Question 1(a) [3 marks]

Define human learning. List out types of human learning.

Answer:

Human learning is the process by which humans acquire new knowledge, skills, behaviors, or modify existing ones through experience, study, or instruction.

Types of Human Learning:

Type	Description
Supervised Learning	Learning with guidance from teacher/mentor
Unsupervised Learning	Self-directed learning without external guidance
Reinforcement Learning	Learning through trial and error with feedback

Mnemonic: "SUR - Supervised, Unsupervised, Reinforcement"

Question 1(b) [4 marks]

Differentiate between qualitative data and quantitative data.

Answer:

Table: Qualitative vs Quantitative Data

Feature	Qualitative Data	Quantitative Data
Nature	Descriptive, categorical	Numerical, measurable
Analysis	Subjective interpretation	Statistical analysis
Examples	Colors, names, gender	Height, weight, age
Representation	Words, categories	Numbers, graphs

Mnemonic: "QUAN-Numbers, QUAL-Words"

Question 1(c) [7 marks]

Compare the different types of machine learning.

Answer:

Table: Types of Machine Learning Comparison

Type	Training Data	Goal	Examples
Supervised	Labeled data	Predict outcomes	Classification, Regression
Unsupervised	Unlabeled data	Find patterns	Clustering, Association
Reinforcement	Reward/penalty	Maximize rewards	Gaming, Robotics

Key Differences:

Supervised: Uses input-output pairs for training
Unsupervised: Discovers hidden patterns in data
Reinforcement: Learns through interaction with environment

Mnemonic: "SUR-LAP: Supervised-Labeled, Unsupervised-Reveal, Reinforcement-Action"

Question 1(c OR) [7 marks]

Define machine learning. Explain any four applications of machine learning in brief.

Answer:

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

Four Applications:

Application	Description
Email Spam Detection	Classifies emails as spam or legitimate
Image Recognition	Identifies objects in photos
Recommendation Systems	Suggests products/content to users
Medical Diagnosis	Assists doctors in disease detection

Mnemonic: "SIRM - Spam, Image, Recommendation, Medical"

Question 2(a) [3 marks]

Relate the appropriate data type of following examples.

Answer:

Data Type Classification:

Example	Data Type
Nationality of students	Categorical (Nominal)
Education status of students	Categorical (Ordinal)
Height of students	Numerical (Continuous)

Mnemonic: "NCN - Nominal, Categorical, Numerical"

Question 2(b) [4 marks]

Explain data pre-processing in brief.

Answer:

Data pre-processing is the technique of preparing raw data for machine learning algorithms.

Key Steps:

Step	Purpose
Data Cleaning	Remove errors and inconsistencies
Data Integration	Combine data from multiple sources
Data Transformation	Convert data to suitable format
Data Reduction	Reduce data size while preserving information

Mnemonic: "CITR - Clean, Integrate, Transform, Reduce"

Question 2(c) [7 marks]

Show K-fold cross validation in detail.

Answer:

K-fold cross validation is a technique to evaluate model performance by dividing data into K equal parts.

Process:

graph LR
    A[Original Dataset] --> B[Split into K folds]
    B --> C[Use K-1 folds for training]
    C --> D[Use 1 fold for testing]
    D --> E[Repeat K times]
    E --> F[Average results]

Steps:

Divide: Split dataset into K equal parts
Train: Use K-1 folds for training
Test: Use remaining fold for validation
Repeat: Perform K iterations
Average: Calculate mean performance

Advantages:

Reduces overfitting
Better use of limited data
More reliable performance estimate

Mnemonic: "DTRA - Divide, Train, Repeat, Average"

Question 2(a OR) [3 marks]

Define following terms: i) Mean, ii) Outliers, iii) Interquartile range

Answer:

Statistical Terms:

Term	Definition
Mean	Average of all values in dataset
Outliers	Data points significantly different from others
Interquartile Range	Difference between 75th and 25th percentiles

Mnemonic: "MOI - Mean, Outliers, Interquartile"

Question 2(b OR) [4 marks]

Explain structure of confusion matrix.

Answer:

Confusion Matrix Structure:

	Predicted
Actual	Positive	Negative
Positive	True Positive (TP)	False Negative (FN)
Negative	False Positive (FP)	True Negative (TN)

Components:

TP: Correctly predicted positive cases
TN: Correctly predicted negative cases
FP: Incorrectly predicted as positive
FN: Incorrectly predicted as negative

Mnemonic: "TTFF - True True, False False"

Question 2(c OR) [7 marks]

Prepare short note on feature subset selection.

Answer:

Feature subset selection is the process of selecting relevant features from the original feature set.

Methods:

Method	Description
Filter Methods	Use statistical measures to rank features
Wrapper Methods	Use ML algorithms to evaluate feature subsets
Embedded Methods	Feature selection during model training

Benefits:

Reduced complexity: Fewer features, simpler models
Improved performance: Eliminates noise and irrelevant features
Faster training: Less computational overhead

Popular Techniques:

Chi-square test
Recursive Feature Elimination
LASSO regularization

Mnemonic: "FWE - Filter, Wrapper, Embedded"

Question 3(a) [3 marks]

Give the difference between predictive model and descriptive model.

Answer:

Model Type Comparison:

Feature	Predictive Model	Descriptive Model
Purpose	Forecast future outcomes	Understand current patterns
Output	Predictions/classifications	Insights/summaries
Examples	Regression, classification	Clustering, association rules

Mnemonic: "PF-DC: Predictive-Future, Descriptive-Current"

Question 3(b) [4 marks]

Discuss the difference between classification and regression.

Answer:

Classification vs Regression:

Aspect	Classification	Regression
Output	Discrete categories	Continuous values
Goal	Predict class labels	Predict numerical values
Examples	Spam detection, image recognition	Price prediction, temperature
Evaluation	Accuracy, precision, recall	MSE, RMSE, R-squared

Mnemonic: "CCNM - Classification-Categories, Regression-Numbers"

Question 3(c) [7 marks]

Define classification. Illustrate classification learning steps in details.

Answer:

Classification is a supervised learning technique that predicts discrete class labels for input data.

Classification Learning Steps:

graph LR
    A[Data Collection] --> B[Data Preprocessing]
    B --> C[Feature Selection]
    C --> D[Train-Test Split]
    D --> E[Model Training]
    E --> F[Model Evaluation]
    F --> G[Model Deployment]

Detailed Steps:

Data Collection: Gather labeled training data
Preprocessing: Clean and prepare data
Feature Selection: Choose relevant attributes
Split Data: Divide into training and testing sets
Training: Build model using training data
Evaluation: Test model performance
Deployment: Use model for predictions

Mnemonic: "DCFSTED - Data, Clean, Features, Split, Train, Evaluate, Deploy"

Question 3(a OR) [3 marks]

Give the difference between bagging and boosting.

Answer:

Bagging vs Boosting:

Feature	Bagging	Boosting
Sampling	Bootstrap sampling	Sequential weighted sampling
Training	Parallel training	Sequential training
Focus	Reduce variance	Reduce bias

Mnemonic: "BPV-BSB: Bagging-Parallel-Variance, Boosting-Sequential-Bias"

Question 3(b OR) [4 marks]

Explain different types of logistic regression in brief.

Answer:

Types of Logistic Regression:

Type	Classes	Use Case
Binary	2 classes	Yes/No, Pass/Fail
Multinomial	3+ classes (unordered)	Color classification
Ordinal	3+ classes (ordered)	Rating scales

Mnemonic: "BMO - Binary, Multinomial, Ordinal"

Question 3(c OR) [7 marks]

Write and show the use of k-NN algorithms.

Answer:

K-Nearest Neighbors (k-NN) is a lazy learning algorithm that classifies data points based on the majority class of k nearest neighbors.

Algorithm:

1. Choose value of k
2. Calculate distance to all training points
3. Select k nearest neighbors
4. For classification: majority vote
   For regression: average of k neighbors
5. Assign class/value to test point

Distance Calculation:

Euclidean Distance: √[(x₁-x₂)² + (y₁-y₂)²]

Applications:

Recommendation systems: Similar user preferences
Image recognition: Pattern matching
Medical diagnosis: Symptom similarity

Advantages:

Simple to implement
No training required
Works well with small datasets

Mnemonic: "CDSA - Choose, Distance, Select, Assign"

Question 4(a) [3 marks]

List out applications of support vector machine.

Answer:

SVM Applications:

Application	Domain
Text Classification	Document categorization
Image Recognition	Face detection
Bioinformatics	Gene classification

Mnemonic: "TIB - Text, Image, Bio"

Question 4(b) [4 marks]

Create pseudo code for k-means algorithm.

Answer:

K-means Pseudo Code:

BEGIN K-means
1. Initialize k cluster centroids randomly
2. REPEAT
   a. Assign each point to nearest centroid
   b. Update centroids as mean of assigned points
   c. Calculate total within-cluster sum of squares
3. UNTIL convergence or max iterations
4. RETURN final clusters and centroids
END

Mnemonic: "IAUC - Initialize, Assign, Update, Check"

Question 4(c) [7 marks]

Write and explain applications of unsupervised learning.

Answer:

Unsupervised learning discovers hidden patterns in data without labeled examples.

Major Applications:

Application	Description	Example
Customer Segmentation	Group customers by behavior	Market research
Anomaly Detection	Identify unusual patterns	Fraud detection
Data Compression	Reduce dimensionality	Image compression
Association Rules	Find item relationships	Market basket analysis

Clustering Applications:

Market research: Customer grouping
Social network analysis: Community detection
Gene sequencing: Biological classification

Dimensionality Reduction:

Visualization: High-dimensional data plotting
Feature extraction: Noise reduction

Mnemonic: "CADA - Customer, Anomaly, Data, Association"

Question 4(a OR) [3 marks]

List out applications of regression.

Answer:

Regression Applications:

Application	Purpose
Stock Price Prediction	Financial forecasting
Sales Forecasting	Business planning
Medical Diagnosis	Risk assessment

Mnemonic: "SSM - Stock, Sales, Medical"

Question 4(b OR) [4 marks]

Define following terms: i) Support ii) Confidence

Answer:

Association Rule Terms:

Term	Definition	Formula
Support	Frequency of itemset in database	Support(A) =
Confidence	Conditional probability of rule	Confidence(A→B) = Support(A∪B) / Support(A)

Example:

If 30% transactions contain bread and milk: Support = 0.3
If 80% of bread buyers also buy milk: Confidence = 0.8

Mnemonic: "SF-CP: Support-Frequency, Confidence-Probability"

Question 4(c OR) [7 marks]

Explain apriori algorithm in detail.

Answer:

Apriori algorithm finds frequent itemsets in transactional data using the apriori property.

Algorithm Steps:

graph LR
    A[Find frequent 1-itemsets] --> B[Generate candidate 2-itemsets]
    B --> C[Prune using apriori property]
    C --> D[Count support in database]
    D --> E[Find frequent k-itemsets]
    E --> F{More candidates?}
    F -->|Yes| B
    F -->|No| G[Generate rules]

Apriori Property:

If an itemset is frequent, all its subsets are frequent
If an itemset is infrequent, all its supersets are infrequent

Steps:

Scan database: Count 1-item support
Generate candidates: Create k+1 itemsets from frequent k-itemsets
Prune: Remove candidates with infrequent subsets
Count support: Scan database for candidate frequencies
Repeat: Until no new frequent itemsets found

Applications:

Market basket analysis
Web usage patterns
Protein sequences

Mnemonic: "SGPCR - Scan, Generate, Prune, Count, Repeat"

Question 5(a) [3 marks]

List out the major features of matplotlib.

Answer:

Matplotlib Features:

Feature	Description
Multiple Plot Types	Line, bar, scatter, histogram
Customization	Colors, styles, labels
Export Options	PNG, PDF, SVG formats

Mnemonic: "MCE - Multiple, Customization, Export"

Question 5(b) [4 marks]

How to load iris dataset in Numpy program? Explain.

Answer:

Loading Iris Dataset in NumPy:

import numpy as np
from sklearn.datasets import load_iris

# Load iris dataset
iris = load_iris()
data = iris.data    # Features
target = iris.target # Labels

Steps:

Import: Import required libraries
Load: Use sklearn's load_iris() function
Extract: Get features and target arrays
Access: Use .data and .target attributes

Mnemonic: "ILEA - Import, Load, Extract, Access"

Question 5(c) [7 marks]

Explain features and applications of Pandas.

Answer:

Pandas is a powerful data manipulation and analysis library for Python.

Key Features:

Feature	Description
DataFrame	2D labeled data structure
Series	1D labeled array
Data I/O	Read/write various file formats
Data Cleaning	Handle missing values
Grouping	Group and aggregate operations

Applications:

Application	Use Case
Data Analysis	Statistical analysis
Data Cleaning	Preprocessing for ML
Financial Analysis	Stock market data
Web Scraping	Parse HTML tables

Common Operations:

Reading data: pd.read_csv(), pd.read_excel()
Filtering: df[df['column'] > value]
Grouping: df.groupby('column').mean()

Mnemonic: "DSDCG - DataFrame, Series, Data I/O, Cleaning, Grouping"

Question 5(a OR) [3 marks]

List out the applications of matplotlib.

Answer:

Matplotlib Applications:

Application	Purpose
Scientific Visualization	Research data plotting
Business Analytics	Dashboard creation
Educational Content	Teaching materials

Mnemonic: "SBE - Scientific, Business, Educational"

Question 5(b OR) [4 marks]

Develop and explain the steps to import csv file in Pandas.

Answer:

Steps to Import CSV in Pandas:

import pandas as pd

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

# Optional parameters
df = pd.read_csv('file.csv', 
                 header=0,     # First row as header
                 sep=',',      # Comma separator
                 index_col=0)  # First column as index

Process:

Import: Import pandas library
Read: Use pd.read_csv() function
Specify: Add file path and parameters
Store: Assign to DataFrame variable

Mnemonic: "IRSS - Import, Read, Specify, Store"

Question 5(c OR) [7 marks]

Explain features and applications of Scikit-Learn.

Answer:

Scikit-Learn is a comprehensive machine learning library for Python.

Key Features:

Feature	Description
Algorithms	Classification, regression, clustering
Preprocessing	Data scaling and transformation
Model Selection	Cross-validation and grid search
Metrics	Performance evaluation tools

Applications:

Domain	Use Case
Healthcare	Disease prediction
Finance	Credit scoring
Marketing	Customer segmentation
Technology	Recommendation systems

Algorithm Categories:

Supervised: SVM, Random Forest, Linear Regression
Unsupervised: K-means, DBSCAN, PCA
Ensemble: Bagging, Boosting

Workflow:

Data preparation: Preprocessing
Model selection: Choose algorithm
Training: Fit model to data
Evaluation: Assess performance
Prediction: Make forecasts

Mnemonic: "APME - Algorithms, Preprocessing, Metrics, Evaluation"