DADS303 INTRODUCTION TO MACHINE LEARNING

Description

SESSION	JULY-AUGUST 2025
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	III
COURSE CODE & NAME	DADS303 INTRODUCTION TO MACHINE LEARNING

 

 

Assignment Set – 1

 

 

Q1. What do you mean by Machine Learning? Discuss the concept of Supervised, Unsupervised and Reinforcement learning. 10 

Ans 1.

Machine Learning

Machine Learning refers to a branch of artificial intelligence that enables computer systems to learn patterns from data and make predictions or decisions without being explicitly programmed for every scenario. Instead of following a rigid set of instructions, a machine learning model improves its performance over time as it is exposed to additional data. The core principle behind machine learning is the ability to identify relationships, extract meaningful insights, and generate accurate predictions in dynamic environments. As organizations collect

 

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Q2. What is Support Vector Machine? What are the various steps in using Support Vector Machine? 10   

Ans 2.

Support Vector Machine

Support Vector Machine (SVM) is a powerful supervised learning algorithm used primarily for classification and, to some extent, regression problems. The central idea of SVM is to identify an optimal separating boundary, known as a hyperplane, that best differentiates between different classes in the dataset. SVM works by maximizing the margin, which refers to the distance between the hyperplane and the nearest data points from each class. These nearest points are called support vectors. By maximizing this margin, the algorithm aims to achieve robust and accurate

 

 

 

Q3. Discuss Decision Tree in detail for classification. How we can make a balance model taking decision tree. 10       

Ans 3.

Decision Tree Classification and Creating a Balanced Decision Tree Model

Decision Tree Classification

A Decision Tree is a supervised learning method used for classification and prediction by dividing a dataset into branches based on decision rules. It resembles a tree-like structure with a root node, internal decision nodes, and leaf nodes representing outcomes. Decision Trees are intuitive because they mimic human decision-making by splitting data according to conditions. They are widely used in risk analysis, medical diagnosis, marketing segmentation, operational planning, and various prediction tasks. The core objective of a decision tree is to classify observations into appropriate categories based on the values of input features.

Working

 

Assignment Set – 2

 

4. Explain the K-Means Clustering algorithm 10

Ans 4.

K-Means Clustering

K-Means is one of the most widely used unsupervised learning algorithms for clustering data into groups based on similarity. The algorithm aims to partition data points into a predefined number of clusters, represented by the variable K. Each cluster has a centroid, which serves as the central point around which similar data points are grouped. K-Means is effective because it is computationally efficient and works well when the structure of data exhibits natural groupings. It is

 

5. Discuss various validation measures used for Machine Learning in detail. 10

Ans 5.

Validation Measures in Machine Learning

Validation measures play an essential role in evaluating the performance and reliability of machine learning models. They assess how well a model generalizes to unseen data and help prevent issues such as overfitting or underfitting. Validation ensures that model predictions are credible, stable, and suitable for real-world deployment. Since machine learning solutions often guide critical decisions in finance, healthcare, marketing, and security, selecting the right validation

 

 

6. What is a recommendation system. Discuss various types of recommendation systems. 10

Ans 6.

Meaning of Recommendation Systems and Types of Recommendation Systems

Recommendation Systems

A recommendation system is an intelligent information filtering tool used to suggest relevant products, services, or content to users based on their preferences and behavior. These systems play a central role in modern digital platforms, shaping user experience on e-commerce sites, streaming services, social media, and online learning environments. The core purpose of recommendation