Question 1(a) [3 marks]

Define human learning and explain how machine learning is different from human learning?

Answer:

Table: Human Learning vs Machine Learning

Aspect	Human Learning	Machine Learning
Method	Experience, trial and error	Data and algorithms
Speed	Slow, gradual	Fast processing
Data Requirement	Limited examples needed	Large datasets required

Human Learning: Process of acquiring knowledge through experience, observation, and reasoning
Machine Learning: Automated learning from data using algorithms to identify patterns

Mnemonic: "Humans Experience, Machines Analyze Data" (HEMAD)

Question 1(b) [4 marks]

Describe the use of machine learning in finance and banking.

Answer:

Applications in Finance and Banking:

Application	Purpose	Benefit
Fraud Detection	Identify suspicious transactions	Reduce financial losses
Credit Scoring	Assess loan default risk	Better lending decisions
Algorithmic Trading	Automated trading decisions	Faster market responses

Risk Assessment: ML analyzes customer data to predict creditworthiness
Customer Service: Chatbots provide 24/7 support using NLP
Regulatory Compliance: Automated monitoring for suspicious activities

Mnemonic: "Finance Needs Smart Analysis" (FNSA)

Question 1(c) [7 marks]

Give difference between Supervised Learning, Unsupervised Learning and Reinforcement Learning.

Answer:

Comparison Table:

Feature	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Data Type	Labeled data	Unlabeled data	Environment interaction
Goal	Predict output	Find patterns	Maximize rewards
Examples	Classification, Regression	Clustering, Association	Game playing, Robotics
Feedback	Immediate	None	Delayed rewards

Key Characteristics:

Supervised Learning: Teacher-guided learning with correct answers provided
Unsupervised Learning: Self-discovery of hidden patterns in data
Reinforcement Learning: Learning through trial and error with rewards/penalties

Mnemonic: "Supervised Teachers, Unsupervised Explores, Reinforcement Rewards" (STUER)

Question 1(c OR) [7 marks]

Explain different tools and technology used in machine learning.

Answer:

ML Tools and Technologies:

Category	Tools	Purpose
Programming	Python, R, Java	Algorithm implementation
Libraries	Scikit-learn, TensorFlow	Ready-made algorithms
Visualization	Matplotlib, Seaborn	Data visualization
Data Processing	Pandas, NumPy	Data manipulation

Key Technologies:

Cloud Platforms: AWS, Google Cloud for scalable computing
Development Environments: Jupyter Notebook, Google Colab
Big Data Tools: Spark, Hadoop for large datasets

Mnemonic: "Python Libraries Visualize Data Effectively" (PLVDE)

Question 2(a) [3 marks]

Define outliers with one example.

Answer:

Definition: Outliers are data points that significantly differ from other observations in a dataset.

Example Table:

Student Heights (cm)	Classification
165, 170, 168, 172	Normal values
195	Outlier (too tall)
140	Outlier (too short)

Detection: Values beyond 1.5 × IQR from quartiles
Impact: Can skew statistical analysis and model performance

Mnemonic: "Outliers Stand Apart" (OSA)

Question 2(b) [4 marks]

Explain regression steps in detail.

Answer:

Regression Process Steps:

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

Detailed Steps:

Data Collection: Gather relevant dataset with input-output pairs
Preprocessing: Clean data, handle missing values, normalize features
Feature Selection: Choose relevant variables that affect target
Model Training: Fit regression line to minimize prediction errors

Mnemonic: "Data Preprocessing Features Train Evaluation Predicts" (DPFTEP)

Question 2(c) [7 marks]

Define Accuracy and for the following binary classifier's confusion matrix, find the various measurement parameters like 1. Accuracy 2. Precision.

Answer:

Confusion Matrix Analysis:

	Predicted No	Predicted Yes
Actual No	10 (TN)	3 (FP)
Actual Yes	2 (FN)	15 (TP)

Calculations:

Metric	Formula	Calculation	Result
Accuracy	(TP+TN)/(TP+TN+FP+FN)	(15+10)/(15+10+3+2)	83.33%
Precision	TP/(TP+FP)	15/(15+3)	83.33%

Definitions:

Accuracy: Proportion of correct predictions out of total predictions
Precision: Proportion of true positive predictions out of all positive predictions

Mnemonic: "Accuracy Counts All, Precision Picks Positives" (ACAPP)

Question 2(a OR) [3 marks]

Identify basic steps of feature subset selection.

Answer:

Feature Subset Selection Steps:

flowchart LR
    A[Original Features] --> B[Generate Subsets]
    B --> C[Evaluate Subsets]
    C --> D[Select Best Subset]

Basic Steps:

Generation: Create different combinations of features
Evaluation: Test each subset using performance metrics
Selection: Choose optimal subset based on criteria

Mnemonic: "Generate, Evaluate, Select" (GES)

Question 2(b OR) [4 marks]

Discuss the strength and weakness of the KNN algorithm.

Answer:

KNN Algorithm Analysis:

Strengths	Weaknesses
Simple to understand	Computationally expensive
No training required	Sensitive to irrelevant features
Works with non-linear data	Performance degrades with high dimensions
Effective for small datasets	Requires optimal K value selection

Key Points:

Lazy Learning: No explicit training phase required
Distance-Based: Classification based on neighbor proximity
Memory-Intensive: Stores entire training dataset

Mnemonic: "Simple but Slow, Effective but Expensive" (SBSEBE)

Question 2(c OR) [7 marks]

Define Error-rate and for the following binary classifier's confusion matrix, find the various measurement parameters like 1. Error value 2. Recall.

Answer:

Confusion Matrix Analysis:

	Predicted No	Predicted Yes
Actual No	20 (TN)	3 (FP)
Actual Yes	2 (FN)	15 (TP)

Calculations:

Metric	Formula	Calculation	Result
Error Rate	(FP+FN)/(TP+TN+FP+FN)	(3+2)/(15+20+3+2)	12.5%
Recall	TP/(TP+FN)	15/(15+2)	88.24%

Definitions:

Error Rate: Proportion of incorrect predictions out of total predictions
Recall: Proportion of actual positives correctly identified

Mnemonic: "Error Excludes, Recall Retrieves" (EERR)

Question 3(a) [3 marks]

Give any three examples of unsupervised learning.

Answer:

Unsupervised Learning Examples:

Example	Description	Application
Customer Segmentation	Group customers by behavior	Marketing strategies
Document Classification	Organize documents by topics	Information retrieval
Gene Sequencing	Group similar DNA patterns	Medical research

Market Basket Analysis: Finding product purchase patterns
Social Network Analysis: Identifying community structures
Anomaly Detection: Detecting unusual patterns in data

Mnemonic: "Customers, Documents, Genes Group Automatically" (CDGGA)

Question 3(b) [4 marks]

Find Mean and Median for the following data: 4,6,7,8,9,12,14,15,20

Answer:

Statistical Calculations:

Statistic	Calculation	Result
Mean	(4+6+7+8+9+12+14+15+20)/9	10.56
Median	Middle value (5th position)	9

Step-by-step:

Data: Already sorted: 4,6,7,8,9,12,14,15,20
Mean: Sum all values ÷ count = 95 ÷ 9 = 10.56
Median: Middle value in sorted list = 9 (5th position)

Mnemonic: "Mean Averages All, Median Middle Value" (MAAMV)

Question 3(c) [7 marks]

Describe k-fold cross validation method in detail.

Answer:

K-Fold Cross Validation Process:

flowchart LR
    A[Original Dataset] --> B[Split into K folds]
    B --> C[Train on K-1 folds]
    C --> D[Test on 1 fold]
    D --> E[Repeat K times]
    E --> F[Average Results]

Process Steps:

Step	Description	Purpose
1. Data Division	Split data into K equal parts	Ensure balanced testing
2. Iterative Training	Use K-1 folds for training	Maximum data utilization
3. Validation	Test on remaining fold	Unbiased evaluation
4. Averaging	Calculate mean performance	Robust performance estimate

Advantages:

Unbiased Estimation: Each data point used for both training and testing
Reduced Overfitting: Multiple validation rounds increase reliability
Efficient Data Use: All data utilized for both training and validation

Mnemonic: "K-fold Keeps Keen Knowledge" (KKKK)

Question 3(a OR) [3 marks]

Give any three applications of multiple linear regression.

Answer:

Multiple Linear Regression Applications:

Application	Variables	Purpose
House Price Prediction	Size, location, age	Real estate valuation
Sales Forecasting	Marketing spend, season, economy	Business planning
Medical Diagnosis	Symptoms, age, history	Disease prediction

Stock Market Analysis: Multiple economic indicators predict stock prices
Academic Performance: Study hours, attendance, previous grades predict scores
Marketing ROI: Various marketing channels impact sales revenue

Mnemonic: "Houses, Sales, Medicine Predict Multiple Variables" (HSMPV)

Question 3(b OR) [4 marks]

Find Standard Deviation for the following data: 4,15,20,28,35,45

Answer:

Standard Deviation Calculation:

Step	Calculation	Value
Mean	(4+15+20+28+35+45)/6	24.5
Variance	Σ(xi-mean)²/n	236.92
Std Dev	√Variance	15.39

Detailed Calculation:

Deviations from mean: (-20.5)², (-9.5)², (-4.5)², (3.5)², (10.5)², (20.5)²
Squared deviations: 420.25, 90.25, 20.25, 12.25, 110.25, 420.25
Sum: 1073.5
Variance: 1073.5/6 = 178.92
Standard Deviation: √178.92 = 13.38

Mnemonic: "Deviation Measures Data Spread" (DMDS)

Question 3(c OR) [7 marks]

Explain Bagging, Boosting in detail.

Answer:

Ensemble Methods Comparison:

Aspect	Bagging	Boosting
Strategy	Parallel training	Sequential training
Data Sampling	Random with replacement	Weighted sampling
Combination	Simple averaging/voting	Weighted combination
Bias-Variance	Reduces variance	Reduces bias

Bagging (Bootstrap Aggregating):

flowchart LR
    A[Original Data] --> B[Bootstrap Sample 1]
    A --> C[Bootstrap Sample 2]
    A --> D[Bootstrap Sample n]
    B --> E[Model 1]
    C --> F[Model 2]
    D --> G[Model n]
    E --> H[Final Prediction]
    F --> H
    G --> H

Boosting Process:

Sequential Learning: Each model learns from previous model's mistakes
Weight Adjustment: Increase weight of misclassified examples
Final Prediction: Weighted combination of all models

Key Differences:

Bagging: Independent models trained in parallel, reduces overfitting
Boosting: Dependent models trained sequentially, improves accuracy

Mnemonic: "Bagging Builds Parallel, Boosting Builds Sequential" (BBPBS)

Question 4(a) [3 marks]

Define: Support, Confidence.

Answer:

Association Rule Metrics:

Metric	Definition	Formula
Support	Frequency of itemset in transactions	Support(A) = Count(A)/Total transactions
Confidence	Conditional probability of rule	Confidence(A→B) = Support(A∪B)/Support(A)

Example:

Support(Bread) = 0.6 (60% transactions contain bread)
Confidence(Bread→Butter) = 0.8 (80% of bread buyers also buy butter)

Applications:

Market Basket Analysis: Finding product associations
Recommendation Systems: Suggesting related items

Mnemonic: "Support Shows Frequency, Confidence Shows Connection" (SSFC)

Question 4(b) [4 marks]

Illustrate any two applications of logistic regression.

Answer:

Logistic Regression Applications:

Application	Input Variables	Output	Use Case
Email Spam Detection	Word frequency, sender, subject	Spam/Not Spam	Email filtering
Medical Diagnosis	Symptoms, age, test results	Disease/No Disease	Healthcare

Key Features:

Binary Classification: Predicts probability between 0 and 1
S-shaped Curve: Uses sigmoid function for probability estimation
Linear Decision Boundary: Separates classes with linear boundary

Real-world Examples:

Marketing: Customer purchase probability based on demographics
Finance: Credit approval based on credit history and income

Mnemonic: "Logistic Limits Linear Logic" (LLLL)

Question 4(c) [7 marks]

Discuss the main purpose of Numpy and Pandas in machine learning.

Answer:

NumPy and Pandas in ML:

Library	Purpose	Key Features
NumPy	Numerical computing	Arrays, mathematical functions
Pandas	Data manipulation	DataFrames, data cleaning

NumPy Functions:

graph TD
    A[NumPy] --> B[Array Operations]
    A --> C[Mathematical Functions]
    A --> D[Linear Algebra]
    A --> E[Random Numbers]

Pandas Capabilities:

Data Import/Export: Read CSV, Excel, JSON files
Data Cleaning: Handle missing values, duplicates
Data Transformation: Group, merge, pivot operations
Statistical Analysis: Descriptive statistics, correlation

Integration with ML:

Data Preprocessing: Clean and prepare data for algorithms
Feature Engineering: Create new features from existing data
Model Input: Convert data to format required by ML algorithms

Key Benefits:

Performance: Optimized C/C++ backend for speed
Memory Efficiency: Efficient data storage and manipulation
Ecosystem Integration: Works seamlessly with scikit-learn, matplotlib

Mnemonic: "NumPy Numbers, Pandas Processes Data" (NNPD)

Question 4(a OR) [3 marks]

Give any three examples of Supervised Learning.

Answer:

Supervised Learning Examples:

Example	Type	Input → Output
Email Classification	Classification	Email features → Spam/Not Spam
House Price Prediction	Regression	House features → Price
Image Recognition	Classification	Pixel values → Object class

Medical Diagnosis: Patient symptoms → Disease classification
Stock Price Prediction: Market indicators → Future price
Speech Recognition: Audio signals → Text transcription

Mnemonic: "Emails, Houses, Images Learn Supervised" (EHILS)

Question 4(b OR) [4 marks]

Explain any two applications of the apriori algorithm.

Answer:

Apriori Algorithm Applications:

Application	Description	Business Value
Market Basket Analysis	Find products bought together	Cross-selling strategies
Web Usage Mining	Discover website navigation patterns	Improve user experience

Market Basket Analysis:

Example: "Customers who buy bread and milk also buy eggs"
Business Impact: Product placement, promotional offers
Implementation: Analyze transaction data to find frequent itemsets

Web Usage Mining:

Example: "Users visiting page A often visit page B next"
Website Optimization: Improve navigation, recommend content
User Experience: Personalized website layouts

Algorithm Process:

Generate Candidates: Create frequent itemsets
Prune: Remove infrequent items
Generate Rules: Create association rules with confidence

Mnemonic: "Apriori Analyzes Associations Automatically" (AAAA)

Question 4(c OR) [7 marks]

Explain the features and applications of Matplotlib.

Answer:

Matplotlib Features and Applications:

Feature Category	Capabilities	Applications
Plot Types	Line, bar, scatter, histogram	Data exploration
Customization	Colors, labels, styles	Professional presentations
Subplots	Multiple plots in one figure	Comparative analysis
3D Plotting	Three-dimensional visualizations	Scientific modeling

Key Features:

graph TD
    A[Matplotlib] --> B[2D Plotting]
    A --> C[3D Plotting]
    A --> D[Interactive Plots]
    A --> E[Publication Quality]
    B --> F[Line Charts]
    B --> G[Bar Charts]
    B --> H[Scatter Plots]
    C --> I[Surface Plots]
    C --> J[3D Scatter]

Applications in Machine Learning:

Data Exploration: Visualize data distribution and patterns
Model Performance: Plot accuracy, loss curves during training
Result Presentation: Display predictions vs actual values
Feature Analysis: Correlation matrices, feature importance plots

Advanced Capabilities:

Animation: Create animated plots for time-series data
Interactive Widgets: Add sliders, buttons for user interaction
Integration: Works with Jupyter notebooks, web applications

Benefits:

Flexibility: Highly customizable plotting options
Community: Large user base with extensive documentation
Compatibility: Integrates with NumPy, Pandas seamlessly

Mnemonic: "Matplotlib Makes Meaningful Visual Displays" (MMVD)

Question 5(a) [3 marks]

List out the major features of Numpy.

Answer:

NumPy Major Features:

Feature	Description	Benefit
N-dimensional Arrays	Efficient array operations	Fast mathematical computations
Broadcasting	Operations on different sized arrays	Flexible array manipulation
Linear Algebra	Matrix operations, decompositions	Scientific computing support

Universal Functions: Element-wise operations on arrays
Memory Efficiency: Contiguous memory layout for speed
C/C++ Integration: Interface with compiled languages

Mnemonic: "NumPy Numbers Need Neat Operations" (NNNNO)

Question 5(b) [4 marks]

How to load an iris dataset csv file in a Pandas Dataframe program? Explain with example.

Answer:

Loading Iris Dataset:

import pandas as pd

# Method 1: Load from file
df = pd.read_csv('iris.csv')

# Method 2: Load from sklearn
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

# Display basic information
print(df.head())
print(df.info())
print(df.describe())

Code Explanation:

pd.read_csv(): Reads CSV file into DataFrame
columns parameter: Assigns column names
head(): Shows first 5 rows
info(): Displays data types and memory usage

Mnemonic: "Pandas Reads CSV Files Easily" (PRCFE)

Question 5(c) [7 marks]

Compare and Contrast Supervised Learning and Unsupervised Learning.

Answer:

Comprehensive Comparison:

Aspect	Supervised Learning	Unsupervised Learning
Data Type	Labeled (input-output pairs)	Unlabeled (input only)
Learning Goal	Predict target variable	Discover hidden patterns
Evaluation	Accuracy, precision, recall	Silhouette score, inertia
Complexity	Less complex to evaluate	More complex to validate
Applications	Classification, regression	Clustering, dimensionality reduction

Detailed Comparison:

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

Supervised Learning Characteristics:

Training Process: Learn from examples with known correct answers
Performance Measurement: Direct comparison with actual outcomes
Common Algorithms: Decision trees, SVM, neural networks
Business Applications: Fraud detection, medical diagnosis, price prediction

Unsupervised Learning Characteristics:

Exploration: Find unknown patterns without guidance
Validation Challenges: No ground truth for direct comparison
Common Algorithms: K-means, hierarchical clustering, PCA
Business Applications: Customer segmentation, market research, anomaly detection

Key Contrasts:

Feedback: Supervised has immediate feedback, unsupervised relies on domain expertise
Data Requirements: Supervised needs expensive labeled data, unsupervised uses readily available unlabeled data
Problem Types: Supervised solves prediction problems, unsupervised solves discovery problems

Mnemonic: "Supervised Seeks Specific Solutions, Unsupervised Uncovers Unknown" (SSSUU)

Question 5(a OR) [3 marks]

List out the applications of Pandas.

Answer:

Pandas Applications:

Application	Description	Industry
Data Cleaning	Handle missing values, duplicates	All industries
Financial Analysis	Stock market, trading data	Finance
Business Intelligence	Sales reports, KPI analysis	Business

Scientific Research: Experimental data analysis
Web Analytics: Website traffic, user behavior analysis
Healthcare: Patient records, clinical trial data

Mnemonic: "Pandas Processes Data Perfectly" (PPDP)

Question 5(b OR) [4 marks]

How to plot a vertical line and horizontal line in matplotlib? Explain with examples.

Answer:

Matplotlib Line Plotting:

import matplotlib.pyplot as plt
import numpy as np

# Create sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Plot the main curve
plt.plot(x, y, label='sin(x)')

# Vertical line at x = 5
plt.axvline(x=5, color='red', linestyle='--', label='Vertical Line')

# Horizontal line at y = 0.5
plt.axhline(y=0.5, color='green', linestyle=':', label='Horizontal Line')

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

Key Functions:

axvline(): Creates vertical line at specified x-coordinate
axhline(): Creates horizontal line at specified y-coordinate
Parameters: color, linestyle, linewidth, alpha

Mnemonic: "Matplotlib Makes Lines Easily" (MMLE)

Question 5(c OR) [7 marks]

Describe the concept of clustering using appropriate real-world examples.

Answer:

Clustering Concept and Applications:

Clustering Type	Real-World Example	Business Impact
Customer Segmentation	Group customers by purchase behavior	Targeted marketing campaigns
Image Segmentation	Medical imaging for tumor detection	Improved diagnosis accuracy
Gene Analysis	Group genes with similar expression	Drug discovery and treatment

Clustering Process:

flowchart LR
    A[Raw Data] --> B[Feature Selection]
    B --> C[Distance Calculation]
    C --> D[Cluster Formation]
    D --> E[Cluster Validation]
    E --> F[Business Insights]

Detailed Examples:

1. Customer Segmentation:

Data: Purchase history, demographics, website behavior
Clusters: High-value customers, price-sensitive buyers, occasional shoppers
Business Value: Customized marketing, product recommendations, retention strategies

2. Social Media Analysis:

Data: User interactions, post topics, engagement patterns
Clusters: Influencers, casual users, brand advocates
Applications: Viral marketing, content strategy, community management

3. Market Research:

Data: Survey responses, product preferences, demographics
Clusters: Market segments with similar needs
Insights: Product development, pricing strategy, market positioning

Clustering Algorithms:

K-Means: Partitions data into k clusters
Hierarchical: Creates tree-like cluster structure
DBSCAN: Finds clusters of varying density

Validation Methods:

Silhouette Score: Measures cluster quality
Elbow Method: Determines optimal number of clusters
Domain Expertise: Business knowledge validation

Benefits:

Pattern Discovery: Reveals hidden data structures
Decision Support: Provides insights for business decisions
Automation: Reduces manual data analysis effort

Mnemonic: "Clustering Creates Clear Categories" (CCCC)