Predictive analytics
Predictive analytics is the use of data and statistical or computational methods to estimate future or otherwise unknown outcomes.
The Art of AI Product Development
The analysis of data with statistical or machine-learning methods to estimate future or unknown outcomes.
- How it is used
- Supports defined product decisions through forecasting, classification, anomaly detection, and recommendations.
- How deep it goes
- Defines predictive AI and its data needs through product examples, but does not work through model training or mathematics.
- Examples
- Uses movie-review sentiment, spam filtering, entertainment recommendations, and personalized fashion to show analytical tasks in products.
- What you can do after
- Evaluate/understand whether a defined analytical task warrants a predictive model instead of a generative one, considering data and delivery constraints.
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Effective Data Analysis
Predictive modeling is the construction and use of models to generate predictions for new or unknown observations.
- How it is used
- Supports estimates for new cases, framed around stakeholder questions such as house prices, sign-ups, rat sightings, and product margins.
- How deep it goes
- Explains fitting and applying a regression equation, outlines question-led modeling, and calls for diagnostics without detailing every testing procedure.
- Examples
- Uses house-price estimation and weather-linked rat sightings, then poses a product-profit-margin modeling exercise.
- What you can do after
- Build it from scratch by fitting a predictive model, assessing its metrics, and planning an adjustable profit-margin tool for stakeholders.
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Data-Driven Cybersecurity
Predictive modeling is the construction of statistical or computational models that estimate likely future or unknown outcomes from data.
- How it is used
- Supports proactive cybersecurity decisions by forecasting incidents and directing attention toward likely threats.
- How deep it goes
- Defines the approach and works through risk metrics and a dashboard exercise, but defers a deep dive into statistics and AI.
- Examples
- Uses a financial-services dashboard with malware likelihood, incident volume, response time, and before-and-after risk scores.
- What you can do after
- Apply it via risk and threat indexes to set targets, prioritize defenses, and assess measured improvement.
Read how Data-Driven Cybersecurity explains predictive analytics →
Related concepts
- labeled datauses · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
- linear regressionuses · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
- threat detectionused by · Threat detection is the identification of activity or conditions that may indicate a security threat, attack, or compromise.
- machine learninguses · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- historical datauses · Historical data is information recorded about past events, observations, outcomes, behaviors, or states and retained for later access or analysis.
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