Predictive Analytics for Business

Course Info

Length: 1 Week

City: Kuala Lumpur

Type: In Classroom

Available Dates

  • Dec-30-2024

    Kuala Lumpur

  • Jan-13-2025

    Kuala Lumpur

  • Apr-14-2025

    Kuala Lumpur

  • July-14-2025

    Kuala Lumpur

  • Oct-13-2025

    Kuala Lumpur

Dates in Other Venues

  • Dec-23-2024

    Amsterdam

  • Dec-23-2024

    Istanbul

  • Dec-30-2024

    London

  • Jan-06-2025

    London

  • Jan-06-2025

    Dubai

  • Jan-13-2025

    Istanbul

  • Jan-13-2025

    Paris

  • Jan-13-2025

    Barcelona

  • Jan-13-2025

    Amsterdam

  • Jan-13-2025

    Singapore

  • Apr-14-2025

    Barcelona

  • Apr-14-2025

    London

  • Apr-14-2025

    Singapore

  • Apr-14-2025

    Dubai

  • Apr-14-2025

    Amsterdam

  • Apr-14-2025

    Paris

  • Apr-14-2025

    Istanbul

  • June-09-2025

    London

  • June-09-2025

    Dubai

  • July-07-2025

    London

  • July-07-2025

    Dubai

  • July-14-2025

    Paris

  • July-14-2025

    Amsterdam

  • July-14-2025

    Barcelona

  • July-14-2025

    Istanbul

  • July-14-2025

    Singapore

  • Sep-22-2025

    London

  • Sep-22-2025

    Dubai

  • Oct-13-2025

    Istanbul

  • Oct-13-2025

    Barcelona

  • Oct-13-2025

    Singapore

  • Oct-13-2025

    Amsterdam

  • Oct-13-2025

    Paris

  • Nov-17-2025

    Dubai

  • Nov-17-2025

    London

Course Details

Course Outline

5 days course

Introduction to Predictive Analytics for Business

  • Overview of predictive analytics and its applications in the business world
  • Exploring the different types of predictive models
  • Understanding the data requirements and data sources for predictive analytics
  • Discussing the key steps involved in building a predictive model

 

Data Preparation and Feature Engineering

  • Understanding the importance of data quality in predictive modelling
  • Exploring different techniques for data cleaning and feature engineering
  • Handling missing values, outliers, and imbalanced data
  • Feature selection techniques and methods for creating new features

Supervised Learning Techniques

  • Introduction to supervised learning algorithms such as linear regression, logistic regression, decision trees, and random forests
  • Understanding the assumptions and limitations of each algorithm
  • Evaluating the performance of the models using metrics such as accuracy, precision, and recall
  • Tuning the models to improve their performance

Unsupervised Learning Techniques

  • Introduction to unsupervised learning algorithms such as clustering, dimensionality reduction, and association rules
  • Understanding the assumptions and limitations of each algorithm
  • Evaluating the performance of the models using metrics such as silhouette score, inertia, and lift
  • Applying unsupervised learning to business problems such as customer segmentation and market basket analysis

 

Advanced Topics in Predictive Analytics for Business

  • Feature scaling and normalization techniques
  • Ensemble methods such as bagging, boosting, and stacking
  • Introduction to deep learning algorithms for predictive analytics
  • Deploying predictive models in a business setting

Course Video