Artifical_intiligence_worksheet-exp-9.docx

1. Experiment no. – 9
Student Name: Aditya UID: 19BCS2604
Branch: CSE - 11 Section/Group: ‘C’
Semester:5th
Date of Performance:15 Nov 2021
Subject Name:Artificial intelligence Subject Code: CSP - 303
& Machine learning Lab
1. Aim/Overview of the practical: Import cereal dataset shared by Carnegie Mellon
University (CMU). The details of the dataset are on the following link:
http://lib.stat.cmu.edu/DASL/Datafiles/Cereals.html. The objective is to predict the rating
of the cereals variables such as calories, proteins, fat etc. Test and Train using Neural
Networks.
2. Task to be done: a) Viewing the dataset
b) plotting the neural Network
c) Calculating the result & accuracy
3. Algorithm:
a) Algorithm for Viewing the dataset
Step1: Start
Step2: installing package “MASS”
Step3: import MASS library and then print dataset
Step4: then calculating min , max and scaled dataset by using
function
max = apply(dataset, 2 , max)
min = apply(dataset, 2 , min)
scaled_dataset = as.data.frame(scale(dataset, center =
min,scale = max - min))
Step5: then printing the scaled_dataset
Step6: end

2. b) Algorithms for plotting the neural Network
Step1: Start
Step2: installing packages “catools”and import the library
Step3: then initialising “neuralnet” and import the library
Step4: then defining the allvars and printing the allvars
Step5: then defining predictorvars by using function
predictorvars=paste(predictorvars,collapse="+");
Step6: printing the predictorvars
Step7: then defining form by using function
form=as.formula(paste("medv~",predictorvars,collapse="+"))
Step8: printing the form
Step9: then defining nm by using function
nn=neuralnet(formula =form,data =training_set,hidden
=c(4,2),threshold=0.01)
Step10:plotting nm
Step11:end
c) Algorithms for Calculating the result & accuracy
Step1: Start
Step2: then defining results by using function
results<- data.frame(actual = test_set$medv, prediction =
nn.results$net.result);
Step3: printing the results
Step4: then calculating predicted by using function
predicted=(results$prediction * (max(dataset$medv) -
min(dataset$medv))) + min(dataset$medv)
Step5: then calculating acutal by using function
actual=(results$actual * (max(dataset$medv) -
min(dataset$medv))) + min(dataset$medv)
Step6: then calculating comparision and deviation co by using
function
comparison=data.frame(predicted,actual)
deviation=((actual-predicted)/actual);
Step7: printing the deviation

3. Step8: calculating accuracy of dataset by using function
comparison=data.frame(predicted,actual,deviation)
accuracy=1-abs(mean(deviation))
Step9: printing the accuracy
Step10:end
4. Programming Code:
a) Code for Viewing the dataset
install.packages("MASS")
library("MASS")
dataset=Boston;
print(dataset)
View(dataset)
max = apply(dataset, 2 , max)
min = apply(dataset, 2 , min)
scaled_dataset = as.data.frame(scale(dataset, center = min,scale = max - min))
print(scaled_dataset)
b) Code for plotting the neural Network
install.packages("caTools")
library(caTools)
set.seed(123)
sample = sample.split(scaled_dataset$medv, SplitRatio = 0.6)
training_set = subset(scaled_dataset, sample==TRUE)
test_set = subset(scaled_dataset, sample==FALSE)
install.packages("neuralnet")
library(neuralnet)
set.seed(2)
allvars=colnames(dataset);
print(allvars)
predictorvars=allvars[!allvars%in%"medv"];

4. print(predictorvars)
predictorvars=paste(predictorvars,collapse="+");
print(predictorvars)
form=as.formula(paste("medv~",predictorvars,collapse="+"))
print(form)
nn=neuralnet(formula =form,data =training_set,hidden =c(4,2),threshold=0.01)
plot(nn)
c) Code for Calculating the result & accuracy
nn.results=compute(nn,test_set[,1:13])
results<- data.frame(actual = test_set$medv, prediction = nn.results$net.result);
print(results)
predicted=(results$prediction * (max(dataset$medv) -min(dataset$medv))) +
min(dataset$medv)
actual=(results$actual * (max(dataset$medv) - min(dataset$medv))) +
min(dataset$medv)
comparison=data.frame(predicted,actual)
deviation=((actual-predicted)/actual);
print(deviation)
comparison=data.frame(predicted,actual,deviation)
accuracy=1-abs(mean(deviation))
print(accuracy)

5. 5. Output: a) Output for Viewing the dataset

6. b) Output for plotting the neural Network

7. c) Output for Calculating the result & accuracy

8. 6. Learning outcomes (What I have learnt):
1. I have learnt about the R studio.
2. I have learnt about the R programming language.
3. Got to know about Neural Networks.
4. I have learnt how to use library files used to create Neural Networks in R.
5. I have learnt about calculating the Accuracy for a Neural Network.
7. valuation Grid (To be created as per the SOP and Assessment guidelines by the faculty):
Sr. No. Parameters Marks Obtained Maximum Marks
1.
2.
3.