#Interquartile range (75 percentile - 25 percentile)
IQR(mtcars$mpg)
## Univariate Analysis (Pattern Recognition)
summary(mtcars) # Complete summary of the dataset
fivenum(mtcars)
fivenum(mtcars$mpg)
# edit the the data for Outliers
mtcars=edit(mtcars)
mtcars

HISTOGRAM
#Histogram
hist(mtcars$mpg)
hist(mtcars$mpg,col = "Red")
# Colored Histogram with Different Number of Bins
hist(mtcars$mpg, breaks=12, col="red")
# Add labels to the graph
x <- mtcars$mpg
h<-hist(x, breaks=10, col="red", xlab="Miles Per Gallon",
main="Histogram")

##(Histograms can be a poor method for determining the shape of a
distribution because it is so strongly affected by the number of bins used.)
##Kernal density plots are usually a much more effective way to view the
distribution of a variable.
# Kernel Density Plot
d <- density(mtcars$mpg) # returns the density data
plot(d) # plots the results
# Filled Density Plot
d <- density(mtcars$mpg)
plot(d, main="Kernel Density of Miles Per Gallon")
polygon(d, col="red", border="blue")

BAR PLOT
# Simple Bar Plot
counts <- table(mtcars$gear)
counts
barplot(counts, main="Car Distribution",
xlab="Number of Gears")
barplot(counts,col="Yellow", main="Car Distribution",
xlab="Number of Gears")
# Simple Horizontal Bar Plot with Added Labels
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", horiz=TRUE)
barplot(counts, main="Car Distribution", horiz=TRUE,
names.arg=c("3 Gears", "4 Gears", "5 Gears"))

BOX PLOT
#Boxplot
boxplot(mtcars$mpg)
boxplot(mtcars$mpg,horizontal = TRUE)
boxplot(mtcars$mpg,horizontal = TRUE,col = "Pink")
boxplot(mtcars$mpg,horizontal = TRUE,col = "Green",main="Mileage")

2. BIVARIATE ANALYSIS
o Continous Numerical Variables vs Continous Numerical Variables
o Continous Numerical Variables vs Discrete Numerical Variables
o Discrete Numerical Variables vs Categorical Variables

STACKED BAR PLOT
# Stacked Bar Plot with Colors and Legend
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",
xlab="Number of Gears", col=c("darkblue","red"),
legend = rownames(counts))
counts <- table(mtcars$cyl, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and Cylinders",
xlab="Number of Gears", col=c("darkblue","red","Yellow"),
legend = rownames(counts))

GROUPED BAR PLOT
# Grouped Bar Plot
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",
xlab="Number of Gears", col=c("darkblue","red"),
legend = rownames(counts), beside=TRUE)
counts <- table(mtcars$cyl, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and Cylinders",
xlab="Number of Gears", col=c("darkblue","red","Yellow"),
legend = rownames(counts),beside = TRUE)

BOX PLOT
#Comparitive Boxplot
boxplot(mtcars$mpg~mtcars$vs,horizontal=TRUE,col=c("Red","Green"))
boxplot(mtcars$mpg~mtcars$vs,horizontal=TRUE,col=c("Red","Green"),main="Compar
itive Boxplot")

3. MULTIVARIATE ANALYSIS
## To check the correlation among the variables
pairs(~ mpg + disp + hp + wt + drat + qsec , data = mtcars) #graphical
representation
cor(mtcars[,c(1,2,3,4,5,6,7,8,9,10,11)])

Artificial inteliggence and machine learning ppt

1. AI & MACHINE LEARNING

2. MODULE 2 : EXPLORATORY DATA ANALYSIS Basic R oblects •Variables •Vector •Array •Matrics •Data Frame

3. BASIC FUNCTIONS # To pull mtcars data in this session data() data("mtcars") #To view the variables / data dictionary mtcars # print mydata #To view the variables / data dictionary str(mtcars) #To view the variables / data dictionary dim(mtcars) # dimensions of an object names(mtcars) class(mtcars) # class of an object (numeric, matrix, data frame, etc)

4. head(mtcars) #To view top records of mtcars # print first 10 rows of mydata head(mtcars, n=10) tail(mtcars) #to view bottom records of mtcars ##Variable Identification - Inferences nrow(mtcars) ncol(mtcars) mean(mtcars[,2]) median(mtcars[,9]) range(mtcars$mpg) mean(mtcars$mpg)

5. 1. UNIVARIATE ANALYSIS #Interquartile range (75 percentile - 25 percentile) IQR(mtcars$mpg) ## Univariate Analysis (Pattern Recognition) summary(mtcars) # Complete summary of the dataset fivenum(mtcars) fivenum(mtcars$mpg) # edit the the data for Outliers mtcars=edit(mtcars) mtcars

6. HISTOGRAM #Histogram hist(mtcars$mpg) hist(mtcars$mpg,col = "Red") # Colored Histogram with Different Number of Bins hist(mtcars$mpg, breaks=12, col="red") # Add labels to the graph x <- mtcars$mpg h<-hist(x, breaks=10, col="red", xlab="Miles Per Gallon", main="Histogram")

7. ##(Histograms can be a poor method for determining the shape of a distribution because it is so strongly affected by the number of bins used.) ##Kernal density plots are usually a much more effective way to view the distribution of a variable. # Kernel Density Plot d <- density(mtcars$mpg) # returns the density data plot(d) # plots the results # Filled Density Plot d <- density(mtcars$mpg) plot(d, main="Kernel Density of Miles Per Gallon") polygon(d, col="red", border="blue")

8. BAR PLOT # Simple Bar Plot counts <- table(mtcars$gear) counts barplot(counts, main="Car Distribution", xlab="Number of Gears") barplot(counts,col="Yellow", main="Car Distribution", xlab="Number of Gears") # Simple Horizontal Bar Plot with Added Labels counts <- table(mtcars$gear) barplot(counts, main="Car Distribution", horiz=TRUE) barplot(counts, main="Car Distribution", horiz=TRUE, names.arg=c("3 Gears", "4 Gears", "5 Gears"))

9. BOX PLOT #Boxplot boxplot(mtcars$mpg) boxplot(mtcars$mpg,horizontal = TRUE) boxplot(mtcars$mpg,horizontal = TRUE,col = "Pink") boxplot(mtcars$mpg,horizontal = TRUE,col = "Green",main="Mileage")

10. 2. BIVARIATE ANALYSIS o Continous Numerical Variables vs Continous Numerical Variables o Continous Numerical Variables vs Discrete Numerical Variables o Discrete Numerical Variables vs Categorical Variables

11. STACKED BAR PLOT # Stacked Bar Plot with Colors and Legend counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("darkblue","red"), legend = rownames(counts)) counts <- table(mtcars$cyl, mtcars$gear) barplot(counts, main="Car Distribution by Gears and Cylinders", xlab="Number of Gears", col=c("darkblue","red","Yellow"), legend = rownames(counts))

12. GROUPED BAR PLOT # Grouped Bar Plot counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("darkblue","red"), legend = rownames(counts), beside=TRUE) counts <- table(mtcars$cyl, mtcars$gear) barplot(counts, main="Car Distribution by Gears and Cylinders", xlab="Number of Gears", col=c("darkblue","red","Yellow"), legend = rownames(counts),beside = TRUE)

13. BOX PLOT #Comparitive Boxplot boxplot(mtcars$mpg~mtcars$vs,horizontal=TRUE,col=c("Red","Green")) boxplot(mtcars$mpg~mtcars$vs,horizontal=TRUE,col=c("Red","Green"),main="Compar itive Boxplot")

14. 3. MULTIVARIATE ANALYSIS ## To check the correlation among the variables pairs(~ mpg + disp + hp + wt + drat + qsec , data = mtcars) #graphical representation cor(mtcars[,c(1,2,3,4,5,6,7,8,9,10,11)])

Artificial inteliggence and machine learning ppt

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