Course details

Predictive Modelling Best Practices: Applying Predictive Analytics

Predictive Modelling Best Practices: Applying Predictive Analytics

Expected Duration
Lesson Objectives
Course Number
Expertise Level


This 13-video course explores machine learning predictive analytics, and how its application can drive revenues, reduce costs, and provide a competitive advantage to businesses. Learners will observe the predictive modeling process and how to apply tools and techniques for performing predictive analytics, and how to use historical data to identify trends and patterns to forecast future events. First, you will learn about the predicative modeling process, the statistical concepts for predictive modeling, and regression techniques. This course uses two examples to demonstrate commonly used methods of predictive analytics, by examining decision trees and SVMs (support vector machines). Next, you will learn about survival analysis, market basket analysis, and how to apply data for cluster models. You will learn about random forests in predictive analytics, and you will examine probabilistic graphical models. Learn about classification models, and how to organize data into groups based on predicting the class of the data points. Finally, you will explore some best practices for predictive modeling.

Expected Duration (hours)

Lesson Objectives

Predictive Modelling Best Practices: Applying Predictive Analytics

  • Course Overview
  • describe predictive analytics is and where it is applicable
  • recognize the predictive modeling process, including how to explore and understand data, prepare for and model data, and evaluate and deploy the model
  • identify methods for random sampling and use hypothesis testing, Chi-square tests, and correlation
  • recognize common model categories and analytical techniques
  • use Decision Trees and Support Vector Machines for predictive analytics
  • use Survival Analysis to predict and analyze customer churn
  • use Market Basket Analysis to perform predictive analysis
  • apply data clustering models to perform predictive analysis
  • apply random forests for predictive analytics
  • use Naive Bayes classifiers for predictive analytics
  • use Logistic Regression for predictive analytics
  • describe best practices in predictive modeling
  • apply models to perform predictive analytics
  • Course Number:

    Expertise Level