Predictive Analytics in BI Cheat Sheet

Updated 2026-05-26

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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 21 focused tables and 175 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 IssuesTable 18: Model Interpretability & ExplainabilityTable 19: Survival AnalysisTable 20: Causal Inference & Uplift ModelingTable 21: Model Monitoring & Drift Detection

Table 1: Regression Techniques

Regression is the backbone of continuous-outcome prediction; every BI analyst should know when to use plain linear models versus regularized or logistic variants. The right choice depends on the number of predictors, the presence of multicollinearity, and whether the outcome is continuous or binary.

Technique	Example	Description
Linear Regression	`y = β0 + β1x + ε` `sales = 50 + 3*ads + error`	Predicts continuous outcomes by modeling the linear relationship between dependent and independent variables using ordinary least squares.
Multiple Linear Regression	`y = β0 + β1x1 + β2x2 + ... + βnxn` `sales = 50 + 3ads + 2season`	• Extends simple regression to multiple predictors • each coefficient represents the effect of one variable holding others constant
Logistic Regression	$P(y=1) = \frac{1}{1+e^{-(\beta_0+\beta_1x)}}$ `churn_prob = logit(score)`	Predicts binary outcomes using a sigmoid function — outputs probabilities 0–1 for classification despite the "regression" name.
Ridge Regression (L2)	`Loss = MSE + λ∑β²` `alpha=1.0 in sklearn`	Adds L2 penalty to shrink coefficients toward zero — prevents overfitting without eliminating features.

Table 1: Regression Techniques

Technique	Example	Description
Linear Regression	`y = β0 + β1x + ε` `sales = 50 + 3*ads + error`	Predicts continuous outcomes by modeling the linear relationship between dependent and independent variables using ordinary least squares.
Multiple Linear Regression	`y = β0 + β1x1 + β2x2 + ... + βnxn` `sales = 50 + 3ads + 2season`	• Extends simple regression to multiple predictors • each coefficient represents the effect of one variable holding others constant
Logistic Regression	$P(y=1) = \frac{1}{1+e^{-(\beta_0+\beta_1x)}}$ `churn_prob = logit(score)`	Predicts binary outcomes using a sigmoid function — outputs probabilities 0–1 for classification despite the "regression" name.
Ridge Regression (L2)	`Loss = MSE + λ∑β²` `alpha=1.0 in sklearn`	Adds L2 penalty to shrink coefficients toward zero — prevents overfitting without eliminating features.