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Specifically, you will learn:
- Appropriate measures that are used to evaluate predictive models
- Procedures that are used to ensure that models do not cheat through, for example, overfitting or predicting incorrect distributions
- The ways that different model evaluation criteria illustrate how one model excels over another and how to identify when to use certain criteria
This is the foundation of optimising successful predictive models. The concepts will be brought together in a comprehensive case study that deals with customer churn. You will be tasked with selecting suitable variables to predict whether a customer will leave a telecommunications provider by looking into their behaviour, creating various models, and benchmarking them by using the appropriate evaluation criteria.
This course is part of the Predictive Analytics using Python MicroMasters® Program.
What you'll learn
In this course, you will:
- Analyse the accuracy and quality of a predictive model
- Implement effective measures and strategies to measure models
- Evaluate datasets to determine appropriateness and strength of techniques
- Understand the techniques used in recommender systems
Week 1: Evaluation Metrics and Feature Selection
Week 2: Feature Selection and Correlation Analysis
Week 3: Feature Selection with Decomposition Techniques
Week 4: Sampling Techniques
Week 5: Resampling Techniques
Week 6: Case Study
You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).
Learners pursuing the MicroMasters programme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python on the verified track prior to undertaking this course.