Successfully Evaluating Predictive Modelling
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date_range Starts on October 26, 2021
event_note Ends on December 20, 2021
list 6 sequences
assignment Level : Advanced
chat_bubble_outline Language : English
card_giftcard 672 point
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credit_card Free access
verified_user Fee-based Certificate
timer 48 hours in total

About the content

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.

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

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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.



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



Dr Xuefei Lu
Lecturer in Predictive Analytics
University of Edinburgh

Sofia Varypati
Course Tutor
University of Edinburgh

Obinna Unigwe
Course Tutor
The University of Edinburgh

Dr Johannes De Smedt
Assistant Professor in Business Information Systems
KU Leuven

Dr Zexun Chen
Lecturer in Predictive Analytics
University of Edinburgh




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