# Predictive Analytics

### 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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