Assignment 7 Classification Examples

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Arizona State University *

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511

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Business

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Apr 3, 2024

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Uploaded by AgentQuetzal3025

IFT 511: Analyzing Big Data Assignment 7: Classification Examples Prasad Srinivas Asmaa Elbadrawy Tuesday and Thursday (12:00PM -1:15PM) Date: 09/27/2023

1. In your own words, define the classification problem. Explain what is given, and what is required.?? The classification problem can be described as It involves the task of finding a function that can predict the class label (Y) of a data point based on its attributes (X). In this scenario we have a dataset, with data points each having attributes. These attributes act as the input variables (X). The objective is to determine the class label (Y) for each data point. We are given the attributes and their corresponding class labels and our goal is to develop a model or function (Y = f(X)) that can accurately predict the class label for any data point based on its attribute values. 2. Two examples of classification problems. I. Sentiment Analysis in Text : Sentiment analysis aims to classify text data, such as customer reviews, social media comments or product feedback into sentiment categories like "Positive " "Negative," or "Neutral." Here the attributes could be words or phrases, within the text and our task is to determine which sentiment category (class label) each piece of text belongs to. II. Detecting Fraud, in Financial Transactions : In this scenario the task involves analyzing data related to transactions. The data includes attributes such as transaction amount, location, time, and transaction history. The goal is to categorize transactions into two groups; "Legitimate" or "Fraudulent." By learning from data, the model can identify patterns or anomalies and promptly identify potentially fraudulent transactions, in real time. These examples showcase the flexibility of classification problems, which can be utilized across fields including natural language processing, finance, healthcare, and many others. This enables us to make informed decisions based on data.

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