Machine Learning Lab Manual
LIST OF EXPERIMENTS:
1. The probability that it is Friday and that a student is absent is 3 %. Since there are 5
school days in a week, the probability that it is Friday is 20 %. What is the probability
that a student is absent given that today is Friday? Apply Baye’s rule in python to get the
result. (Ans: 15%)
2. Extract the data from database using python
3. Implement k-nearest neighbours classification using python
4. Given the following data, which specify classifications for nine combinations of VAR1
and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606,
using the result of k- means clustering with 3 means (i.e., 3 centroids)
VAR1 VAR2 CLASS
1.713 1.586 0
0.180 1.786 1
0.353 1.240 1
0.940 1.566 0
1.486 0.759 1
1.266 1.106 0
1.540 0.419 1
0.459 1.799 1
0.773 0.186 1
5. The following training examples map descriptions of individuals onto high, medium
and low credit - worthiness. KGR21 B. Tech – CSE Syllabus KGRCET
medium skiing design single twenties no -> highRisk
high golf trading married forties yes -> lowRisk
low speedway transport married thirties yes -> medRisk
medium football banking single thirties yes -> lowRisk
high flying media married fifties yes -> highRisk
low football security single twenties no -> medRisk
medium golf media single thirties yes -> medRisk
medium golf transport married forties yes -> lowRisk
high skiing banking single thirties yes -> highRisk
low golf unemployed married forties yes -> highRisk
Input attributes are (from left to right) income, recreation, job, status, age group, home-
owner. Find the unconditional probability of `golf' and the conditional probability of
`single' given `medRisk' in the dataset?
6. Implement linear regression using python.
7. Implement Naïve Bayes theorem to classify the English text.
8. Implement an algorithm to demonstrate the significance of genetic algorithm.
9. Implement the finite words classification system using Backpropagation algorithm.
6) Implement linear regression using python
Aim:
Implement linear regression using python
Source Code:
''' Implement linear regression using python
=================================
Explanation:
=================================
===> To run this program you need to install the pandas Module
---> pandas Module is used to read csv files
===> To install, Open Command propmt and then execute the following command
---> pip install pandas
And, then you need to install the matplotlib Module
---> matplotlib Module is used to plot the graphs
===> To install, Open Command propmt and then execute the following command
---> pip install matplotlib
Finally, you need to create dataset called "Age_Income.csv" file.
===============================
Source Code :
===============================
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# To read data from Age_Income.csv file
dataFrame = pd.read_csv('Age_Income.csv')
# To place data in to age and income vectors
age = dataFrame['Age']
income = dataFrame['Income']
# number of points
num = np.size(age)
# To find the mean of age and income vector
mean_age = np.mean(age)
mean_income = np.mean(income)
# calculating cross-deviation and deviation about age
CD_ageincome = np.sum(income*age) - num*mean_income*mean_age
CD_ageage = np.sum(age*age) - num*mean_age*mean_age
# calculating regression coefficients
b1 = CD_ageincome / CD_ageage
b0 = mean_income - b1*mean_age
# to display coefficients
print("Estimated Coefficients :")
print("b0 = ",b0,"\nb1 = ",b1)
# To plot the actual points as scatter plot
plt.scatter(age, income, color = "b",marker = "o")
# TO predict response vector
response_Vec = b0 + b1*age
# To plot the regression line
plt.plot(age, response_Vec, color = "r")
# Placing labels
plt.xlabel('Age')
plt.ylabel('Income')
# To display plot
plt.show()
Age_Income.csv
Age,Income
25,25000
23,22000
24,26000
28,29000
34,38600
32,36500
42,41000
55,81000
45,47500
Output:
7) Implement naive baye's theorem to classify the English text
Aim:
Implement naive baye's theorem to classify the English text
Source Code:
=================================
Explanation:
=================================
===> To run this program you need to install the pandas Module
---> pandas Module is used to read csv files
===> To install, Open Command propmt and then execute the following command
---> pip install pandas
And, then you need to install the sklearn Module
===> Open Command propmt and then execute the following command to install sklearn
Module
---> pip install scikit-learn
Finally, you need to create dataset called "Statements_data.csv" file.
===============================
Source Code :
===============================
'''
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_sc
ore
msglbl_data = pd.read_csv('Statements_data.csv', names=['Message', 'Label'])
print("The Total instances in the Dataset: ", msglbl_data.shape[0])
msglbl_data['labelnum'] = msglbl_data.Label.map({'pos': 1, 'neg': 0})
# place the data in X and Y Vectors
X = msglbl_data["Message"]
Y = msglbl_data.labelnum
# to split the data into train se and test set
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y)
count_vect = CountVectorizer()
Xtrain_dims = count_vect.fit_transform(Xtrain)
Xtest_dims = count_vect.transform(Xtest)
df = pd.DataFrame(Xtrain_dims.toarray(),columns=count_vect.get_feature_names_out())
clf = MultinomialNB()
# to fit the train data into model
clf.fit(Xtrain_dims, Ytrain)
# to predict the test data
prediction = clf.predict(Xtest_dims)
print('******** Accuracy Metrics *********')
print('Accuracy : ', accuracy_score(Ytest, prediction))
print('Recall : ', recall_score(Ytest, prediction))
print('Precision : ',precision_score(Ytest, prediction))
print('Confusion Matrix : \n', confusion_matrix(Ytest, prediction))
print(10*"-")
# to predict the input statement
test_stmt = [input("Enter any statement to predict :")]
test_dims = count_vect.transform(test_stmt)
pred = clf.predict(test_dims)
for stmt,lbl in zip(test_stmt,pred):
if lbl == 1:
print("Statement is Positive")
else:
print("Statement is Negative")
Statements_data.csv
This is very good place,pos
I like this biryani,pos
I feel very happy,pos
This is my best work,pos
I do not like this restaurant,neg
I am tired of this stuff,neg
I can't deal with this,neg
What an idea it is,pos
My place is horrible,neg
This is an awesome place,pos
I do not like the taste of this juice,neg
I love to sing,pos
I am sick and tired,neg
I love to dance,pos
What a great holiday,pos
That is a bad locality to stay,neg
We will have good fun tomorrow,pos
I hate this food,neg
Output:
8) Implement an algorithm to demonstrate the significance of genetic algorithm
# Python3 program to create target string, starting from
# random string using Genetic Algorithm
import random
# Number of individuals in each generation
POPULATION_SIZE = 100
# Valid genes
GENES = '''abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOP
QRSTUVWXYZ 1234567890, .-;:_!"#%&/()=?@${[]}'''
# Target string to be generated
TARGET = "I love GeeksforGeeks"
class Individual(object):
'''
Class representing individual in population
'''
def __init__(self, chromosome):
self.chromosome = chromosome
self.fitness = self.cal_fitness()
@classmethod
def mutated_genes(self):
'''
create random genes for mutation
'''
global GENES
gene = random.choice(GENES)
return gene
@classmethod
def create_gnome(self):
'''
create chromosome or string of genes
'''
global TARGET
gnome_len = len(TARGET)
return [self.mutated_genes() for _ in range(gnome_len)]
def mate(self, par2):
'''
Perform mating and produce new offspring
'''
# chromosome for offspring
child_chromosome = []
for gp1, gp2 in zip(self.chromosome, par2.chromosome):
# random probability
prob = random.random()
# if prob is less than 0.45, insert gene
# from parent 1
if prob < 0.45:
child_chromosome.append(gp1)
# if prob is between 0.45 and 0.90, insert
# gene from parent 2
elif prob < 0.90:
child_chromosome.append(gp2)
# otherwise insert random gene(mutate),
# for maintaining diversity
else:
child_chromosome.append(self.mutated_genes())
# create new Individual(offspring) using
# generated chromosome for offspring
return Individual(child_chromosome)
def cal_fitness(self):
'''
Calculate fitness score, it is the number of
characters in string which differ from target
string.
'''
global TARGET
fitness = 0
for gs, gt in zip(self.chromosome, TARGET):
if gs != gt: fitness+= 1
return fitness
# Driver code
def main():
global POPULATION_SIZE
#current generation
generation = 1
found = False
population = []
# create initial population
for _ in range(POPULATION_SIZE):
gnome = Individual.create_gnome()
population.append(Individual(gnome))
while not found:
# sort the population in increasing order of fitness score
population = sorted(population, key = lambda x:x.fitness)
# if the individual having lowest fitness score ie.
# 0 then we know that we have reached to the target
# and break the loop
if population[0].fitness <= 0:
found = True
break
# Otherwise generate new offsprings for new generation
new_generation = []
# Perform Elitism, that mean 10% of fittest population
# goes to the next generation
s = int((10*POPULATION_SIZE)/100)
new_generation.extend(population[:s])
# From 50% of fittest population, Individuals
# will mate to produce offspring
s = int((90*POPULATION_SIZE)/100)
for _ in range(s):
parent1 = random.choice(population[:50])
parent2 = random.choice(population[:50])
child = parent1.mate(parent2)
new_generation.append(child)
population = new_generation
print("Generation: {}\tString: {}\tFitness: {}".\
format(generation,
"".join(population[0].chromosome),
population[0].fitness))
generation += 1
print("Generation: {}\tString: {}\tFitness: {}".\
format(generation,
"".join(population[0].chromosome),
population[0].fitness))
if __name__ == '__main__':
main()
Output:
Generation: 1 String: tO{"-?=jH[k8=B4]Oe@} Fitness: 18
Generation: 2 String: tO{"-?=jH[k8=B4]Oe@} Fitness: 18
Generation: 3 String: .#lRWf9k_Ifslw #O$k_ Fitness: 17
Generation: 4 String: .-1Rq?9mHqk3Wo]3rek_ Fitness: 16
Generation: 5 String: .-1Rq?9mHqk3Wo]3rek_ Fitness: 16
Generation: 6 String: A#ldW) #lIkslw cVek) Fitness: 14
Generation: 7 String: A#ldW) #lIkslw cVek) Fitness: 14
Generation: 8 String: (, o x _x%Rs=, 6Peek3 Fitness: 13
.
.
.
Generation: 29 String: I lope Geeks#o, Geeks Fitness: 3
Generation: 30 String: I loMe GeeksfoBGeeks Fitness: 2
Generation: 31 String: I love Geeksfo0Geeks Fitness: 1
Generation: 32 String: I love Geeksfo0Geeks Fitness: 1
Generation: 33 String: I love Geeksfo0Geeks Fitness: 1
Generation: 34 String: I love GeeksforGeeks Fitness: 0
9) Implement the finite words classification system using Back-propagation algorithm
Aim:
Implement the finite words classification system using Back-propagation algorithm
Source Code:
Week9. py
''' Implement the finite words classification system using Back-propagation algorithm
=================================
Explanation:
=================================
===> To run this program you need to install the pandas Module
---> pandas Module is used to read csv files
===> To install, Open Command propmt and then execute the following command
---> pip install pandas
And, then you need to install the sklearn Module
===> Open Command propmt and then execute the following command to install sklearn
Module
---> pip install scikit-learn
===> Open Command propmt and then execute the following command to install sklearn
-neuralnetwork Module
---> pip install scikit-neuralnetwork
Finally, you need to create dataset called "Statements_data.csv" file.
===============================
Source Code :
===============================
'''
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_sc
ore
msglbl_data = pd.read_csv('Statements_data.csv', names=['Message', 'Label'])
print("The Total instances in the Dataset: ", msglbl_data.shape[0])
msglbl_data['labelnum'] = msglbl_data.Label.map({'pos': 1, 'neg': 0})
# place the data in X and Y Vectors
X = msglbl_data["Message"]
Y = msglbl_data.labelnum
# to split the data into train se and test set
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y)
count_vect = CountVectorizer()
Xtrain_dims = count_vect.fit_transform(Xtrain)
Xtest_dims = count_vect.transform(Xtest)
df = pd.DataFrame(Xtrain_dims.toarray(),columns=count_vect.get_feature_names_out())
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=
1)
# to fit the train data into model
clf.fit(Xtrain_dims, Ytrain)
# to predict the test data
prediction = clf.predict(Xtest_dims)
print('******** Accuracy Metrics *********')
print('Accuracy : ', accuracy_score(Ytest, prediction))
print('Recall : ', recall_score(Ytest, prediction))
print('Precision : ',precision_score(Ytest, prediction))
print('Confusion Matrix : \n', confusion_matrix(Ytest, prediction))
print(10*"-")
# to predict the input statement
test_stmt = [input("Enter any statement to predict :")]
test_dims = count_vect.transform(test_stmt)
pred = clf.predict(test_dims)
for stmt,lbl in zip(test_stmt,pred):
if lbl == 1:
print("Statement is Positive")
else:
print("Statement is Negative")
Statements_data.csv
This is very good place,pos
I like this biryani,pos
I feel very happy,pos
This is my best work,pos
I do not like this restaurant,neg
I am tired of this stuff,neg
I can't deal with this,neg
What an idea it is,pos
My place is horrible,neg
This is an awesome place,pos
I do not like the taste of this juice,neg
I love to sing,pos
I am sick and tired,neg
I love to dance,pos
What a great holiday,pos
That is a bad locality to stay,neg
We will have good fun tomorrow,pos
I hate this food,neg
Output:
