Predictive Analytics

Build models to forecast outcomes and estimate future values.

Purpose

Streamlines the development and evaluation of machine learning and time series models to forecast outcomes and identify critical predictive drivers in tabular data.

Key functions

• CreateARIMAModel — Fits and evaluates Autoregressive Integrated Moving Average models for sequential time series forecasting.

• CreateBoostedTreeModel — Builds high-performance gradient boosted ensembles using XGBoost for complex classification and regression tasks.

• CreateDecisionTreeModel — Generates interpretable hierarchical models with visual decision paths and rule-based logic.

• CreateExponentialSmoothingModel — Implements Holt-Winters techniques to project future trends with automated seasonality detection.

• CreateLinearRegressionModel — Estimates relationships between variables to forecast continuous outcomes and assess predictor influence.

• CreateLogisticRegressionModel — Models the probability of binary outcomes to classify observations and evaluate categorical risk.

• CreateNeuralNetwork_SingleOutcome — Constructs deep learning architectures using TensorFlow to model non-linear interactions in high-dimensional data.

Common use cases

• Projecting financial performance and inventory requirements using seasonal forecasting models.

• Identifying high-risk scenarios and customer churn patterns through ensemble learning.

• Quantifying the ROI of marketing and operational drivers using multi-variable regression.

• Automating the deployment of deep learning models for complex, non-linear predictive challenges.

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