Question 1(a) [3 marks]

Define machine Learning. Give any two applications of machine learning.

Answer:

Machine Learning is a subset of artificial intelligence that enables computers to learn and make decisions from data without being explicitly programmed for every task.

Applications:

Email spam detection: Automatically identifies and filters spam emails
Recommendation systems: Suggests products on e-commerce sites like Amazon

Table: ML vs Traditional Programming

Traditional Programming	Machine Learning
Input data + Program → Output	Input data + Output → Program
Rules are explicitly coded	Rules are learned from data

Mnemonic: "ML = Make Learning from data"

Question 1(b) [4 marks]

Define: Under fitting and overfitting.

Answer:

Underfitting occurs when a model is too simple to capture underlying patterns in data, resulting in poor performance on both training and test data.

Overfitting occurs when a model learns training data too well, including noise, causing poor performance on new unseen data.

Table: Comparison

Aspect	Underfitting	Overfitting
Training accuracy	Low	High
Test accuracy	Low	Low
Model complexity	Too simple	Too complex
Solution	Increase complexity	Reduce complexity

Mnemonic: "Under = Under-performs, Over = Over-learns"

Question 1(c) [7 marks]

Describe different types of machine learning with suitable example.

Answer:

Table: Types of Machine Learning

Type	Description	Example
Supervised	Uses labeled training data	Email classification
Unsupervised	No labeled data, finds patterns	Customer segmentation
Reinforcement	Learns through rewards/penalties	Game playing AI

Supervised Learning uses input-output pairs to train models. The algorithm learns from examples to predict outcomes for new data.

Unsupervised Learning discovers hidden patterns in data without target labels. It groups similar data points together.

Reinforcement Learning trains agents to make decisions by rewarding good actions and penalizing bad ones.

Diagram:

graph TD
    A[Machine Learning] --> B[Supervised Learning]
    A --> C[Unsupervised Learning]
    A --> D[Reinforcement Learning]
    B --> E[Classification]
    B --> F[Regression]
    C --> G[Clustering]
    C --> H[Association Rules]

Mnemonic: "Super Un-supervised Reinforces learning"

Question 1(c) OR [7 marks]

Describe different tools and technology used in the field machine learning.

Answer:

Table: ML Tools and Technologies

Category	Tools	Purpose
Programming	Python, R	Core development
Libraries	Scikit-learn, TensorFlow	Model building
Data Processing	Pandas, NumPy	Data manipulation
Visualization	Matplotlib, Seaborn	Data plotting

Python is the most popular language due to its simplicity and extensive libraries.

Scikit-learn provides simple tools for data mining and analysis, perfect for beginners.

TensorFlow and PyTorch are advanced frameworks for deep learning applications.

Jupyter Notebook offers interactive development environment for experimentation.

Diagram:

graph LR
    A[Data] --> B[Pandas/NumPy]
    B --> C[Scikit-learn]
    C --> D[Model]
    D --> E[Matplotlib]

Mnemonic: "Python Pandas Scikit Tensor Jupyter"

Question 2(a) [3 marks]

Give the difference between Qualitative data and Quantitative data.

Answer:

Table: Qualitative vs Quantitative Data

Qualitative Data	Quantitative Data
Non-numerical categories	Numerical values
Colors, names, grades	Height, weight, price
Cannot be measured	Can be measured

Qualitative data describes qualities or characteristics that cannot be measured numerically.

Quantitative data represents measurable quantities expressed as numbers.

Mnemonic: "Quality = Categories, Quantity = Numbers"

Question 2(b) [4 marks]

Find the mean and median for the following data: 3,4,5,5,7,8,9,11,12,14.

Answer:

Given data: 3, 4, 5, 5, 7, 8, 9, 11, 12, 14

Mean calculation:

Sum = 3+4+5+5+7+8+9+11+12+14 = 78
Count = 10 numbers
Mean = 78/10 = 7.8

Median calculation:

Data is already sorted
For 10 numbers: Median = (5th + 6th value)/2
Median = (7+8)/2 = 7.5

Table: Results

Measure	Value
Mean	7.8
Median	7.5

Mnemonic: "Mean = Average, Median = Middle"

Question 2(c) [7 marks]

Describe machine learning activities in detail.

Answer:

Table: Machine Learning Activities

Activity	Description	Example
Data Collection	Gathering relevant data	Survey responses
Data Preprocessing	Cleaning and preparing data	Removing duplicates
Feature Selection	Choosing important variables	Age, income for loans
Model Training	Teaching algorithm patterns	Feeding training data
Model Evaluation	Testing model performance	Accuracy measurement

Data Collection involves gathering information from various sources like databases, sensors, or surveys.

Data Preprocessing includes cleaning, transforming, and organizing raw data for analysis.

Feature Selection identifies the most relevant variables that contribute to predictions.

Model Training uses algorithms to learn patterns from prepared training data.

Model Evaluation tests how well the trained model performs on new, unseen data.

Diagram:

flowchart LR
    A[Data Collection] --> B[Data Preprocessing]
    B --> C[Feature Selection]
    C --> D[Model Training]
    D --> E[Model Evaluation]
    E --> F[Deployment]

Mnemonic: "Collect Process Feature Train Evaluate Deploy"

Question 2(a) OR [3 marks]

Give the difference between predicative model and descriptive model.

Answer:

Table: Predictive vs Descriptive Models

Predictive Model	Descriptive Model
Forecasts future outcomes	Explains current patterns
Uses supervised learning	Uses unsupervised learning
Stock price prediction	Customer segmentation

Predictive models use historical data to make predictions about future events or unknown outcomes.

Descriptive models analyze existing data to understand current patterns and relationships.

Mnemonic: "Predict = Future, Describe = Present"

Question 2(b) OR [4 marks]

Classify the following using appropriate data type: hair color, gender, blood group type, time of day.

Answer:

Table: Data Type Classification

Data	Type	Reason
Hair color	Nominal	Categories with no order
Gender	Nominal	Categories with no order
Blood group	Nominal	Categories with no order
Time of day	Continuous	Measurable quantity

Nominal data represents categories without any natural ordering.

Continuous data can take any value within a range and is measurable.

Mnemonic: "Names = Nominal, Numbers = Numerical"

Question 2(c) OR [7 marks]

Explain various methods used in data pre-processing.

Answer:

Table: Data Preprocessing Methods

Method	Purpose	Example
Data Cleaning	Remove errors and inconsistencies	Fix typos, remove duplicates
Data Integration	Combine multiple sources	Merge customer databases
Data Transformation	Convert to suitable format	Normalize values 0-1
Data Reduction	Reduce dataset size	Select important features

Data Cleaning removes or corrects erroneous, incomplete, or irrelevant data.

Data Integration combines data from multiple sources into a unified dataset.

Data Transformation converts data into appropriate formats for analysis.

Data Reduction decreases dataset size while maintaining information quality.

Diagram:

graph LR
    A[Raw Data] --> B[Data Cleaning]
    B --> C[Data Integration]
    C --> D[Data Transformation]
    D --> E[Data Reduction]
    E --> F[Processed Data]

Mnemonic: "Clean Integrate Transform Reduce"

Question 3(a) [3 marks]

Give difference between classification and regression.

Answer:

Table: Classification vs Regression

Classification	Regression
Discrete output	Continuous output
Predicts categories	Predicts numerical values
Email: spam/not spam	House price prediction

Classification predicts discrete categories or classes from input data.

Regression predicts continuous numerical values from input data.

Mnemonic: "Class = Categories, Regress = Real numbers"

Question 3(b) [4 marks]

Write confusion matrix using appropriate example. Calculate accuracy and error rate for it.

Answer:

Example: Email Classification

Table: Confusion Matrix

	Predicted Spam	Predicted Not Spam
Actual Spam	85 (TP)	15 (FN)
Actual Not Spam	10 (FP)	90 (TN)

Calculations:

Accuracy = (TP+TN)/(TP+TN+FP+FN) = (85+90)/200 = 87.5%
Error Rate = (FP+FN)/(TP+TN+FP+FN) = (10+15)/200 = 12.5%

Key Terms:

TP: True Positive - Correctly predicted spam
TN: True Negative - Correctly predicted not spam

Mnemonic: "True Positive True Negative = Correct predictions"

Question 3(c) [7 marks]

Explain KNN algorithm in detail.

Answer:

K-Nearest Neighbors (KNN) is a simple classification algorithm that classifies data points based on the majority class of their K nearest neighbors.

Table: KNN Algorithm Steps

Step	Description	Example
Choose K	Select number of neighbors	K=3
Calculate Distance	Find distance to all points	Euclidean distance
Find Neighbors	Identify K closest points	3 nearest points
Vote	Majority class wins	2 cats, 1 dog → cat

Working Process:

Calculate distances between test point and all training points
Sort distances and select K nearest neighbors
Count votes from each class among neighbors
Assign class with majority votes

Diagram:

graph LR
    A[New Data Point] --> B[Calculate Distances]
    B --> C[Find K Nearest]
    C --> D[Majority Vote]
    D --> E[Predict Class]

Advantages:

Simple to implement and understand
No training required - lazy learning algorithm

Mnemonic: "K Nearest Neighbors Vote for classification"

Question 3(a) OR [3 marks]

Give any three applications of multiple linear regression.

Answer:

Applications of Multiple Linear Regression:

Table: Applications

Application	Variables	Purpose
House Price Prediction	Size, location, age	Estimate property value
Sales Forecasting	Advertising, season, price	Predict revenue
Medical Diagnosis	Symptoms, age, history	Risk assessment

Multiple Linear Regression uses multiple input variables to predict a continuous output variable.

Mnemonic: "Multiple inputs, One output"

Question 3(b) OR [4 marks]

Explain bagging, boosting and stacking in detail.

Answer:

Table: Ensemble Methods

Method	Approach	Example
Bagging	Parallel training, average results	Random Forest
Boosting	Sequential training, learn from errors	AdaBoost
Stacking	Meta-learner combines models	Neural network combiner

Bagging trains multiple models on different data subsets and averages predictions.

Boosting trains models sequentially, each learning from previous model's mistakes.

Stacking uses a meta-model to learn how to combine predictions from base models.

Mnemonic: "Bag parallel, Boost sequential, Stack meta"

Question 3(c) OR [7 marks]

Explain single linear regression with its application.

Answer:

Single Linear Regression finds the best straight line relationship between one input variable (X) and one output variable (Y).

Formula: Y = a + bX

a: Y-intercept
b: Slope of line

Table: Application Example - House Price vs Size

House Size (sq ft)	Price (lakhs)
1000	50
1500	75
2000	100

Working Process:

Collect data with input-output pairs
Plot points on scatter graph
Find best line that minimizes error
Make predictions using line equation

Diagram:

    Price |
          |    *
       75 |      *
          |        *
       50 |  *
          |________________
             1000  1500  Size

Applications:

Sales vs Advertising: More ads → More sales
Temperature vs Ice cream sales: Hot weather → More sales

Mnemonic: "One X predicts One Y with a line"

Question 4(a) [3 marks]

Define the following: (1)support (2) confidence.

Answer:

Support measures how frequently an itemset appears in the dataset.

Confidence measures how often items in consequent appear when antecedent is present.

Table: Definitions

Measure	Formula	Example
Support	Count(itemset)/Total transactions	Bread appears in 60% transactions
Confidence	Support(A∪B)/Support(A)	80% who buy bread also buy butter

Support = Frequency of occurrence Confidence = Reliability of rule

Mnemonic: "Support = How often, Confidence = How reliable"

Question 4(b) [4 marks]

Explain applications of unsupervised learning.

Answer:

Table: Unsupervised Learning Applications

Application	Purpose	Example
Customer Segmentation	Group similar customers	Marketing campaigns
Data Compression	Reduce data size	Image compression
Anomaly Detection	Find unusual patterns	Fraud detection
Recommendation Systems	Suggest similar items	Music recommendations

Customer Segmentation groups customers with similar buying behavior for targeted marketing.

Data Compression reduces storage space by finding patterns and removing redundancy.

Anomaly Detection identifies unusual patterns that may indicate fraud or errors.

Mnemonic: "Segment Compress Detect Recommend"

Question 4(c) [7 marks]

Write and explain apriori algorithm with suitable example.

Answer:

Apriori Algorithm finds frequent itemsets and generates association rules for market basket analysis.

Table: Algorithm Steps

Step	Description	Example
Find frequent 1-itemsets	Count individual items	{Bread}:4, {Milk}:3
Generate 2-itemsets	Combine frequent items	{Bread,Milk}:2
Apply minimum support	Filter infrequent sets	Keep if support ≥ 50%
Generate rules	Create if-then rules	Bread → Milk

Example Dataset:

Transaction 1: {Bread, Milk, Eggs}
Transaction 2: {Bread, Milk}
Transaction 3: {Bread, Eggs}
Transaction 4: {Milk, Eggs}

Working Process:

Scan database to count item frequencies
Generate candidate itemsets of increasing size
Prune infrequent itemsets below minimum support
Generate association rules from frequent itemsets

Diagram:

flowchart LR
    A[Transaction Database] --> B[Find Frequent 1-itemsets]
    B --> C[Generate 2-itemsets]
    C --> D[Apply Min Support]
    D --> E[Generate Rules]

Mnemonic: "A-priori knowledge helps find frequent patterns"

Question 4(a) OR [3 marks]

List out the difference between clustering and classification.

Answer:

Table: Clustering vs Classification

Clustering	Classification
Unsupervised learning	Supervised learning
No labeled data	Uses labeled training data
Groups similar data	Assigns predefined labels

Clustering discovers hidden groups in unlabeled data.

Classification assigns new data to known categories using trained models.

Mnemonic: "Cluster = Groups unknown, Classify = Labels known"

Question 4(b) OR [4 marks]

Explain the clustering process in detail.

Answer:

Table: Clustering Process Steps

Step	Description	Purpose
Data Preparation	Clean and normalize data	Ensure quality input
Distance Metric	Choose similarity measure	Euclidean, Manhattan
Algorithm Selection	Pick clustering method	K-means, Hierarchical
Cluster Validation	Evaluate cluster quality	Silhouette score

Clustering Process groups similar data points together based on their characteristics.

Key decisions include choosing the number of clusters and appropriate distance metrics.

Validation ensures clusters are meaningful and well-separated.

Mnemonic: "Prepare Distance Algorithm Validate"

Question 4(c) OR [7 marks]

Write and explain K-means clustering algorithm with suitable example.

Answer:

K-means partitions data into K clusters by minimizing within-cluster sum of squares.

Table: Algorithm Steps

Step	Description	Example
Initialize centroids	Random K center points	C1(2,3), C2(8,7)
Assign points	Each point to nearest centroid	Point(1,2) → C1
Update centroids	Mean of assigned points	New C1(1.5, 2.5)
Repeat	Until centroids stop moving	Convergence

Example: Customer Income vs Age

Customer 1: (Income=30k, Age=25)
Customer 2: (Income=35k, Age=30)
Customer 3: (Income=70k, Age=45)
Customer 4: (Income=75k, Age=50)

Working Process:

Choose K=2 clusters for young/old customers
Initialize centroids randomly
Calculate distances from each customer to centroids
Assign customers to nearest centroid
Update centroid positions to center of assigned customers
Repeat until stable

Diagram:

flowchart LR
    A[Choose K] --> B[Initialize Centroids]
    B --> C[Assign Points to Nearest Centroid]
    C --> D[Update Centroid Positions]
    D --> E{Converged?}
    E -->|No| C
    E -->|Yes| F[Final Clusters]

Advantages:

Simple and fast for large datasets
Works well with spherical clusters

Mnemonic: "K centroids Mean their assigned points"

Question 5(a) [3 marks]

List the applications of matplotlib.

Answer:

Table: Matplotlib Applications

Application	Purpose	Example
Data Visualization	Create charts and graphs	Bar charts, histograms
Scientific Plotting	Research presentations	Mathematical functions
Dashboard Creation	Interactive displays	Business metrics

Matplotlib is Python's primary plotting library for creating static, animated, and interactive visualizations.

Key features include support for multiple plot types and customizable styling.

Mnemonic: "Mat-plot-lib = Math Plotting Library"

Question 5(b) [4 marks]

Write down code to plot a vertical line and horizontal line using matplotlib.

Answer:

Code Block:

import matplotlib.pyplot as plt

# Create figure
plt.figure(figsize=(8, 6))

# Plot vertical line at x=3
plt.axvline(x=3, color='red', linestyle='--', label='Vertical Line')

# Plot horizontal line at y=2
plt.axhline(y=2, color='blue', linestyle='-', label='Horizontal Line')

# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Vertical and Horizontal Lines')
plt.legend()
plt.grid(True)
plt.show()

Key Functions:

axvline(): Creates vertical line
axhline(): Creates horizontal line

Mnemonic: "axvline = Vertical, axhline = Horizontal"

Question 5(c) [7 marks]

Explain features and applications of Scikit-Learn.

Answer:

Table: Scikit-Learn Features

Feature	Description	Example
Simple API	Easy to use interface	fit(), predict()
Multiple Algorithms	Various ML methods	SVM, Random Forest
Data Preprocessing	Built-in data tools	StandardScaler
Model Evaluation	Performance metrics	accuracy_score

Scikit-Learn is Python's most popular machine learning library providing simple tools for data analysis.

Key Strengths:

Consistent interface across all algorithms
Excellent documentation with examples
Active community support and development

Applications:

Classification: Email spam detection
Regression: House price prediction
Clustering: Customer segmentation
Dimensionality Reduction: Data visualization

Diagram:

graph TD
    A[Scikit-Learn] --> B[Classification]
    A --> C[Regression]
    A --> D[Clustering]
    A --> E[Preprocessing]
    B --> F[SVM, Decision Trees]
    C --> G[Linear, Polynomial]
    D --> H[K-means, DBSCAN]
    E --> I[Scaling, Encoding]

Mnemonic: "Scikit = Science Kit for machine learning"

Question 5(a) OR [3 marks]

Give the purpose of NumPy in machine learning.

Answer:

Table: NumPy Purpose in ML

Purpose	Description	Benefit
Numerical Computing	Fast array operations	Efficient calculations
Foundation Library	Base for other libraries	Pandas, Scikit-learn use it
Mathematical Functions	Built-in math operations	Statistics, linear algebra

NumPy provides the foundation for numerical computing in Python machine learning applications.

Essential for handling large datasets and performing mathematical operations efficiently.

Mnemonic: "Num-Py = Numerical Python"

Question 5(b) OR [4 marks]

Write down steps to import csv file in pandas.

Answer:

Code Block:

import pandas as pd

# Step 1: Import pandas library
# Step 2: Use read_csv() function
data = pd.read_csv('filename.csv')

# Step 3: Display first few rows
print(data.head())

# Optional: Specify parameters
data = pd.read_csv('file.csv', 
                   delimiter=',',
                   header=0,
                   index_col=0)

Steps:

Import pandas library
Use read_csv() function with filename
Verify data with head() method

Mnemonic: "Import Read Verify"

Question 5(c) OR [7 marks]

Explain features and applications of Pandas.

Answer:

Table: Pandas Features

Feature	Description	Example
Data Structures	DataFrame and Series	Tabular data handling
Data I/O	Read/write multiple formats	CSV, Excel, JSON
Data Cleaning	Handle missing values	dropna(), fillna()
Data Analysis	Statistical operations	groupby(), describe()

Pandas is the primary data manipulation library in Python for machine learning projects.

Key Capabilities:

Data Loading from various file formats
Data Cleaning and preprocessing operations
Data Transformation and reshaping
Statistical Analysis and aggregation

Applications:

Data Preprocessing: Clean datasets before ML
Exploratory Analysis: Understand data patterns
Feature Engineering: Create new variables
Data Integration: Merge multiple data sources

Diagram:

graph LR
    A[Raw Data] --> B[Pandas DataFrame]
    B --> C[Data Cleaning]
    C --> D[Data Analysis]
    D --> E[Feature Engineering]
    E --> F[ML Ready Data]

Advantages:

Intuitive syntax for data operations
High performance with optimized operations
Integration with other ML libraries

Mnemonic: "Pandas = Panel Data for analysis"