Machine Learning Lab (Practice)

 

Machine Learning Lab (AM505PC) syllabus for B.Tech III Year I Semester.

Vision and Mission of ACE Engineering College

Vision

To build a strong, dynamic, and inclusive alumni network that contributes meaningfully to the academic excellence, professional growth, and societal impact of ACE Engineering College.

Mission

• To foster lifelong relationships between alumni and the institution.
• To promote continuous interaction among alumni, students, faculty, and management.
• To encourage alumni participation in academic, technical, and career-oriented activities.
• To support institutional development through knowledge sharing, mentoring, and resource mobilization.
• To uphold the values, traditions, and reputation of ACE Engineering College.

Vision and Mission of AI and ML (CSM)

Vision of the Department

To be an epicentre of excellence in education by offering cutting-edge technologies, research, and product-based opportunities to students, enabling them to succeed in global professional competitions with a foundation of core knowledge, entrepreneurial skills, ethical values, and social responsibility.

Mission of the Department

Imparting quality technical education to young computer engineers by providing them

• M1: Impart quality technical education with state-of-the-art laboratories, analytical and core technical skills of international standards, delivered by qualified and experienced faculty.
• M2: Prepare students for global professional competitions, examinations for higher studies, and employment in product-based companies.
• M3: Develop professional attitudes, research aptitude, critical reasoning, problem-solving skills, and technical consultancy capabilities by providing training in cutting-edge technologies.
• M4: Promote and nurture knowledge, lifelong learning, entrepreneurial practices, ethical values, and social responsibility.

Programme Educational Objectives (PEOs)

PEO1: Equip students with strong foundations in mathematics, statistics, programming, and machine learning for solving real-world problems.

PEO2: Enable students to design, develop, and implement machine learning models using modern programming languages and software tools.

PEO3: Develop analytical thinking, problem-solving abilities, and data-driven decision-making skills through practical laboratory experiments.

PEO4: Prepare students for higher education, research, entrepreneurship, and successful careers in Artificial Intelligence, Machine Learning, and Data Science.

PEO5: Encourage lifelong learning, professional ethics, teamwork, and effective communication in multidisciplinary environments.

Programme Outcomes (POs)

After successful completion of the Machine Learning Laboratory, students will be able to:

PO1: Apply knowledge of mathematics, statistics, computer science, and engineering fundamentals to machine learning problems.

PO2: Identify, formulate, analyze, and solve data-driven problems using appropriate machine learning techniques.

PO3: Design and implement machine learning models for prediction, classification, clustering, and regression tasks.

PO4: Conduct experiments, analyze datasets, interpret results, and draw meaningful conclusions.

PO5: Use modern engineering tools, programming languages, libraries, and frameworks such as Python, NumPy, Pandas, SciPy, Matplotlib, and Scikit-learn.

PO6: Understand the impact of machine learning solutions in societal, environmental, and industrial contexts.

PO7: Apply ethical principles while handling data and developing intelligent systems.

PO8: Function effectively as an individual and as a member or leader in multidisciplinary teams.

PO9: Communicate technical concepts, experimental findings, and project results effectively.

PO10: Recognize the importance of lifelong learning and adapt to emerging technologies in AI and Machine Learning.

Programme Specific Outcomes (PSOs)

After completing the Machine Learning Laboratory, students will be able to:

PSO1: Perform statistical analysis using Python libraries including Statistics, NumPy, SciPy, and Math.

PSO2: Apply data preprocessing, visualization, and exploratory data analysis using Pandas and Matplotlib.

PSO3: Develop supervised learning models such as Linear Regression, Multiple Linear Regression, Logistic Regression, Decision Trees, and K-Nearest Neighbors.

PSO4: Implement unsupervised learning techniques such as K-Means Clustering for data grouping and pattern discovery.

PSO5: Evaluate and compare machine learning algorithms using appropriate performance metrics and parameter tuning techniques.

PSO6: Build complete machine learning solutions by integrating data preprocessing, model training, testing, evaluation, and visualization.

Course Objective

• To provide an overview of various machine learning techniques and demonstrate them using Python.

Laboratory Course Outcomes (COs)

CO1	Apply Python programming and scientific libraries for statistical computations and data analysis.
CO2	Perform data preprocessing, visualization, and exploratory analysis using Pandas and Matplotlib.
CO3	Develop and evaluate regression models for prediction problems using Scikit-learn.
CO4	Implement classification algorithms including Decision Tree, KNN, and Logistic Regression.
CO5	Apply clustering techniques such as K-Means to discover hidden patterns in datasets.
CO6	Analyze and compare the performance of different machine learning algorithms through a mini project.

List of Experiments

Exp. No.	Experiment
1	Write a Python program to compute Central Tendency Measures (Mean, Median, Mode) and Measures of Dispersion (Variance, Standard Deviation).
2	Study Python basic libraries such as Statistics, Math, NumPy, and SciPy.
3	Study Python libraries for ML applications such as Pandas and Matplotlib.
4	Write a Python program to implement Simple Linear Regression.
5	Implement Multiple Linear Regression for House Price Prediction using scikit-learn.
6	Implement a Decision Tree using scikit-learn and perform parameter tuning.
7	Implement K-Nearest Neighbors (KNN) using scikit-learn.
8	Implement Logistic Regression using scikit-learn.
9	Implement K-Means Clustering.
10	Performance analysis of Classification Algorithms on a specific dataset (Mini Project).

Text Book

• Tom M. Mitchell – Machine Learning (MGH).

Reference Book

• Stephen Marsland – Machine Learning: An Algorithmic Perspective (Taylor & Francis).

 

 

Install python

 https://youtu.be/rgf0-Uypb28?si=SWeb1LZo_3ziqO08

 

 

 

 

LAB 1

  

 1. Find mean of numbers?

numbers = [10, 20, 30, 40, 50]

mean = sum(numbers) / len(numbers)

print("Mean =", mean)

 OUTPUT

Mean = 30.0

2. Find median of numbers?

# Program to find Median

numbers = [10, 20, 30, 40, 50]

numbers.sort()

n = len(numbers)

if n % 2 == 0:

    median = (numbers[n//2 - 1] + numbers[n//2]) / 2

else:

    median = numbers[n//2]

print("Median =", median)

 OUTPUT

Median = 30

3. Find mode of numbers?

# Program to find Mode

numbers = [10, 20, 20, 30, 40, 20, 50]

mode = max(set(numbers), key=numbers.count)

print("Mode =", mode)

 OUTPUT

Mode = 20

4. Find Variance of numbers?

# Program to find Variance

numbers = [10, 20, 30, 40, 50]

mean = sum(numbers) / len(numbers)

variance = sum((x - mean) ** 2 for x in numbers) / len(numbers)

print("Variance =", variance)

 OUTPUT

Variance = 200.0

 5. Find standard deviation of numbers?

# Program to find Standard Deviation

numbers = [10, 20, 30, 40, 50]

mean = sum(numbers) / len(numbers)

variance = sum((x - mean) ** 2 for x in numbers) / len(numbers)

std_deviation = variance ** 0.5

print("Standard Deviation =", std_deviation)

 OUTPUT

Standard Deviation = 14.142135623730951

 6. Find mean, median, mode, variance and standard deviation of numbers?

# Experiment 1

# Program to compute Mean, Median, Mode, Variance, and Standard Deviation

import statistics

# Input numbers from user

data = list(map(float, input("Enter numbers separated by space: ").split()))

# Mean

mean = statistics.mean(data)

# Median

median = statistics.median(data)

# Mode

try:

    mode = statistics.mode(data)

except statistics.StatisticsError:

    mode = "No unique mode"

# Variance

variance = statistics.variance(data)

# Standard Deviation

std_dev = statistics.stdev(data)

# Display Results

print("\n----- Statistical Measures -----")

print("Mean =", mean)

print("Median =", median)

print("Mode =", mode)

print("Variance =", variance)

print("Standard Deviation =", std_dev)

 OUTPUT

Enter numbers separated by space: 5 6 4 8 5 9 4 5 6 8 9 2 1 45

----- Statistical Measures -----

Mean = 8.357142857142858

Median = 5.5

Mode = 5.0

Variance = 117.01648351648352

Standard Deviation = 10.817415750376036

LAB 2 

 1. Find mean with statistics library?

# Study of Statistics Library

import statistics

data = [10, 20, 30, 40, 50]

print("Mean:", statistics.mean(data))

print("Median:", statistics.median(data))

print("Mode:", statistics.mode(data))

print("Variance:", statistics.variance(data))

print("Standard Deviation:", statistics.stdev(data))

 OUTPUT

Mean: 30

Median: 30

Mode: 10

Variance: 250

Standard Deviation: 15.811388300841896

 2. Write a simple program using math library?

# Study of Math Library

import math

num = 25

print("Square Root:", math.sqrt(num))

print("Power (5^2):", math.pow(5, 2))

print("Factorial of 5:", math.factorial(5))

print("Value of Pi:", math.pi)

print("Value of e:", math.e)

print("Ceil of 4.3:", math.ceil(4.3))

print("Floor of 4.7:", math.floor(4.7))

 OUTPUT

Square Root: 5.0

Power (5^2): 25.0

Factorial of 5: 120

Value of Pi: 3.141592653589793

Value of e: 2.718281828459045

Ceil of 4.3: 5

Floor of 4.7: 4

 3. Write a simple python program using scipy library?

# Study of SciPy Library

from scipy import stats

data = [10, 20, 30, 40, 50]

print("Mean:", stats.tmean(data))

print("Variance:", stats.tvar(data))

print("Standard Deviation:", stats.tstd(data))

 OUTPUT

Mean: 30.0 Variance: 250.0 Standard Deviation: 15.811388300841896

 4. Write a simple program using numpy?

# Study of NumPy Library

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print("Array:", arr)

print("Sum:", np.sum(arr))

print("Mean:", np.mean(arr))

print("Maximum:", np.max(arr))

print("Minimum:", np.min(arr))

 OUTPUT

Array: [10 20 30 40 50]

Sum: 150

Mean: 30.0

Maximum: 50

Minimum: 10

 5. Write a python program using statistics, math, scipy and numpy?

# Experiment 2

# Study of Python Libraries: Statistics, Math, NumPy, and SciPy

import statistics

import math

import numpy as np

from scipy import stats

# Sample Data

data = [10, 20, 30, 40, 50]

# Statistics Library

print("=== Statistics Library ===")

print("Mean:", statistics.mean(data))

print("Median:", statistics.median(data))

print("Mode:", statistics.mode(data))

# Math Library

print("\n=== Math Library ===")

print("Square Root of 25:", math.sqrt(25))

print("Factorial of 5:", math.factorial(5))

print("Value of Pi:", math.pi)

# NumPy Library

arr = np.array(data)

print("\n=== NumPy Library ===")

print("Array:", arr)

print("Sum:", np.sum(arr))

print("Average:", np.mean(arr))

print("Maximum:", np.max(arr))

print("Minimum:", np.min(arr))

# SciPy Library

print("\n=== SciPy Library ===")

print("Mean:", stats.tmean(data))

print("Variance:", stats.tvar(data))

print("Standard Deviation:", stats.tstd(data))

 OUTPUT

=== Statistics Library === Mean: 30 Median: 30 Mode: 10 === Math Library === Square Root of 25: 5.0 Factorial of 5: 120 Value of Pi: 3.141592653589793 === NumPy Library === Array: [10 20 30 40 50] Sum: 150 Average: 30.0 Maximum: 50 Minimum: 10 === SciPy Library === Mean: 30.0 Variance: 250.0 Standard Deviation: 15.811388300841896

 LAB 3

 1. Write a python program using pandas library?

# Study of Pandas Library

import pandas as pd

data = {

    "Name": ["Alice", "Bob", "Charlie"],

    "Marks": [85, 90, 78]

}

df = pd.DataFrame(data)

print(df)

 OUTPUT

      Name  Marks

0    Alice     85

1      Bob     90

2  Charlie     78

 2. Write a python program using matplotlib library?

# Study of Matplotlib Library

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 20, 30, 40, 50]

plt.plot(x, y)

plt.title("Simple Line Graph")

plt.xlabel("X-axis")

plt.ylabel("Y-axis")

plt.show()

 OUTPUT

 3. line graph

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 20, 30, 40, 50]

plt.plot(x, y)

plt.title("Line Graph")

plt.xlabel("X-axis")

plt.ylabel("Y-axis")

plt.show()

 OUTPUT

 4. Bar graph

import matplotlib.pyplot as plt

students = ["A", "B", "C", "D"]

marks = [80, 90, 75, 85]

plt.bar(students, marks)

plt.title("Bar Graph")

plt.xlabel("Students")

plt.ylabel("Marks")

plt.show()

 OUTPUT

 5. Pie chart

import matplotlib.pyplot as plt

labels = ["Python", "Java", "C++", "C"]

sizes = [40, 30, 20, 10]

plt.pie(sizes, labels=labels, autopct="%1.1f%%")

plt.title("Pie Chart")

plt.show()

 OUTPUT

 6. Column chart

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr"]

sales = [200, 300, 250, 400]

plt.bar(months, sales)

plt.title("Column Graph")

plt.xlabel("Months")

plt.ylabel("Sales")

plt.show()

 OUTPUT

 

 LAB 4

 

1. Write a simple python code for linear regression using scikit-learn 

# Simple Linear Regression

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

# Input data

x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)

y = np.array([2, 4, 5, 4, 5])

# Create and train the model

model = LinearRegression()

model.fit(x, y)

# Predict values

y_pred = model.predict(x)

# Print equation

print("Slope:", model.coef_[0])

print("Intercept:", model.intercept_)

# Plot graph

plt.scatter(x, y, color="blue", label="Actual Data")

plt.plot(x, y_pred, color="red", label="Regression Line")

plt.title("Simple Linear Regression")

plt.xlabel("X")

plt.ylabel("Y")

plt.legend()

plt.show()

 OUTPUT

 LAB 5

 1. Implement Multiple Linear Regression for House Price Prediction using scikit-learn.

# Multiple Linear Regression

import pandas as pd

from sklearn.linear_model import LinearRegression

# Sample dataset

data = {

    "Area": [1000, 1200, 1500, 1800, 2000],

    "Bedrooms": [2, 2, 3, 3, 4],

    "Price": [300000, 350000, 450000, 500000, 600000]

}

# Create DataFrame

df = pd.DataFrame(data)

# Features (Independent variables)

X = df[["Area", "Bedrooms"]]

# Target (Dependent variable)

y = df["Price"]

# Create and train the model

model = LinearRegression()

model.fit(X, y)

# Predict house price

predicted_price = model.predict([[1600, 3]])

print("Predicted House Price =", predicted_price[0])

 OUTPUT

Predicted House Price = 469158.87850467284

 LAB 6

 1. Implement a Decision Tree using scikit-learn and perform parameter tuning.

# Decision Tree with Parameter Tuning

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Sample dataset

X = [

    [22, 0],

    [25, 1],

    [47, 1],

    [52, 0],

    [46, 1],

    [56, 0],

    [48, 1],

    [33, 0]

]

# Target (0 = No, 1 = Yes)

y = [0, 0, 1, 1, 1, 1, 1, 0]

# Split dataset

X_train, X_test, y_train, y_test = train_test_split(

    X, y, test_size=0.25, random_state=42

)

# Create Decision Tree with parameter tuning

model = DecisionTreeClassifier(max_depth=3)

# Train model

model.fit(X_train, y_train)

# Predict

y_pred = model.predict(X_test)

# Accuracy

print("Accuracy:", accuracy_score(y_test, y_pred))

 OUTPUT

Accuracy: 1.0

 LAB 7

1. Implement a Decision Tree using scikit-learn and perform parameter tuning?

# Decision Tree with Parameter Tuning

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Sample dataset

X = [

    [22, 0],

    [25, 1],

    [47, 1],

    [52, 0],

    [46, 1],

    [56, 0],

    [48, 1],

    [33, 0]

]

# Target (0 = No, 1 = Yes)

y = [0, 0, 1, 1, 1, 1, 1, 0]

# Split dataset

X_train, X_test, y_train, y_test = train_test_split(

    X, y, test_size=0.25, random_state=42

)

# Create Decision Tree with parameter tuning

model = DecisionTreeClassifier(max_depth=3)

# Train model

model.fit(X_train, y_train)

# Predict

y_pred = model.predict(X_test)

# Accuracy

print("Accuracy:", accuracy_score(y_test, y_pred))

 OUTPUT

Accuracy: 1.0

 LAB 8

 1. Implement Logistic Regression using scikit-learn?

# Logistic Regression

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Sample dataset

X = [

    [20], [25], [30], [35], [40],

    [45], [50], [55], [60], [65]

]

# Target (0 = No, 1 = Yes)

y = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]

# Split data

X_train, X_test, y_train, y_test = train_test_split(

    X, y, test_size=0.2, random_state=42

)

# Create Logistic Regression model

model = LogisticRegression()

# Train the model

model.fit(X_train, y_train)

# Predict

y_pred = model.predict(X_test)

# Accuracy

print("Accuracy:", accuracy_score(y_test, y_pred))

# Predict for a new value

new_data = [[42]]

prediction = model.predict(new_data)

print("Prediction for Age 42:", prediction[0])

 OUTPUT

Accuracy: 1.0 Prediction for Age 42: 1

LAB 9

1. Implement k-means clustering? # K-Means Clustering import matplotlib.pyplot as plt from sklearn.cluster import KMeans # Sample data X = [     [1, 2],     [2, 3],     [3, 3],     [8, 7],     [8, 8],     [9, 8] ] # Create K-Means model kmeans = KMeans(n_clusters=2, random_state=0) # Train the model kmeans.fit(X) # Cluster labels print("Cluster Labels:", kmeans.labels_) # Cluster centers print("Cluster Centers:") print(kmeans.cluster_centers_) # Plot clusters plt.scatter([i[0] for i in X], [i[1] for i in X], c=kmeans.labels_) plt.scatter(kmeans.cluster_centers_[:,0],             kmeans.cluster_centers_[:,1],             color='red', marker='X', s=200) plt.title("K-Means Clustering") plt.xlabel("X") plt.ylabel("Y") plt.show()  OUTPUT Cluster Labels: [1 1 1 0 0 0] Cluster Centers: [[8.33333333 7.66666667] [2. 2.66666667]]