INDEX
Sr. Practical Page Date Sign
No. No.
1 Write a program to Implementation of mean, 1
median and mode
2 Write a program to implement Data distribution 2
histogram.
3 Write a program to implement scatter plot using 3
given dataset
4 Write a program to Implementation of linear 4
regression from given dataset
5 Write a program to implement Scale 5
6 Write a program to training and testing from given 6
dataset
7 Write a program to Implementation of Decision 7
tree from given dataset
8 Write a program to Implement K-Nearest 8
Neighbors Algorithm from given dataset
9 Write a program to implementation of K- Mean 9
clustering from given dataset
10 10
Write a program to implementation of hierarchical
clustering from dataset
Machine Learning [3170724] 191390107018
Practical-1
Aim: - Write a program to Implementation of mean, median and mode.
Code :-
import statistics
# Input: Read a list of numbers from the user
num_list = input("Enter a list of numbers separated by spaces: ").split()
num_list = [int(num) for num in num_list]
# Calculate the mean
mean = statistics.mean(num_list)
print(f"Mean: {mean}")
# Calculate the median
median = statistics.median(num_list)
print(f"Median: {median}")
# Calculate the mode with error handling
try:
mode = statistics.mode(num_list)
print(f"Mode: {mode}")
except statistics.StatisticsError:
print("No unique mode found.")
Output: -
BAIT,Surat 1
Machine Learning [3170724] 191390107018
Practical-2
Aim: - Write a program to implement Data distribution histogram.
Code :-
import matplotlib.pyplot as plt
import numpy as np
data = np.random.normal(0, 1, 1000) # Generate 1000 random data points with mean 0 and
standard deviation 1
plt.hist(data, bins=20, edgecolor='k') # You can adjust the number of bins as needed
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Data Distribution Histogram')
plt.show()
Output: -
BAIT,Surat 2
Machine Learning [3170724] 191390107018
Practical-3
Aim: - Write a program to implement scatter plot using given dataset.
Code :-
import matplotlib.pyplot as plt
x1 = [90, 46, 38, 40, 98, 12, 68, 36, 40, 22]
y1 = [24, 48, 6, 38, 68, 98, 56, 74, 60, 12]
x2 = [28, 30, 50, 66, 8, 6, 38, 68, 74, 42]
y2 = [28, 36, 95, 36, 40, 22, 58, 4, 50, 18]
plt.scatter(x1, y1, c ="black", linewidths=2, marker="s",edgecolor="green", s=50)
plt.scatter(x2, y2, c ="yellow", linewidths=2, marker="^", edgecolor="red", s=200)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()
Output: -
BAIT,Surat 3
Machine Learning [3170724] 191390107018
Practical-4
Aim: - Write a program to Implementation of linear regression from given dataset.
Code :-
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X = np.array([2, 4, 6, 8, 10]).reshape(-1, 1)
y = np.array([3, 6, 9, 12, 15])
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)
slope = model.coef_[0]
intercept = model.intercept_
print(f"Slope: {slope}")
print(f"Intercept:
{intercept}")
plt.scatter(X, y, label='Data', color='black')
plt.plot(X, y_pred, label='Linear Regression',
color='blue') plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression
Example') plt.legend()
plt.show()
Output: -
BAIT,Surat 4
Machine Learning [3170724] 191390107018
Practical-5
Aim: - Write a program to implement Scale.
Code :-
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import numpy as np
# Input data
data = np.array([
[2.0, 4.0, 6.0],
[3.0, 6.0, 9.0],
[4.0, 8.0, 12.0]
])
# Apply Min-Max Scaling
min_max_scaler = MinMaxScaler()
scaled_data_minmax = min_max_scaler.fit_transform(data)
# Apply Standard Scaling
standard_scaler = StandardScaler()
scaled_data_standard = standard_scaler.fit_transform(data)
# Print the results
print("Min-Max Scaled Data:")
print(scaled_data_minmax)
print("\nStandardized Data:")
print(scaled_data_standard)
Output: -
BAIT,Surat 5
Machine Learning [3170724] 191390107018
Practical-6
Aim: - Write a program to training and testing from given dataset.
Code :-
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np
# Data
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 5, 4, 5])
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train the Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Predict the target values for the test set
y_pred = model.predict(X_test)
# Calculate the mean squared error
mse = mean_squared_error(y_test, y_pred)
# Get the slope and intercept
slope = model.coef_[0]
intercept = model.intercept_
# Print the results
print(f"Slope: {slope}")
print(f"Intercept: {intercept}")
print(f"Mean Squared Error: {mse}")
Output: -
BAIT,Surat 6
Machine Learning [3170724] 191390107018
Practical-7
Aim: - Write a program to Implementation of Decision tree from given dataset.
Code :-
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
# Load the Iris dataset
data = load_iris()
X = data.data
y = data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train the DecisionTreeClassifier model
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
# Predict the target values for the test set
y_pred = clf.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
# Generate the classification report
report = classification_report(y_test, y_pred)
print("Classification Report:")
print(report)
Output: -
BAIT,Surat 7
Machine Learning [3170724] 191390107018
Practical-8
Aim: - Write a program to Implement K-Nearest Neighbors Algorithm from given dataset.
Code :-
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
data = load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
report = classification_report(y_test, y_pred, target_names=data.target_names)
print("Classification Report:")
print(report)
confusion = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(confusion)
Output: -
BAIT,Surat 8
Machine Learning [3170724] 191390107018
Practical-9
Aim: - Write a program to implementation of K- Mean clustering from given dataset.
Code :-
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=300, centers=3, random_state=42)
means = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)
labels = kmeans.labels_
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', label='Centroids',
marker='x')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('K-Means Clustering (k=3)')
plt.legend()
plt.show()
Output: -
BAIT,Surat 9
Machine Learning [3170724] 191390107018
Practical-10
Aim: - Write a program to implementation of hierarchical clustering from dataset.
Code :-
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.datasets import make_circles
# Generate synthetic data
X, _ = make_circles(n_samples=30, factor=0.5, noise=0.05, random_state=42)
# Perform hierarchical/agglomerative clustering using 'single' linkage method
linkage_matrix = linkage(X, method='single') # You can use different linkage methods
# Plot the dendrogram
dendrogram(linkage_matrix)
# Add title and labels to the plot
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Data Points')
plt.ylabel('Distance')
# Show the plot
plt.show()
Output: -
BAIT,Surat 10
