Part 4: Predictive Analytics

Part 4: Predictive Analytics

Predictive Modelling Basics

This section explores how predictive models leverage historical data to forecast outcomes or probabilities, emphasizing the alignment between modeling objectives and business goals. It also highlights typical steps such as data preparation, exploratory analysis, model selection, and final implementation to ensure models add real-world value

Learning Objectives

Define predictive modeling and explain its importance, identify key application areas across various industries, and outline the general process of building and implementing predictive models.

Indicative Content

  • Definition and Importance

    • A statistical model designed to predict an outcome or the probability of an outcome

    • Built using historical or purposely collected data

    • Applications in industries such as finance, healthcare, telecommunications, retail, and more

  • General Process

    • Setting Business Goals: Align model objectives with organizational needs

    • Data Understanding & Preprocessing: Identify data sources, handle missing values or outliers, and ensure data quality

    • Exploratory Data Analysis (EDA): Use summary statistics and visualizations to uncover patterns or anomalies

    • Model Identification, Selection, and Validation: Choose an appropriate technique (e.g., regression, classification) and validate performance (e.g., via hold-out sets or cross-validation)

    • Model Implementation: Integrate the final model into real-world processes or systems for practical use

Key Statistical Models in Predictive Analytics

Learning Objectives

Provide an overview of the main statistical models used in predictive analytics, emphasizing how regression, classification, and time-to-event models address different types of predictive tasks.

Indicative Content

  • Overview of Statistical Models

    • Core goal: Forecast a numeric outcome, classify categories, or estimate time to an event

    • Driven by the nature of the dependent variable (continuous, categorical, or time-based)

  • Role of Regression Models

    • Linear Regression: Predicts continuous outcomes

    • Logistic Regression: Predicts binary or multi-class outcomes (classification)

  • Role of Classification Models

    • Discrete or categorical dependent variables

    • Techniques include logistic regression and advanced classification algorithms

  • Role of Time-to-Event Models

    • Focus on estimating the time until an event occurs (e.g., survival analysis)

    • Useful in domains like healthcare (patient survival times) or warranty analysis