Description

SESSION JUL – AUG 2024
PROGRAM MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER III
COURSE CODE & NAME DADS303 INTRODUCTION TO MACHINE
LEARNING

Assignment Set – 1

1. What do you mean by Machine Learning? Discuss the relevance of Machine
Learning in Business.
Ans 1.
Machine Learning and Its Relevance in Business
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables machines to
learn from data and make decisions or predictions without explicit programming. Unlike
traditional programming, where rules are coded manually, ML uses algorithms to parse data,
identify patterns, and make predictions or decisions. The essence of ML lies in its ability to
improve automatically over time as it processes more data. This improvement stems from its
capacity to find patterns in complex and large datasets that are often beyond human
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2. What is Support Vector Machine? What are the various steps in using Support
Vector Machine?
Ans 2.
Support Vector Machine and Its Steps
Support Vector Machine (SVM) is a supervised machine learning algorithm used for
classification and regression tasks. It is particularly effective for high-dimensional datasets
and complex problems where the data is not linearly separable. SVM works by identifying
the hyperplane that best separates the data into different classes. The goal of the algorithm is
to maximize the margin, which is the distance between the hyperplane and the nearest data
3. Discuss all the assumptions of linear regression.
Ans 3.
Assumptions of Linear Regression
Linear regression is a statistical method used to model the relationship between a dependent
variable and one or more independent variables. The model is based on certain assumptions
that, when met, ensure the reliability and validity of the results. These assumptions are
foundational to understanding the behavior of linear regression models and their applicability
in various contexts. Below, the key assumptions of linear regression are discussed:
Linearity: The relationship between the dependent variable and the independent variables
Assignment Set – 2
4. Explain the K-Means Clustering algorithm
Ans 4.
K-Means Clustering Algorithm
K-Means clustering is one of the most popular unsupervised machine learning algorithms
used for partitioning a dataset into a predefined number of clusters (k). Unlike supervised
learning, K-Means does not rely on labeled data. Instead, it groups similar data points
together based on their features, minimizing the variance within clusters and maximizing the
variance between them.
The algorithm operates iteratively, assigning data points to one of kkk clusters by minimizing
5. Discuss various validation measures used for Machine Learning in detail.
Ans 5.
Various Validation Measures Used in Machine Learning
Validation measures in machine learning are crucial for evaluating the performance and
effectiveness of a model. These measures ensure that the model generalizes well to unseen
data, avoiding overfitting or underfitting. Below are detailed explanations of key validation
measures commonly used in machine learning:
1. Confusion Matrix and Related Metrics
The confusion matrix is a tabular representation of actual versus predicted classifications. It is
6. Briefly explain ‘Splitting Criteria’, ‘Merging Criteria’ and ‘Stopping Criteria’ in
Decision Tree.
Ans 6.
Splitting Criteria, Merging Criteria, and Stopping Criteria in Decision Trees
Decision trees are popular supervised learning algorithms used for both classification and
regression tasks. They work by recursively splitting the dataset into subsets based on feature
values, ultimately creating a tree structure. The quality and efficiency of the tree depend on
the splitting, merging, and stopping criteria. Below is a detailed explanation of these criteria:
1. Splitting Criteria
Splitting criteria determine how the dataset is divided at each node to maximize the