What are two common business problems that machine learning solves? Select the two correct answers.Restructuring inefficient internal processesIdentifying competitor differentiationLeveraging underutilised employee talentCreating personalized customer experiencesAutomating processes

Describe a problem raised by Machine Learning in our society.

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.

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]

Which of the following is NOT an advantage of Machine Learning?Answer areaAutomation of repetitive tasksHigh initial setup costsAbility to handle large volumes of dataImprovement of outcomes through data analysis

What are the basic design issues and approaches to machine learning?