Reference no: EM132450617
Topic: Predictive Analytics and Predictive Modeling
Overview: Predictive analytics looks forward to attempt to divine unknown future events or actions based on data mining, statistics, modeling, deep learning and artificial intelligence, and machine learning. Business Intelligence, its predecessor in analytics, is a look backward. Predictive models are applied to business activities to better understand customers, with the goal of predicting buying patterns, potential risks, and likely opportunities.
Each industry and business sector deploys predictive analytics in different ways to improve operations and reduce risk. Please select one industry from the list and present a use case for applying predictive analytics and predictive modeling.
? Retail ? Healthcare ? Sports ? Weather ? Insurance ? Energy ? Social Media ? Financial Services
Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings. Be sure to cite and reference your work using the APAguides.
Include the following critical elements in your essay: I. Use Case: Briefly describe a use case for applying predictive analytics in the industry you have selected form the list above. What predictive analytics strategy works best for this use case? (social network, location-based, text, speech, and facial or visual analyses, for example) II. Predictive Modeling. Explain how predictive modeling can be used in this case to help determine the optimal outcome for the scenario in the use case. Which predictive modeling technique best fits the use case for predicting favorable outcomes? (linear regression, decision tree, or clustering, for example) III. Data Mining: Describe one data mining technique that would be useful for collecting the data required to support the model which needs to be developed to predict the most suitable results to the use case (generalization, characterization, pattern matching, data visualization, evolution, or meta rule-guided mining, for example).