In which scenario is regression machine learning most appropriate?Sorting emails into spam and non-spam categoriesPredicting the monthly sales revenue of a retail storeGrouping similar images into distinct classes Identifying fraudulent transactions in a credit card dataset

Which of the following statements involve machine learning models based on supervised learning:<br /> A. 1. An email spam filter uses previously tagged emails to classify messages as spam or non-spam. <br />B. 2. An AI artist generates unique paintings inspired by various art styles without explicit guidance. <br />C. 3. A self-driving car adapts its behavior to traffic conditions in real-time without using labeled data. <br />D. 4. A credit scoring system predicts the likelihood of default based on historical repayment data.

Machine learning has revolutionized data analytics by enabling intelligent pattern recognition, prediction, and decision-making. Provide three specific examples of how machine learning is applied in data analytics, such as fraud detection, recommendation systems, and sentiment analysis. Analyse the impact of machine learning on improving business processes, customer experiences, and decision-making outcomes.

Of the Following Examples, Which would you address using an supervised learning Algorithm?(1 Point)given email labeled as spam or not spam, learn a spam filtergiven a set of news articles found on the web, group them into set of articles about the same story.given a database of customer data, automatically discover market segments and group customers into different market segments.find the patterns in market basket analysis

1.Machine learning has revolutionized data analytics by enabling intelligent pattern recognition, prediction, and decision-making. Provide three specific examples of how machine learning is applied in data analytics, such as fraud detection, recommendation systems, and sentiment analysis. Analyse the impact of machine learning on improving business processes, customer experiences, and decision-making outcomes. [25] 2.Quantitative data methods are essential tools in business analysis for extracting meaningful insights from numerical data. Briefly explain the two primary types of quantitative data methods: descriptive statistics and inferential statistics. [10] 3.Discuss the purpose and application of each method in business decision-making. [15] 4.Explain the mechanism of supervised learning as a fundamental approach in machine learning. Emphasizing how algorithms map inputs to outputs. Provide an example of a supervised learning algorithm, such as logistic regression, and describe its application in a specific domain.[25] 5.Data analytics plays a pivotal role in various AI applications, enabling intelligent decision-making and problem-solving. Discuss three specific examples of how data analytics is utilized in AI applications, such as machine learning, natural language processing, and computer vision.[10] 6.Analyse the impact of data analytics on the performance and effectiveness of these AI applications.[15] 7.Outline the steps involved in developing and evaluating a predictive model for customer churn using logistic regression or a decision tree. Discuss the importance of data preparation, feature engineering, model training, and model evaluation in the process. Explain how you would evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.[25] 8.Data quality is a critical aspect of data analytics, ensuring the accuracy, reliability, and consistency of data for meaningful analysis. Explain four common data quality issues and discuss strategies for addressing them. [15] 9.Elaborate on the process of data transformation, highlighting the role it plays in preparing data for effective analysis. [10] 10.Describe three techniques used to validate the accuracy and completeness of data before analysis. [6] 11.Explain how you would gather and understand the specific needs of stakeholders when designing a data dashboard. [6] 12.Discuss the key characteristics of effective data visualizations that make them clear, informative, and impactful. [5]

Question 2The "Regression" technique in Machine Learning is a group of algorithms that are used for:1 pointPrediction of class/category of a case; for example, a cell is benign or malignant, or a customer will churn or not.Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.Predicting a continuous value; for example predicting the price of a house based on its characteristics.