L. D. College of Engineering
Opp Gujarat University, Navrangpura, Ahmedabad - 380015
LAB MANUAL
Minor Degree: Artificial Intelligence and Machine Learning
Branch: Computer Engineering
Introduction to AI and Machine
Learning (114AG01)
Semester: I V
Faculty Details:
1. Prof. M. K. Shah
2. Prof. P. G. Patel
3. Prof. H. D. Rajput
Introduction to AI and Machine Learning (114AG01)
CERTIFICATE
This is to certify that Mr./Ms. __________________________,
Enrollment Number________________ has satisfactorily completed the practical work
in _______________ subject at L D College of Engineering, Ahmedabad-380015.
Date of Submission:
_______________
Sign of Faculty:
_______________
Head of Department: _______________
Computer Engineering Department,
L. D. College of Engineering, Ahmedabad-15
Introduction to AI and Machine Learning (114AG01)
L. D. College of Engineering, Ahmedabad
Department of Computer Engineering
Practical List
Subject Name: Introduction to AI and Machine Learning (114AG01)
Term: 2022-2023
Sr.
No.
1
Title
Date
Basics of Python Programming
2
Study about numpy, pandas, Scikit-learn and matplotlib
libraries.
3
Getting Started with Python Logic Programming using
Kanren and SymPy packages
Write the code in Kanren to demonstrate the followings:
a) The use of logical variables.
b) The use of membero goal constructor
Write the code in Kanren to create parent and grandparent
relationships and use it to state facts and query based on
the facts.
4
5
Page Marks
No.
(10)
6
Write the code in Kanren to demonstrate the constraint
system.
7
Write the code in Kanren to match the mathematical
expressions
8
Write the code in python to implement linear regression
for one variable.
9
Write the code in python to implement linear regression
using gradient descent for one variable
10
Write the code in python to implement logistic regression
for single class classification.
11
Write the code in python to implement logistic regression
for multi class classification
Computer Engineering Department,
L. D. College of Engineering, Ahmedabad-15
Sign
Introduction to AI and Machine Learning (114AG01)
L. D. College of Engineering, Ahmedabad
Department of Computer Engineering
Practical Rubrics
Subject Name: Introduction to AI and Machine Learning Subject Code: (114AG01)
Term: 2022-2023
Rubrics ID
Criteria
Marks
Good (2)
Satisfactory (1) Need Improvement (0)
RB1
Regularity
02
RB2
Problem
Analysis and
Development of
Solution
03
RB3
Concept Clarity
and
Understanding
03
Concept is very
clear with proper
understanding
Concept is
clear up to
some extent.
RB4
Documentation
02
Documentation
completed
neatly.
Not up to
standard.
Moderate (4070%)
Limited
Appropriate & Full
Identification of
Identification of the
the Problem /
Problem &
Incomplete
Complete Solution
Solution for the
for the Problem
Problem
High (>70%)
Poor (0-40%)
Very Less Identification
of the Problem / Very
Less Solution for the
Problem
Concept is not clear
Proper format not
followed,
incomplete.
SIGN OF FACULTY
Computer Engineering Department,
L. D. College of Engineering, Ahmedabad-15
Introduction to AI and Machine Learning (114AG01)
L. D. College of Engineering, Ahmedabad
Department of Computer Engineering
LABORATORY PRACTICALS ASSESSMENT
Subject Name: Introduction to AI and Machine Learning (114AG01)
Term: 2022-2023
Enroll. No.:
Name:
Pract.
No.
RB1
RB2
RB3
RB4
Total
Date
Faculty
Sign
1
2
3
4
5
6
7
8
9
10
11
Computer Engineering Department,
L. D. College of Engineering, Ahmedabad-15
Practical - 1
Aim: Basics of Python Program
1. Write a program to Convert Celsius to Fahrenheit and vice –a-versa based on the
choice of user.
Code :
print("For Celsius to Fahrenhit Enter (1) and For Fahrenhit to Celsius Enter (2)")
choice = int(input())
if(choice == 1):
celsius = float(input())
fahernhit = (9*celsius)/5 +32
print(fahernhit)
else:
fahernhit = float(input())
celsius = (fahernhit - 32)*(5/9)
print(celsius)
Output:
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
2. Write a program in python to implement menu driven simple calculator.
Code :
print("Calculator:")
print("Enter (1) for Addition \nEnter (2) for Subtraction \nEnter (3) for
Multiplication \nEnter (4) for Division")
choice = int(input())
numOne = int(input())
numTwo = int(input())
if(choice==1):
Sum = numTwo+numOne
print(Sum)
elif(choice==2) :
Sub =numOne-numTwo
print(Sub)
elif(choice==3):
Multiplication = numOne*numTwo
print(Multiplication)
elif(choice==4):
division = numOne/numTwo
print(division)
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
3. Write a program which will allow user to enter 10 numbers and display largest odd
and even number from them. Also display the count of odd and even numbers.
Code :
import array as arr
numbers = [1,2,3,4,5,6,7,8,9,10]
print(numbers)
print(type(numbers))
a = arr.array('i',[])
even = 0
odd = 1
countEven = 0
countOdd = 0
for i in range(0,10):
n = int(input())
a.append(n)
for i in range(0,10):
# print(a[i])
if(a[i]%2 == 0 and a[i]>even):
even = a[i]
countEven += a[i]
elif(a[i]%2!=0 and a[i]>odd):
odd = a[i]
countOdd += a[i]
print(even)
print(odd)
print(countEven)
print(countOdd)
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
4. Write a Python program to check if the number provided by the user is a Prime
number. Perform it with and without user defined function.
Code :
number = int(input())
count=0
for i in range(2, number):
if (number % i == 0):
count+=1
if(count == 0):
print("This is a prime number")
else:
print("This is not a prime number")
Output :
5. Write a program to create a list of ten numbers entered by user and find smallest
and largest number from the list and display it.
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Code :
lst = []
large=0
small = 10000000000
for i in range(0,10):
n = int(input())
lst.append(n)
if(lst[i] > large):
large = lst[i]
if(lst[i] < small):
small = lst[i]
print(large)
print(small)
Output :
6. Write a program to create a tuple to students CPI and display it.
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Code :
cpi = (7.8,8,8.9, 8.5,9,9.1,9.7,8,9,10)
print(cpi)
Output :
7. Write a program to create a set of students’ enrolment number and sort them and
perform addition and deletion operation on it.
Code :
enrollment = {1,22,344,123,12}
print(enrollment)
print("Choose 1 for addition and choose 2 for deletion")
choice = int(input())
if(choice == 1):
number = int(input())
enrollment.add(number)
print(enrollment)
if(choice == 2):
enrollment.pop()
print(enrollment)
print("Sorted Set")
l = list(enrollment)
l.sort()
print(l)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Output :
8. Write a program to create a dictionary of student details- EnrollmentNo, Name,
Branch and perform addition and deletion of entry of key-value pair form it.
Code :
student = {'12': {'bhargavi': 'computer'}}
print(student)
student['14'] = {'meet': 'computer'}
print(student)
student['11'] = {'hasti': 'it'}
student['16'] = {'neeta': 'computer'}
print(student)
student.pop('11')
print(student)
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
9. Write a program to read and display content of the file. Also display number of
lines, words in the given file.
Code :
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 2
Aim: Study about NumPy, pandas, Scikit-learn and matplotlib
libraries.
Code :
import numpy as np
arr_1d=np.array([1,2,3,4,5,6,7])
arr_2d=np.array([[101,90],[102,85],[103,99],[104,78],[105,80]])
print(arr_1d)
print(arr_2d)
[1 2 3 4 5 6 7]
[[101 90]
[102 85]
[103 99]
[104 78]
[105 80]]
Shape of an array-number of elements in each dimension
print(arr_1d.shape)
print(arr_2d.shape)
(7,)
(5, 2)
Size of an array-total number of elements
print(arr_1d.size)
print(arr_2d.size)
7
10
Dimension of an array
print(arr_1d.ndim)
print(arr_2d.ndim)
210280107065
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BE/LDCE/CE/SEM-4
#to define dimension while declaring
arr_3d = np.array([1, 2, 3, 4], ndmin=3)
print(arr_3d)
1
2
[[[1 2 3 4]]]
Indexing
print(arr_1d[0])
print(arr_1d[1] + arr_1d[2])
print(arr_1d[-1])
print(arr_2d[0,1])
print(arr_2d[0][1])
print(arr_2d[1,-2])
1
5
7
90
90
102
Slicing
#arr[x:y] returns elements from index x to y-1
print(arr_1d[0:5])
print(arr_1d[-5:])
#with step
print(arr_1d[::2])
print(arr_1d[-5:-1:3])
#2d array
print(arr_2d[0:3,:])
print(arr_2d[0::2, 0:1])
[1 2 3 4 5]
[3 4 5 6 7]
[1 3 5 7]
[3 6]
[[101 90]
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
[102 85]
[103 99]]
[[101]
[103]
[105]]
Universal Function
#square root af an array
print(np.sqrt(arr_1d))
#absolute values
arr=np.array([-3,-2,-1,0,1,2,3])
print(np.absolute(arr))
#exponents & log
print(np.exp(arr))
print(np.log(arr))
#min/max
print(np.max(arr_1d))
print(np.min(arr_2d[0:,1:]))
#sign
print(np.sign(arr))
#trigonometry
print(np.sin(arr))
print(np.cos(arr))
Copy and View
copyarr=arr.copy()
viewarr=arr.view()
arr[0]=3
print(copyarr)
print(viewarr)
viewarr[0]=3
print(arr)
[ 3 -2 -1 0 1 2 3]
[ 3 -2 -1 0 1 2 3]
[ 3 -2 -1 0 1 2 3]
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Reshape - Increase and Flat
arr=np.array([1,2,3,4,5,6,7,8,9])
#1D to 2D
arr_1t2=arr.reshape(3,3)
print(arr_1t2)
#1D to 3D
arr_1t3=arr.reshape(3,1,3)
print(arr_1t3)
#3D to 1D
flatarr=arr_1t3.reshape(-1)
print(flatarr)
[[1 2 3]
[4 5 6]
[7 8 9]]
[[[1 2 3]]
[[4 5 6]]
[[7 8 9]]]
[1 2 3 4 5 6 7 8 9]
Sorting
#np.sort() - returns a sorted copy
arr = np.array([12,98,32,25,1])
print(np.sort(arr))
name = np.array(['Raj','Priya','Rahul','Aditi'])
print(np.sort(name))
[ 1 12 25 32 98]
['Aditi' 'Priya' 'Rahul' 'Raj']
Filter
filter_arr = []
for element in arr_1d:
# if the element is higher than 3, set the value to True, otherwise False:
if element > 3:
filter_arr.append(True)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
else:
filter_arr.append(False)
newarr = arr_1d[filter_arr]
print(filter_arr)
print(newarr)
#another way
filtered = arr_1d > 3
print(filtered)
print(arr_1d[filtered])
[False, False, False, True, True, True, True]
[4 5 6 7]
[False False False True True True True]
[4 5 6 7]
Pandas:
Pandas is a Python library for data manipulation and analysis.
It simplifies working with large datasets, enabling data cleaning, transformation,
and statistical analysis.
Its powerful data structures like Series and DataFrame make data readable and
relevant for decision-making.
import pandas as pd
import numpy as np
from numpy.random import randn
Series
A Series is a one-dimensional labeled array that can hold data of any type.
It is similar to a Python list but provides additional functionalities for data
analysis and manipulation.
result = [9.5,9.3,8.5,9.7,8.5,9,8.7,7,8,7.8]
res_series = pd.Series(result) #series a column in table
res_series
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
0 9.5
1 9.3
2 8.5
3 9.7
4 8.5
5 9.0
6 8.7
7 7.0
8 8.0
9 7.8
dtype: float64
#adding labels
sid = [101,102,103,104,105,106,107,108,109,110]
res_series = pd.Series(result,sid)
res_series
101 9.5
102 9.3
103 8.5
104 9.7
105 8.5
106 9.0
107 8.7
108 7.0
109 8.0
110 7.8
dtype: float64
res_series[102]
9.3
#from dictionary
grade =
{101:"AA",102:"AB",103:"BC",104:"AA",105:"BC",106:"AB",107:"",108:"",10
9:"",110:""}
grade_series = pd.Series(grade)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
grade_series
101 AA
102 AB
103 BC
104 AA
105 BC
106 AB
107
108
109
110
dtype: object
DataFrame
random_data = randn(3,3) #3rows&3cols
rows = ["A","B","C"]
cols = ["X","Y","Z"]
random_df = pd.DataFrame(random_data,rows,cols)
random_df
#import a csv file
df = pd.read_csv(‘iris.csv’)
df
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BE/LDCE/CE/SEM-4
#head(x) - returns first x rows (default 5)
df.head()
#tail(x) - returns last x rows (default 5)
df.tail(3)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
#information about data
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- ----0 Id 150 non-null int64
1 SepalLengthCm 150 non-null float64
2 SepalWidthCm 150 non-null float64
3 PetalLengthCm 150 non-null float64
4 PetalWidthCm 150 non-null float64
5 Species 150 non-null object
dtypes: float64(4), int64(1), object(1)
memory usage: 7.2+ KB
#statistics of stat
df.describe()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
df['Species'].value_counts()
Iris-setosa 50
Iris-versicolor 50
Iris-virginica 50
Name: Species, dtype: int64
df['Species'].value_counts(normalize=True)
Iris-setosa 0.333333
Iris-versicolor 0.333333
Iris-virginica 0.333333
Name: Species, dtype: float64
#add columns in data
age=np.zeros(150)
df['Age']=age
df.head(3)
df.insert(1,"Color",[np.nan]*len(df))
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
df.tail(3)
#condional selection
df[df['SepalLengthCm']>5.85].count()
Id 70
SepalLengthCm 70
SepalWidthCm 70
PetalLengthCm 70
PetalWidthCm 70
Species 70
dtype: int64
#multiple condition - &(and) |(or)
df[(df['PetalLengthCm']>3.75) & (df['PetalWidthCm']<1.2)]
#function
def cm_to_inch(x):
return x*0.394
usdf=df.copy()
columns_to_convert = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm',
'PetalWidthCm']
usdf[columns_to_convert] = df[columns_to_convert].apply(cm_to_inch)
usdf.rename(columns={col: col.replace('Cm', 'In') for col in
columns_to_convert}, inplace=True)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
usdf.head(3)
#sorting
df.sort_values("SepalLengthCm",ascending=False)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
MatPlotLib:
Matplotlib is a low level graph plotting library in python that serves as a
visualization utility.
import numpy as np
import pandas as pd
from numpy.random import randn
import matplotlib.pyplot as plt
random_data = pd.DataFrame(randn(50,3),columns=["A","B","C"])
random_data.tail(3)
Histograms:
plt.figure(figsize=(4,4))
plt.hist(random_data["B"])
plt.title("Histogram Example")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
#with grid
plt.figure(figsize=(4,4))
plt.hist(random_data)
plt.title("Histogram Example with Grid")
plt.grid()
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Areaplot:
plt.figure(figsize=(4,4))
plt.stackplot(range(len(random_data)),random_data["C"].values.T)
plt.title("Areaplot Example")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
plt.figure(figsize=(4,4))
plt.stackplot(range(len(random_data)),random_data.abs().values.T)
plt.title("Areaplot Example with absolute value")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
BarChart:
random_data["B"].plot.bar(figsize=(8,3))
plt.title("Barchart Example")
plt.show()
random_data.abs().plot.bar(figsize=(6,4),stacked=True)
plt.title("Barchart Example with stacked abs. data")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Line Charts:
random_data["A"].plot.line(figsize=(4,4))
plt.title("LineChart Example")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Scatterplot:
random_data.plot.scatter(x="A", y="B",figsize=(4,3))
plt.title("ScatterPlot Example")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
random_data.plot.scatter(x="A", y="B",c="C",cmap="cool",figsize=(5,3))
plt.title("ScatterPlot Example with color")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
BoxPlot:
#Presents the summary statistics (such as quartiles, median, and outliers)
random_data.plot.box(cmap="jet",figsize=(5,3))
plt.title("BoxPlot Example")
plt.grid()
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
HexPlots:
#used to visualize the density of points in a scatter plot
random_data.plot.hexbin(x="A",y="B",gridsize=15,figsize=(5,3))
plt.title("HexPlot Example")
plt.show()
KDE chart:
#Kernel Density Estimation - used to estimate the probability density function
(PDF)
random_data.plot.kde(figsize=(5,3))
plt.title("KDE Chart")
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
ScikitLearn:
Scikit-learn provides a wide range of tools and algorithms for various machine
learning tasks.
import numpy as np
import pandas as pd
Dataset Loading
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
print("\nFirst 5 rows of X:\n", X[:5])
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Splitting the data into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=42)
print(X.shape,end=" = ")
print(X_train.shape,end=" + ")
print(X_test.shape)
print(y.shape,end=" = ")
print(y_train.shape,end=" + ")
print(y_test.shape)
(150, 4) = (105, 4) + (45, 4)
(150,) = (105,) + (45,)
Preprocessing the data
#scaling the data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(X[0],end=" -> ")
print(X_scaled[0])
[5.1 3.5 1.4 0.2] -> [-0.90068117 1.01900435 -1.34022653 -1.3154443 ]
#normalization
from sklearn import preprocessing
#L1
data_normalized_l1 = preprocessing.normalize(X, norm='l1')
print("\nL1 normalized data:", data_normalized_l1[0])
#L2
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
data_normalized_l2 = preprocessing.normalize(X, norm='l2')
print("\nL1 normalized data:", data_normalized_l2[0])
L1 normalized data: [0.5 0.34313725 0.1372549 0.01960784]
L1 normalized data: [0.80377277 0.55160877 0.22064351 0.0315205 ]
Choosing a machine learning algorithm
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
Evaluating the model
from sklearn.metrics import accuracy_score
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
1.0
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 3
Aim: Getting Started with Python Logic Programming using Kanren and
SymPy packages.
Code :
Kanren:
Kanren enables the expression of relations and the search for values which satisfy
them. The following code is the "Hello, world!" of logic
programming. It asks for `1` number, `x`, such that `x == 5
Code:
from kanren import run, eq, membero, var, conde
x = var()
run(1, x, eq(x, 5))
(5,)
Sympy:
SymPy is a Python library for symbolic mathematics that provides a wide range of
capabilities, including solving algebraic equations, differentiation,
integration, and more. The following code uses SymPy to solve the equation 2*x + 1
= 5 for the variable x and prints the solution.
Code :
from sympy import symbols, Eq, solve
# Define the variables
x = symbols('x')
# Define the equation
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
equation = Eq(2*x + 1, 5)
# Solve the equation
solution = solve(equation, x)
print(solution)
Output:
[2]
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 4
Aim: Write the code in Kanren to demonstrate the followings: a) The use of
logical variables. b) The use of membero goal constructor.
Logical Variables in Kanren:
Logical variables in Kanren represent unknown values or entities in logical relationships. They
act as placeholders for values that need to be determined during the execution of the logic
program, allowing exploration of multiple possible solutions efficiently.
Membero Goal Constructor:
The membero goal in Kanren checks if an element belongs to a list or collection. It finds a value
for the logical variable representing the element, binding it to a valid value present in the list, or it
fails if no valid value is found.
Code :
from kanren import run,var,eq,conde
# Define a goal to find an element in a list using membero
def find_elemento(element, lst):
return membero(element, lst)
# Define some sample data and a logical variable
list_data = [1, 2, 3, 4, 5]
element_var = var()
# Find the logical variable that satisfies the goal
result = run(1, element_var, find_elemento(element_var, list_data))
print(result[0])
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 5
Aim: W rite the code in Kanren to create parent and grandparent relationships
and use it to state facts and query based on the facts.
Code :
from kanren import run, var, eq, conde
# Define the facts
def parent_relation(father, child):
return conde((eq(father, 'John'), eq(child, 'Alice')),
(eq(father, 'John'), eq(child, 'Bob')),
(eq(father, 'Bob'), eq(child, 'Charlie')),
(eq(father, 'Bob'), eq(child, 'David')),
(eq(father, 'David'), eq(child, 'Eva')))
# Define the grandparent relationship
def grandparent_relation(grandparent, grandchild):
parent = var()
return conde((parent_relation(grandparent, parent), parent_relation(parent,
grandchild)))
# Example facts
facts = conde(parent_relation('John', 'Alice'),
parent_relation('John', 'Bob'),
parent_relation('Bob', 'Charlie'),
parent_relation('Bob', 'David'),
parent_relation('David', 'Eva'))
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Jethva Bhargavi
BE/LDCE/CE/SEM-4
x = var()
john_children = run(0, x, parent_relation('John', x))
print("Children of John:", john_children)
y = var()
john_grandchildren = run(0, y, grandparent_relation('John', y))
print("Grandchildren of John:", john_grandchildren)
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 6
Aim: Write the code in Kanren to demonstrate the constraint system.
Code :
def Sum(input_list, iterator, num, counter):
if num <= iterator:
return counter
counter += input_list[iterator]
counter = Sum(input_list, iterator + 1, num, counter)
return counter
input_list = [6, 4, 8, 2, 9]
counter = 0
num = len(input_list)
print(Sum(input_list, 0, num, counter))
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 7
Aim: Write the code in Kanren to match the mathematical expressions.
Code :
from kanren import run, eq, var, conde
def expressiono(x):
a, b = var(), var()
return conde(
(eq(x, 'number')),
(eq(x, 'variable')),
(eq(x, ('sum', a, b))),
(eq(x, ('product', a, b)))
)
expression1 = ('sum', 2, 3)
#2+3
expression2 = ('product', 5, 'x')
#5*x
expression3 = ('sum', 'x', ('product', 2, 4)) # x + (2 * 4)
result = run(1, x, expressiono(x))
print(result[0])
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 8
Aim: Write the code in python to implement linear regression for one variable.
Code :
import numpy as np
import matplotlib.pyplot as plt
def estimate_coef(x, y):
# number of observations/points
n = np.size(x)
# mean of x and y vector
m_x = np.mean(x)
m_y = np.mean(y)
# calculating cross-deviation and deviation about x
SS_xy = np.sum(y*x) - n*m_y*m_x
SS_xx = np.sum(x*x) - n*m_x*m_x
# calculating regression coefficients
b_1 = SS_xy / SS_xx
b_0 = m_y - b_1*m_x
return (b_0, b_1)
def plot_regression_line(x, y, b):
# plotting the actual points as scatter plot
plt.scatter(x, y, color = "m",
marker = "o", s = 30)
# predicted response vector
y_pred = b[0] + b[1]*x
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
# plotting the regression line
plt.plot(x, y_pred, color = "g")
# putting labels
plt.xlabel('x')
plt.ylabel('y')
# function to show plot
plt.show()
def main():
# observations / data
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])
# estimating coefficients
b = estimate_coef(x, y)
print("Estimated coefficients:\nb_0 = {} \
\nb_1 = {}".format(b[0], b[1]))
# plotting regression line
plot_regression_line(x, y, b)
if __name__ == "__main__":
main()
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 9
Aim: Write the code in python to implement linear regression using gradient
descent for one variable.
Code :
import numpy as np
import matplotlib.pyplot as plt
def linear_regression_gradient_descent(X, y, learning_rate=0.01,
num_iterations=1000):
"""
Perform linear regression using gradient descent for one variable.
Parameters:
X (array-like): Input features (independent variable).
y (array-like): Target values (dependent variable).
learning_rate (float): The learning rate for gradient descent. Default is
0.01.
num_iterations (int): The number of iterations for gradient descent. Default
is 1000.
Returns:
theta (float): The learned slope of the linear regression line.
intercept (float): The learned intercept of the linear regression line.
cost_history (list): The history of cost (loss) values during gradient
descent.
"""
m = len(y) # Number of data points
theta = 0.0
intercept = 0.0
cost_history = []
for _ in range(num_iterations):
# Make predictions
y_pred = theta * X + intercept
# Calculate the cost (mean squared error)
cost = np.sum((y_pred - y) ** 2) / (2 * m)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
cost_history.append(cost)
# Compute the gradients
d_theta = np.sum((y_pred - y) * X) / m
d_intercept = np.sum(y_pred - y) / m
# Update parameters using gradient descent
theta -= learning_rate * d_theta
intercept -= learning_rate * d_intercept
return theta, intercept, cost_history
# Sample data for demonstration
X = np.array([1, 2, 3, 4, 5])
y = np.array([3, 6, 4, 8, 7])
# Perform linear regression
theta, intercept, cost_history = linear_regression_gradient_descent(X, y)
# Print the learned parameters
print(f"Slope (Theta): {theta}")
print(f"Intercept: {intercept}")
# Plot the cost history
plt.plot(range(len(cost_history)), cost_history)
plt.xlabel('Iterations')
plt.ylabel('Cost')
plt.title('Cost History during Gradient Descent')
plt.show()
Output :
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 10
Aim: Write the code in python to implement logistic regression for single class
classification.
Code :
Program:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn import metrics
Titanic Dataset
• PassengerId: A unique identif ier for each passenger.
• Survived: Indicates whether the passenger survived or not (0 = No, 1 = Yes).
• Pclass: The ticket class of the passenger (1 = 1st class, 2 = 2nd class, 3 = 3rd
class).
• Name: The name of the passenger.
• Sex: The gender of the passenger (male or female).
• Age: The age of the passenger in years.
• SibSp: The number of siblings/spouses aboard the Titanic.
• Parch: The number of parents/children aboard the Titanic.
• Ticket: The ticket number.
• Fare: The fare paid for the ticket.
• Cabin: The cabin number of the passenger.
• Embarked: The port of embarkation (C = Cherbourg, Q = Queenstown, S =
Southampton).
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
#loading the data
df = pd.read_csv("Titanic.csv")
df.head()
df.info()
Data Preprocessing
Removing Unnecessary Columns
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
The "PassengerID", "Name", "Ticket", and "Embarked" columns are removed as
they are not useful for survival prediction.
df.drop(["PassengerId", "Name", "Ticket", "Embarked"], axis=1, inplace=True)
df.info()
Cabin feature has a large number(327) of missing values and may not provide
signif icant predictive power for survival outcomes.
df.drop(["Cabin"],axis=1,inplace=True)
df.info()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Converting "sex" Column to Integer - "male" to 1 and "female" to 0
df['Sex']=df['Sex'].apply(lambda x: 1 if x=='male' else 0)
df.head(3)
Filling the missing values
The "age" and "fare" features have 86 and 1 null values, respectively. These values
can be replaced by mean, median, or mode.
df['Age'].fillna(df['Age'].mean(),inplace=True)
df['Age'].isna().sum()
0
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
df['Fare'].fillna(df['Fare'].mean(),inplace=True)
df['Fare'].isna().sum()
0
Feature Selection
Defining dependent and independent variables
X = df.drop(['Survived'],axis=1)
y = df['Survived']
print(X.shape,y.shape)
(418, 6) (418,)
Spliting data into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
print("Training set Shape : ",X_train.shape,y_train.shape)
print("Testing set Shape : ",X_test.shape,y_train.shape)
Training set Shape : (334, 6) (334,)
Testing set Shape : (84, 6) (334,)
Model Training
Create and f it the Logistic Regression model
model = LogisticRegression()
model.fit(X_train,y_train)
Crete the predictions on the testing set
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
y_pred = model.predict(X_test)
Model Evaluation
accuracy = accuracy_score(y_test,y_pred)
print("Accuracy: ",accuracy)
Accuracy: 1.0
print(metrics.classification_report(y_test,y_pred))
import seaborn as sn
plt.figure(figsize = (5,5))
sn.heatmap(confusion_matrix(y_test,y_pred), annot=True, cmap="magma")
plt.xlabel('Predicted')
plt.ylabel('Truth')
coefficients = model.coef_[0]
feature_names = X.columns
plt.barh(feature_names, coefficients)
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Coefficient Plot')
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Practical - 11
Aim: Write the code in python to implement logistic regression for multi class
classification.
Code :
import pandas as pd
import numpy as mp
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
Iris Dataset
•
•
•
•
•
•
Id: A unique identif ier for each entry.
SepalLengthCm: Sepal length in centimeters.
SepalWidthCm: Sepal width in centimeters.
PetalLengthCm: Petal length in centimeters.
PetalWidthCm: Petal width in centimeters.
Species: The target variable indicating the species of Iris f lowers (setosa,
versicolor, virginica).
#loading the data
df = pd.read_csv("Iris.csv")
df.head(3)
df.info()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Data Preprocessing
Removing Unnecessary Columns
The "Id" column is removed as it is not useful for species prediction.
df.drop(['Id'],axis=1,inplace=True)
df.info()
Converting "Species" Column to Integer
df['Species'].unique()
array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)
Let's set
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
• Iris-setosa as 1
• Iris-versicolor as 2
• Iris-virginica as 3
df['Species']=df['Species'].apply(lambda x: 1 if x=="Iris-setosa" else 2 if x=="Irisversicolor" else 3)
df['Species'].unique()
array([1, 2, 3])
Feature Selection
Def ining dependent and independent variables
X = df.drop(['Species'],axis=1)
y = df['Species']
print(X.shape,y.shape)
(150, 4) (150,)
Spliting data into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
print("Training set Shape : ",X_train.shape,y_train.shape) print("Testing set Shape :
",X_test.shape,y_train.shape)
Training set Shape : (120, 4) (120,)
Testing set Shape : (30, 4) (120,)
Model Training
Create and f it the Logistic Regression model
model = LogisticRegression()
model.fit(X_train,y_train)
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
Crete the predictions on the testing set
y_pred = model.predict(X_test)
Model Evaluation
accuracy = accuracy_score(y_test,y_pred)
print("Accuracy: ",accuracy)
Accuracy: 1.0
print(metrics.classification_report(y_test,y_pred))
plt.figure(figsize = (7,5)) sns.heatmap(confusion_matrix(y_test,y_pred), annot=True,
cmap="magma")
plt.xlabel('Predicted')
plt.ylabel('Truth')
Text(58.222222222222214, 0.5, 'Truth')
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
coefficients = model.coef_[0]
feature_names = X.columns
plt.barh(feature_names, coefficients)
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Coefficient Plot')
plt.show()
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4
210280107065
Jethva Bhargavi
BE/LDCE/CE/SEM-4