Description

Machine Learning – I
December 2023 Examination

1. Describe the steps in building Linear Regression model? Discuss any two real work
problem where this model is help to find the problem? (10 Marks)
Ans:
Introduction to Linear Regression:
Linear regression is a foundational and extensively used statistical approach in machine
learning and data analysis. It is a fundamental device for modeling the relationship between a
dependent variable (the output or target) and one or more impartial variables (the inputs or
functions). The core idea behind Linear Regression is to identify a linear equation that fine
represents the relationship among the variables. This equation lets us make predictions or
estimates based on the given inputs.
The simplicity and interpretability of Linear Regression make it an attractive desire for
various applications. Its name, “linear,” originates from the truth that the relationship among
It is only half solved
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2. Why feature selection is important in the context of Machine Learning. How will you
effectively select a few features to start off your model building process? State some of
the techniques for Feature Selection in Machine Learning. (10 Marks)
Ans:
Introduction
Function selection is pivotal in enhancing model performance and interpretability in device
learning. It involves choosing a subset of relevant capabilities from the authentic function set
to improve the model’s predictive ability, lessen overfitting, and boost computational
efficiency. In essence, feature selection helps identify the maximum informative attributes
that contribute notably to the prediction venture, thus simplifying the version while
maintaining its accuracy.
The importance of function choice lies in its capability to address the “curse of
dimensionality.” high-dimensional statistics might also contain irrelevant, redundant, or noisy
features that adversely affect version performance. Feature choice mitigates these issues by
3. An e-commerce company has collected a large amount of data on customer
transactions, including the items purchased, the price, and the date of purchase. The
company wants to use this data to improve its sales and marketing strategies, but the
data is too large and complex to be analyzed effectively using traditional methods.
The e-commerce company decided to use machine learning algorithms to perform data
reduction on the customer transaction data. They used a dimensionality reduction
algorithm such as principal component analysis (PCA) to reduce the number of
variables and simplify the data.
a. What were the result of using machine learning algorithms for data reduction in this
case study? (5 Marks)
Ans:
Introduction
Inside the rapidly evolving global of e-trade, corporations are inundated with significant data
generated from consumer transactions. This fact holds the titanic ability for understanding
purchaser conduct, options, and purchasing styles. However, this fact’s sheer volume and
complexity can crush traditional analytical approaches. System learning, mainly
dimensionality discount algorithms like principal component evaluation (PCA), offers a
b. How did the use of machine learning algorithms help the e-commerce company to
analyze the customer transaction data and improve its sales and marketing strategies?
(5 Marks)
Ans:
Introduction
In the era of digitalization, businesses are accumulating sizable amounts of data, along with
customer transaction facts, at an unprecedented fee. However, deriving significant insights
from these records can be daunting due to their length and complexity. To utilize this data
and optimize enterprise strategies, e-commerce companies are turning to machine learning
algorithms. Beneath their capacity to process and examine large and tricky datasets, those