Description

SESSION	FEBRUARY – MARCH 2024
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	IV
COURSE CODE & NAME	DADS402 – UNSTRUCTURED DATA ANALYSIS

 

 

Assignment Set – 1

 

 

1. (a) List down a few differences between structured and unstructured data.

(b) What is the difference between Text and Big data?

Ans 1.

1. Differences Between Structured and Unstructured Data

Definition and Format: Structured data is highly organized and formatted in a way that is easy to search, process, and analyze. It typically resides in relational databases (RDBMS) and is displayed in rows and columns, making it easy to enter, query, and analyze through standard database tools and techniques. Examples include data from CRM systems, inventory, and sales transactions.

In contrast, unstructured data is not organized in a pre-defined manner or does not have a pre-defined data model, making it more complex to collect, process, and analyze. It encompasses

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2. (a) What is a word cloud?What are some libraries that you need to import to create a word cloud in python?

(b) What is a naive Bayes classifier and how does it work in text classification? 

Ans 2.

1. Word Cloud: A word cloud, also known as a tag cloud or text cloud, is a visual representation of text data where the size of each word indicates its frequency or importance in a body of text. Typically, more frequent terms are displayed in larger fonts or bolder colors, helping to highlight the most prominent words and themes in a document or a collection of texts. This makes word clouds useful for quickly perceiving the most prominent

 

 

3. (a) What is the Machine Learning approach in sentiment analysis?

(b) What are some applications of topic modelling?           

Ans 3.

1. Machine Learning Approach in Sentiment Analysis

Sentiment Analysis: Sentiment analysis, often referred to as opinion mining, is a field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. It is predominantly used in the context of processing natural language, text analysis, and computational linguistics.

Machine Learning in Sentiment Analysis: The machine learning approach to sentiment analysis involves training a model on a pre-labeled dataset where each text sample (such as a

 

 

Assignment Set – 2

 

 

4. (a) What is Fast Fourier Transform (FFT)?

(b) What is audio data preprocessing in machinelearning?

Ans 4.

1. Fast Fourier Transform (FFT)

Introduction to FFT: The Fast Fourier Transform (FFT) is an algorithm that computes the Discrete Fourier Transform (DFT) and its inverse. Fourier analysis converts a signal from its original domain (often time or space) into a representation in the frequency domain and vice versa. The FFT is widely utilized because it is an efficient way to calculate the DFT, which is a critical component in signal processing.

Functionality of FFT: The primary function of the FFT is to analyze the frequencies

 

 

5. (a)What are the benefits of using histogram equalization?

(b)What is the advantage of using a CNN for image classification?         

Ans 5.

1. Benefits of Histogram Equalization

Enhancing Image Contrast: Histogram equalization is a technique used in image processing to improve the contrast in images. It operates by effectively spreading out the most frequent intensity values in an image, which is particularly useful in images with backgrounds and foregrounds that are both bright or both dark. The main benefit of using histogram equalization is the enhancement of global contrast and better visibility of details in various

 

 

6. (a)What are some common techniques used for video classification?

(b)What is the difference between feature extraction and feature selection?       

Ans 6.

1. Common Techniques Used for Video Classification

Video classification involves analyzing video data to categorize content into predefined classes. This process is essential in various applications such as surveillance, content moderation, and media retrieval. The unique challenge in video classification compared to image classification is the added dimension of time, which means that both spatial and temporal features must be considered.