SMT.
SUSHILADEVI DESHMUKH COLLEGE OF ARTS,
SCIENCE AND COMMERCE, Airoli, Sector-4, Navi Mumbai-400 708
Date:
CERTIFICATE
This is to Certify that Mr.Yash Narendra Verma seat no. 46 of TYBSC CS
Semester V has completed the practical work in the subject of “Artificial
Intelligence ” during the academic year 2024 -25 under the guidance of
Asst.Prof Dnyaneshwar Deore being the partial requirement for the fulfilment
of the curriculum of Degree of Bachelor of Computer Science, University of
Mumbai.
Signature of Internal Guide Signature of
HOD
Signature of External Principal
College Seal
Artificial Intelligence 2024-25
Index
Sr.n Topic Date Sign
o
Breadth First Search & Iterative Depth First Search.
1 Implement the Breadth First Search algorithm
Implement the Iterative Depth First Search
algorithm
A* Search and Recursive Best-First Search
2 Implement the A* Search algorithm for
solving a pathfinding problem.
Implement the Recursive Best-First Search
algorithm
Implement the decision tree learning algorithm to
3 build a decision tree for a given dataset
Feed Forward Back propagation Neural Network
4 • Implement the Feed Forward Back
propagation algorithm to train a neural
network
• Use a given dataset to train the neural network
for a specific task
Adaboost Ensemble Learning
5 • Implement the Adaboost algorithm to create
an ensemble of weak classifiers.
• Adaboost Ensemble Learning Implement the
Adaboost algorithm to create an ensemble of
weak classifiers.
Train the ensemble model on a given dataset and
evaluate its performance Compare the results with
individual weak classifiers
Naive Bayes' Classifier
6 Implement the Naive Bayes algorithm for
classification.
Trin a Naive Bayes' model using a given
dataset and calculate class probabilities.
Evaluate the accuracy of the model on test data and
analyze the results.
Implement the K-NN Algorithm for classification or
7 regression.
Apply K-NN Algorithm on the given dataset &
predict the class or value for test data.
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Implement the Association Rule Mining algorithm
8 (e.g. Apriori) to find frequent dataset. Generate
association rules from the frequent item set and
calculate their support.
Practical 1
Aim: Breadth First Search & Iterative Depth First Search
Dataset: RMP.py File
Program 1 :Implement the Breadth First Search algorithm
to solve a given problem.
import queue as Q
from RMP import dict_gn #RMP.py i
start='Arad'
goal='Bucharest'
result=''
def BFS(city, cityq, visitedq):
global result
if city==start:
result=result+' '+city
for eachcity in dict_gn[city].keys():
if eachcity==goal:
result=result+' '+eachcity
return
if eachcity not in cityq.queue and eachcity not in visitedq.queue:
cityq.put(eachcity)
result=result+' '+eachcity
visitedq.put(city)
BFS(cityq.get(),cityq,visitedq)
def main():
cityq=Q.Queue()
visitedq=Q.Queue()
BFS(start, cityq, visitedq)
print("BFS Traversal from ",start," to ",goal," is: ")
print(result)
main()
Output:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Program 2 : Implement the Iterative Depth First Search algorithm
to solve the same problem.
import queue as Q
from RMP import dict_gn
start = "Arad"
goal = "Bucharest"
result = ""
def DLS(city,visitedstack,startlimit,endlimit):
global result
found = 0
result = result + city + " "
visitedstack.append(city)
if city == goal:
return 1
if startlimit == endlimit:
return 0
for eachcity in dict_gn[city].keys():
if eachcity not in visitedstack:
found = DLS(eachcity,visitedstack,startlimit+1,endlimit)
if found:
return found
def IDDFS(city,visitedstack,endlimit):
global result
for i in range(0,endlimit):
print("Seaching at Limit:", i)
found = DLS(city,visitedstack, 0 , i)
if found:
print("Found")
break
else:
print("Not Found!")
print(result)
print("______")
result=""
visitedstack = []
def main():
visitedstack = []
IDDFS(start,visitedstack,9)
print("IDDFS Traversal from ", start, " to ",goal," is:")
print(result)
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
main()
Output:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 2
Aim: A* Search and Recursive Best-First Search: -
Dataset: RMP.py File
Program 1: Implement the A* Search algorithm for solving a
pathfinding problem.
import queue as Q
from RMP import dict_gn
from RMP import dict_hn
start = 'Arad'
goal = 'Bucharest'
result = ''
def get_fn(citystr):
cities=citystr.split(",")
hn=gn=0
for ctr in range(0, len(cities)-1):
gn=gn+dict_gn[cities[ctr]][cities[ctr+1]]
hn=dict_hn[cities[len(cities)-1]]
return(hn+gn)
def expand(cityq):
global result
tot, citystr, thiscity=cityq.get()
if thiscity==goal:
result=citystr+"::"+str(tot)
return
for cty in dict_gn[thiscity]:
cityq.put((get_fn(citystr+","+cty),citystr+","+cty,cty))
expand(cityq)
def main():
cityq=Q.PriorityQueue()
thiscity=start
cityq.put((get_fn(start),start,thiscity))
expand(cityq)
print("The A* path with the total is: ")
print(result)
main()
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Output:
Program 2: Implement the Recursive Best-First Search algorithm for the
same problem.
import queue as Q
from RMP import dict_gn
from RMP import dict_hn
start = 'Arad'
goal = 'Bucharest'
result = ''
def get_fn(citystr):
cities=citystr.split(",")
hn=gn=0
for ctr in range(0, len(cities)-1):
gn=gn+dict_gn[cities[ctr]][cities[ctr+1]]
hn=dict_hn[cities[len(cities)-1]]
return(hn+gn)
def printout(cityq):
for i in range(0, cityq.qsize()):
print(cityq.queue[i])
def expand(cityq):
global result
tot, citystr, thiscity = cityq.get()
nexttot = 999
if not cityq.empty():
nexttot,nextcitystr,nextthiscity=cityq.queue[0]
if thiscity== goal and tot < nexttot:
result = citystr + "::" + str(tot)
return
print("Expaded city ---------", thiscity)
print("second best f(n)---------", nexttot)
tempq = Q.PriorityQueue()
for cty in dict_gn[thiscity]:
tempq.put((get_fn(citystr+','+cty), citystr+','+cty, cty))
for ctr in range(1,3):
ctrtot, ctrcitystr ,ctrthiscity = tempq.get()
if ctrtot < nexttot:
cityq.put((ctrtot, ctrcitystr,ctrthiscity))
else:
cityq.put((ctrtot, citystr, thiscity))
break
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
printout(cityq)
expand(cityq)
def main():
cityq=Q.PriorityQueue()
thiscity=start
cityq.put((999, "NA", "NA"))
cityq.put((get_fn(start), start, thiscity))
expand(cityq)
print(result)
main()
Output:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 3
Aim:
• Implement the decision tree learning algorithm to build a decision tree
for a given dataset.
• Evaluate the accuracy and efficiency on the test data set.
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 4
AIM: Feed Forward Back propagation Neural Network
• Implement the Feed Forward Back propagation algorithm to train a
neural network
• Use a given dataset to train the neural network for a specific task
from doctest import OutputChecker
import numpy as np
class NeuralNetwork():
def __init__(self):
np.random.seed()
self.synaptic_weights = 2 * np.random.random((3, 1)) - 1
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(self, x):
return x * (1 - x)
def train(self, training_inputs, training_outputs, training_iterations):
for iteration in range(training_iterations):
output = self.think(training_inputs)
error = training_outputs - output
adjustments = np.dot(training_inputs.T, error * self.sigmoid_derivative(output))
self.synaptic_weights += adjustments
def think(self, inputs):
inputs = inputs.astype(float)
output = self.sigmoid(np.dot(inputs, self.synaptic_weights))
return output
if __name__ == "__main__":
# Initializing the neuron class
neural_network = NeuralNetwork()
print("Beginning Randomly Generated Weights: ")
print(neural_network.synaptic_weights)
# Training data consisting of 4 examples--3 input values and 1 output
training_inputs = np.array([[0, 0, 1],
[1, 1, 1],
[1, 0, 1],
[0, 1, 1]])
training_outputs = np.array([[0, 1, 1, 0]]).T
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
# Training taking place
neural_network.train(training_inputs, training_outputs, 15000)
print("Ending Weights After Training: ")
print(neural_network.synaptic_weights)
user_input_one = str(input("User Input One: "))
user_input_two = str(input("User Input Two: "))
user_input_three = str(input("User Input Three: "))
print("Considering New Situation: ", user_input_one, user_input_two, user_input_three)
print("New Output data: ")
print(neural_network.think(np.array([user_input_one, user_input_two, user_input_three])))
Output:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 5
AIM: Adaboost Ensemble Learning
• Implement the Adaboost algorithm to create an ensemble of weak
classifiers.
• Adaboost Ensemble Learning Implement the Adaboost algorithm to
create an ensemble of weak classifiers.
• Train the ensemble model on a given dataset and evaluate its performance
Compare the results with individual weak classifiers
import pandas
from sklearn import model_selection
from sklearn.ensemble import AdaBoostClassifier
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-
diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
seed = 7
num_trees = 30
#kfold makes trees with split number.
#kfold = model_selection.KFold(n_splits=10, random_state=seed)
#n_estimators : This is the number of trees you want to build before predictions.
#Higher number of trees give you better voting optionsand perfomance performance
model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed,
algorithm='SAMME')
#cross_val_score method is used to calculate the accuracy of model sliced into x, y
#cross validator cv is optional cv=kfold
results = model_selection.cross_val_score(model, X, Y)
print(results.mean())
Output:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 6
AIM: Naive Bayes' Classifier
Implement the Naive Bayes algorithm for classification.
Trin a Naive Bayes' model using a given dataset and calculate class
probabilities.
Evaluate the accuracy of the model on test data and analyze the results.
Requirement: disease dataset
Code:
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 7
Aim:- Implement the K-NN Algorithm for classification or regression.
Apply K-NN Algorithm on the given dataset & predict the class or
value for test data.
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Practical 8
Aim:- Implement the Association Rule Mining algorithm (e.g. Apriori)
to find frequent dataset. Generate association rules from the frequent
item set and calculate their support.
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
Artificial Intelligence 2024-25
Name : Yash Narendra Verma Roll No: 46 Class: TYCS
