DATA4200: Unstructured Data Management Assignment Help
Question
DATA4200: In this Master of Business Analytics assignment for Kaplan Business School, the student is required to demonstrate their data acquisition and management skills to handle unstructured data. The student is supposed to write a report based on a specific application from a chosen industry. This report should address the kind of unstructured data that an AI or machine learning program in this selected field could employ. The report is to be written through an extensive analysis of relevant and credible research articles.
Solution
In providing Master of Business Analytics assignment help for this report, our experts have provided a comprehensive explanation of the kinds of unstructured data that machine learning or artificial intelligence algorithms may use for the product recommendation system application in the retail industry of clothing. Our experts have supported the data with charts and graphs to write high-quality and credible reports.
Introduction
The first section of the report presents a brief overview of the field and its applications. In providing Kaplan Business School assignment Help, our experts have discussed the use of a product recommendation system in the clothing sector in this section.
The advent of digital technology has brought about notable changes in the retail industry, particularly in the clothing sector. The prevalence of online shopping platforms and e-commerce websites has grown significantly, affording consumers expedient entry to a diverse array of clothing merchandise. The vast array of options available to consumers can be a source of difficulty, as it may prove arduous for them to identify the products that align most effectively with their individual preferences and requirements. Product recommendation systems are crucial in this context (Deldjoo et al., 2022). The recommendation system for products is a technology-based approach that utilizes algorithms, data analysis, and customer behavior patterns to provide personalized and pertinent clothing suggestions to customers.
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Unstructured Data
This is followed by outlining the meaning of unstructured data to orient the reader to the importance of this report. In providing Kaplan Business School assignment Help for this section, our experts have drawn upon AI and machine learning can utilize unstructured data from the retail sector to enhance the product recommendation system. This section is further divided into various sub-sections, some of which you can read below:
The term “unstructured data” pertains to data that lacks a predetermined data model or organization.
- Textual data encompasses various forms of written content, such as product descriptions, customer reviews, and social media posts. The application of natural language processing (NLP) techniques enables the extraction of pertinent information, while sentiment analysis facilitates comprehension of customer opinions and preferences (Pereira et al., 2023).
- Product images are frequently provided for clothing items in image data. Artificial intelligence algorithms possess the capability to examine these images and extract characteristics such as color, pattern, style, and texture, which facilitates the provision of more precise recommendations grounded on visual resemblance.
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Best Practices
In the following section, our experts have elaborated on some of the best practices for better utilization of unstructured data in the clothing retail industry. When faced with challenging MBA assignments, students of Kalan Business School trust the expertise and support offered by OAS professionals, as we offer the best Business analytics assignment help in Sydney.
The following discourse pertains to the optimal methodologies and alternatives for obtaining, accumulating, retaining, distributing, recording, and upholding data. Preprocessing is often necessary for unstructured data to be utilized effectively in artificial intelligence (AI) applications. The aforementioned methods encompass procedures such as the elimination of punctuation, stop words, and special characters, as well as the application of stemming or lemmatization, and normalization. The manipulation of image data may involve procedures such as resizing, cropping, or feature extraction (Guan et al., 2016). The implementation of appropriate preprocessing techniques is essential to ensure the quality and consistency of data. The process of identifying pertinent characteristics from unorganized data is a crucial aspect for artificial intelligence algorithms. In the context of textual data, features may comprise of word frequencies, TF-IDF scores, or word embedding’s. In the context of image data, features may encompass color histograms, texture descriptors, or feature representations derived from deep learning techniques. The process of feature engineering serves to augment the quality of data representation and its ability to distinguish between different classes or categories. Unstructured data frequently encompasses confidential information, such as personally identifiable data (PII) or private correspondences.
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Accessing/Collecting
As the assignment demands the student to propose the best practices related to obtaining and accumulating unstructured data, our experts have used highly credible resources to highlight how one can improve the accessing of this kind of data.
To obtain and accumulate unstructured data, retailers may employ web scraping methods to compile product information, customer reviews, and social media data from diverse origins.
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Storing
This is followed by outlining how unstructured data in the product recommendation system can be stored appropriately. In providing assignment help for the students of Kaplan Business School, data from industry reports have also been used to generate a credible report.
Unstructured data can be stored in databases or data lakes that possess the capability to manage vast amounts of data and provide support for adaptable schema.
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Sharing
This is followed by outlining how unstructured data in the product recommendation system can be stored appropriately. In providing assignment help for the students of Kaplan Business School, data from industry reports have also been used to generate a credible report.
Unstructured data may be disseminated to various stakeholders within the organization via secure data sharing platforms or APIs.
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Documentation
This section comments on how the process of documentation can be improved in the product recommendation system for the clothing retail industry. You can read a snippet of the complete solution below:
The appropriate documentation of unstructured data is of utmost importance. It is imperative to document metadata, which includes temporal information, sources of data, and descriptions of data.
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Maintenance
Lastly, the best practices related to the maintenance of unstructured data in the retail clothing industry have been highlighted by our experts here.
Maintenance is a crucial aspect when dealing with unstructured data as it requires periodic upkeep to ensure the accuracy and pertinence of the data. The process entails performing data cleansing procedures to eliminate any duplicate entries or inconsistencies, revising data models to account for newly discovered features or attributes, and periodically retraining machine learning models to integrate the latest data and trends.
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Possible Question
Based on the critically analytical essay written above, our experts have proposed a hypothetical question about unstructured data and suggested a piece of software that would enable the use of AI to run the question and provide an answer.
Question: In what manner can the analysis of customer sentiment derived from textual data, ________________________________________________ ____________________________________________________________________________________________________________________________
The utilization of natural language processing (NLP) methodologies can be employed to examine unstructured textual data, such as customer reviews and social media posts, in order to address this inquiry. Natural Language Processing (NLP) software, such as the Natural Language Toolkit (NLTK) or spaCy, can be employed for the purpose of preprocessing and tokenizing textual data, extracting pertinent features, and conducting sentiment analysis (Gholami, et al., 2022).
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