Predictive analytics turns data into a crystal ball, empowering your organization to anticipate trends, seize opportunities, and stay ahead of the curve with every decision. In this course, we will begin with an overview of predictive analytics models, such as decision trees, kNN, and neural networks, and explore their business applications. Following this, we will examine a case study about customer churn to learn how to use a design sprint framework for brainstorming a predictive analytics project plan. Learning objectives: - Examine how predictive analytics principles can be applied to address business challenges. - Examine advanced ML/AI models for predictive analytics - Analyze business context and construct an issue tree for a predictive analytics project - Select a solution approach and define a predictive modeling project
Introduction to Predictive Modeling
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Skills You'll Learn
Deep Learning, Solution Design, Quality Control, Customer Analysis, Project Design, Predictive Analytics, Applied Machine Learning, Business Analytics, Analysis, Machine Learning, Design Thinking, Brainstorming, Decision Tree Learning
From the lesson
Module 1: Overview of Predictive Analytics
Taught By
Soumya Sen
Associate Professor