Predictive Analytics in BI Cheat Sheet

Updated 2026-03-18

Predictive analytics in business intelligence applies statistical algorithms, machine learning techniques, and historical data to forecast future outcomes and identify trends. It sits at the intersection of data science, BI tools, and business strategy, transforming raw data into actionable foresight that drives proactive decision-making. Unlike descriptive analytics that explains what happened, predictive analytics answers what will likely happen and why. The key mental model to remember: predictive analytics builds mathematical representations of reality (models) that capture patterns from the past to project into the future—but model quality depends entirely on data quality, feature selection, and validation rigor.

What This Cheat Sheet Covers

This topic spans 17 focused tables and 116 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Regression TechniquesTable 2: Time Series Forecasting MethodsTable 3: Classification AlgorithmsTable 4: Ensemble MethodsTable 5: Model Validation TechniquesTable 6: Regression Accuracy MetricsTable 7: Classification MetricsTable 8: Feature Engineering TechniquesTable 9: Feature Selection MethodsTable 10: Correlation and Statistical AnalysisTable 11: Trend Analysis TechniquesTable 12: Anomaly and Outlier DetectionTable 13: Confidence and Prediction IntervalsTable 14: What-If and Scenario AnalysisTable 15: Hyperparameter Tuning MethodsTable 16: Business Applications of Predictive AnalyticsTable 17: Data Quality and Preprocessing Issues

Table 1: Regression Techniques

Technique	Example	Description
Linear Regression	`y = β0 + β1x + ε` `sales = 50 + 3*ads + error`	• Predicts continuous outcomes by modeling linear relationship between dependent and independent variables • foundational method using ordinary least squares.
Multiple Linear Regression	`y = β0 + β1x1 + β2x2 + ... + βnxn` `sales = 50 + 3ads + 2season`	• Extends simple linear regression to multiple predictors simultaneously • each coefficient represents the effect of one variable holding others constant.
Polynomial Regression	`y = β0 + β1x + β2x² + β3x³` `revenue = 10 + 5time + 0.2time²`	• Models nonlinear relationships by adding polynomial terms • captures curved patterns but risks overfitting with high-degree polynomials.
Ridge Regression (L2)	`Loss = MSE + λ∑β²` `alpha=1.0 in sklearn`	• Adds L2 penalty to reduce coefficient magnitudes • prevents overfitting by shrinking coefficients toward zero without eliminating features entirely.

Table 1: Regression Techniques

Technique	Example	Description
Linear Regression	`y = β0 + β1x + ε` `sales = 50 + 3*ads + error`	• Predicts continuous outcomes by modeling linear relationship between dependent and independent variables • foundational method using ordinary least squares.
Multiple Linear Regression	`y = β0 + β1x1 + β2x2 + ... + βnxn` `sales = 50 + 3ads + 2season`	• Extends simple linear regression to multiple predictors simultaneously • each coefficient represents the effect of one variable holding others constant.
Polynomial Regression	`y = β0 + β1x + β2x² + β3x³` `revenue = 10 + 5time + 0.2time²`	• Models nonlinear relationships by adding polynomial terms • captures curved patterns but risks overfitting with high-degree polynomials.
Ridge Regression (L2)	`Loss = MSE + λ∑β²` `alpha=1.0 in sklearn`	• Adds L2 penalty to reduce coefficient magnitudes • prevents overfitting by shrinking coefficients toward zero without eliminating features entirely.