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Successfully Evaluating Predictive Modelling
About this courseSkip About this course
A predictive exercise is not finished when a model is built. This course will equip you with essential skills for understanding performance evaluation metrics, using Python, to determine whether a model is performing adequately.
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.
At a glance
- Institution: EdinburghX
- Subject: Data Analysis & Statistics
- Level: Advanced
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.
- Language: English
- Video Transcript: English
- Associated programs:
- MicroMasters® Program in Predictive Analytics using Python
What you'll learnSkip 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
About the instructors
Frequently Asked QuestionsSkip Frequently Asked Questions
What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.
What software will I be required to use?
All coding activities on this course will be hosted on Vocareum. You will be able to access this free software directly within the edX platform. There is no requirement to purchase further software in order to complete this course.
What do I need to complete the course?
For successful completion of this course, you will need access to a computer or mobile device and a reliable internet connection.
What is the University of Edinburgh Accessibility Guidance?
The University of Edinburgh is committed to providing online information and services accessible to all. Edx provide an accessibility statement which is available via the footer of all edx.org pages and includes an 'Accessibility Feedback' form which allows Learners to register feedback directly with the edx. Courses created by the University of Edinburgh contain an Accessibility Statement which addresses equality of access to information and servicesandis available via the 'Support' page.