In the realm of machine learning, what significant application involves the task of predicting items of interest for users based on their past interactions or behaviors?1 pointOptimizing search engine ranking algorithmsRecommender systems for personalized content suggestionsAnalyzing retail sales trends for inventory managementIdentifying fraudulent transactions in real-time

An online retailer uses a smart analytics tool to ingest real-time customer behavior data to surface the best suggestions for particular users. How can machine learning guide this activity?Machine learning can help identify user behavior in real time, but cannot make personalized suggestions based on the data.Machine learning can be used to make all users see the same product recommendations, regardless of their preferences or behavior.Through machine learning, with every click that the user makes, their website experience becomes increasingly personalized.Through machine learning, a user’s credit card transactions can be analyzed to determine regular purchases.

Question 8+1Tag to RevisitWhich industry heavily uses machine learning for recommendation systems?Answer areaAgricultureRetailManufacturingTransportation

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.

Which of the following use-cases can be solved using the Amazon Forecast service?Question 35Answera.To recommend personalized products for users based on their previous purchasesb.Document search service that can extract s from text within documentsc.To develop and test fully functional machine learning modelsd.Predict the web traffic of a website for the next few weeks

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]