Project File
“Machine Learning with Python”
Department
Of
Computer Science and Engineering
2023-24
Submitted To: Submitted By:
Miss Surbhi Name: Karmbir
Prof. Registration: 181261103032
CSE, IGU, Meerpur MCA 3rd Sem.
Rewari, Haryana 2022-2024
Name= karmbir
roll number=19 (181261103032)
# 1. Draw the star patern as following:
*
**
***
****
*****
****
***
**
*
Solu�on:
n=5
for i in range(1,n+1):
print("*"*i)
for i in range(n-1,0,-1):
print("*"*i)
Output:
Name= karmbir
roll number=19 (181261103032)
#2 Write a program to replace vowel from the given string:
Solu�on:
str = "This is a string."
new_str = str.replace("a", "*")
print(new_str)
Output:
Name= karmbir
roll number=19 (181261103032)
#3 Write a program to read a iris dataset:
Solu�on:
import pandas as pd
import numpy as np
data=pd.read_csv("IRIS.csv")
data.head()
Output:
Name= Karmbir
roll number=19 (181261103032)
#4 Write a program to reverse a number:
Solu�on:
n=int(input("Enter number: "))
rev=0
while(n>0):
dig=n%10
rev=rev*10+dig
n=n//10
print("Reverse of the number:",rev)
Output:
Name= Karmbir
roll number=19 (181261103032)
#5 Write a program to mul�ply two array using numpy:
Solu�on:
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
mul�ply=array1*array2
mul�ply
Output:
Name= Karmbir
roll number=19 (181261103032)
#6 Draw a graph using matplotlib in 2-D:
Solu�on:
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5] # X-axis values
y = [2, 4, 6, 8, 10] # Y-axis values
plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.�tle("Simple 2-D Plot")
plt.show()
Output:
Name= Karmbir
roll number=19 (181261103032)
#7 Draw a graph using matplotlib in 3-D:
Solu�on:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projec�on='3d')
Output:
Name= Karmbir
roll number=19 (181261103032)
#8 draw a pie chart using matplotlib
Solu�on:
import matplotlib.pyplot as plt
import numpy as np
y = np.array([35, 25, 25, 15])
plt.pie(y)
plt.show()
OutPut:
Name= Karmbir
roll number=19 (181261103032)
#9 Split the iris dataset in the ra�o of 70:30
Solu�on:
import sklearn.datasets as ds
import sklearn.model_selec�on as ms
iris = ds.load_iris()
X_train, X_test, y_train, y_test = ms.train_test_split(iris.data, iris.target,
test_size=0.3)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape[0])
print(y_test.shape[0])
Output:
Name= Karmbir
roll number=19 (181261103032)
#10 Implement linear regression:
Solu�on:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
X = np.array([1, 2, 3, 6, 8]).reshape(-1, 1)
y = np.array([2, 4, 5, 7, 9])
# Create a linear regression model
model = LinearRegression()
# Fit the model to the data
model.fit(X, y)
# predic�ons
y_pred = model.predict(X)
# Plot the data and the linear regression line
plt.scater(X, y, label='Actual Data')
plt.plot(X, y_pred, color='red', label='Linear Regression Line')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.legend()
plt.show()
print(f'Intercept (b0): {model.intercept_}')
print(f'Coefficient (b1): {model.coef_}')
Output:
Name= Karmbir
roll number=19 (181261103032)
#11 Implement logis�c regression to find accuracy:
Solu�on:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selec�on import train_test_split
from sklearn.linear_model import Logis�cRegression
from sklearn.metrics import accuracy_score, confusion_matrix
# Generate some sample data for binary classifica�on
X = np.random.rand(100, 2) # Independent variables
y = (X[:, 0] + X[:, 1] > 1).astype(int) # Binary target variable
# Split the data into training and tes�ng sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
# Create a logis�c regression model
model = Logis�cRegression()
# Fit the model to the training data
model.fit(X_train, y_train)
# Make predic�ons on the tes�ng data
y_pred = model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
# Create a confusion matrix
confusion = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:')
print(confusion)
# Plot the decision boundary (op�onal)
plt.scater(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.Paired)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),
np.linspace(ylim[0], ylim[1], 50))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.5)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.�tle('Logis�c Regression Decision Boundary')
plt.show()
Output:
Name= Karmbir
roll number=19 (181261103032)
#12 Implement decision tree to find accuracy:
Solu�on:
# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selec�on import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
# Generate some sample data for binary classifica�on
X = np.random.rand(100, 2) # Independent variables
y = (X[:, 0] + X[:, 1] > 1).astype(int) # Binary target variable
# Split the data into training and tes�ng sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a decision tree classifier
model = DecisionTreeClassifier()
# Fit the model to the training data
model.fit(X_train, y_train)
# Make predic�ons on the tes�ng data
y_pred = model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
# Create a confusion matrix
confusion = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:')
print(confusion)
# Plot the decision tree (op�onal)
plt.scater(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.Paired)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),
np.linspace(ylim[0], ylim[1], 50))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.5)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.�tle('Decision Tree Decision Boundary')
plt.show()
Output:
Name= Karmbir
roll number=19 (181261103032)
#13 Implement SVM to find accuracy:
Solu�on:
import numpy as np
from sklearn import datasets
from sklearn.model_selec�on import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Split the data into training and tes�ng sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create an SVM classifier
svm_classifier = SVC(kernel='linear')
# Train the SVM classifier on the training data
svm_classifier.fit(X_train, y_train)
# Make predic�ons on the test data
y_pred = svm_classifier.predict(X_test)
# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
Output:
Name= Karmbir
roll number=19 (181261103032)
#14 Implement k- means algorithem:
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# some sample data
data = np.array([[1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]])
# Specify the number of clusters (k)
k=2
kmeans = KMeans(n_clusters=k)
# Fit the data to the KMeans model
kmeans.fit(data)
centroids = kmeans.cluster_centers_
labels = kmeans.labels_
# Visualize the data and cluster centers
colors = ["g.", "r.", "c.", "y."]
for i in range(len(data)):
plt.plot(data[i][0], data[i][1], colors[labels[i]], markersize=10)
plt.scater(centroids[:, 0], centroids[:, 1], marker="x", s=200, linewidths=3, color="k")
plt.show()
Output:
Name= Karmbir
roll number=19 (181261103032)
#15 Draw a tree using turtle:
import turtle
# Create a Turtle screen
screen = turtle.Screen()
screen.bgcolor("white")
# Create a Turtle object
t = turtle.Turtle()
t.speed(0) # Set the drawing speed (0 is the fastest)
# Define a recursive func�on to draw branches of the tree
def draw_branch(branch_length, t):
if branch_length > 5:
# Draw the current branch
t.forward(branch_length)
# Right branch
t.right(20)
draw_branch(branch_length - 15, t)
# Le� branch
t.le�(40)
draw_branch(branch_length - 15, t)
# Return to the original posi�on
t.right(20)
t.backward(branch_length)
# Set the ini�al posi�on and heading
t.up()
t.goto(0, -200)
t.setheading(90)
t.down()
draw_branch(100, t)
screen.exitonclick()
Output:
