# AutoML Notebook Solutions

Treasure AutoML provides multiple solution notebooks:
![](/assets/solution-package-tree.d1ed4d1f9969467e78b49eb82c7bf8bb830e4c66060576d2d023c489d71ac40f.3cb60505.png)

Fundamental notebooks include AutoGluon classification/regression, time‑series forecasting, EDA and SHAP explanations, plus notebooks for preparing sample ML datasets. Current solution examples: Next Best Actions, Multi-Touch Attribution, Network Analysis.

## Base Package

The base package includes the fundamental classification/regression notebook using AutoGluon. AutoGluon uses multi‑layer stacking to combine diverse ML models. See the [AutoGluon documentation](https://auto.gluon.ai/stable/index.html).

It also includes a notebook to prepare sample ML datasets for a quick start.

| **Base Package** | **Description** |
|  --- | --- |
| [Gluon Train](/products/customer-data-platform/machine-learning/automl/notebook-solutions/gluon-train) | Train a model using the AutoGluon library on an input training table. |
| [Gluon Predict](/products/customer-data-platform/machine-learning/automl/notebook-solutions/gluon-predict) | Use the model created by Gluon Train to predict values. |
| [ML Datasets](/products/customer-data-platform/machine-learning/automl/notebook-solutions/ml-datasets) | Load sample ML datasets as TD data tables. |


## Solution Packages

Pre‑configured solution packages address specific business use cases.

| **Solution Package** | **Description** |
|  --- | --- |
| [Next Best Action](/products/customer-data-platform/machine-learning/automl/notebook-solutions/next-best-action) | Predict which marketing action is most likely to increase customer value based on past behavior. |
| [Multi-touch Attribution (MTA)](/products/customer-data-platform/machine-learning/automl/notebook-solutions/multi-touch-attribution) | Allocate credit to journey touchpoints toward KPIs (conversion, etc.). |
| [Time Series Forecasting](/products/customer-data-platform/machine-learning/automl/notebook-solutions/time-series-forecasting) | Predict potential future values from historical data to inform strategy. |
| [Exploratory Data Analysis](/products/customer-data-platform/machine-learning/automl/notebook-solutions/exploratory-data-analysis) | Graphical/statistical analysis to explore data and prepare for modeling. |
| [SHAP Analysis](/products/customer-data-platform/machine-learning/automl/notebook-solutions/shap-analysis) | Compute Shapley values for local/global model explainability. |
| [Network Analysis](/products/customer-data-platform/machine-learning/automl/notebook-solutions/network-analysis) | Generate Sankey diagrams and network plots for web access path insights. |
| [RFM Analysis](/products/customer-data-platform/machine-learning/automl/notebook-solutions/rfm-analysis) | Segment customers by recency, frequency, monetary value (RFM). |
| [Clustering](/products/customer-data-platform/machine-learning/automl/notebook-solutions/clustering) | Create clusters (K-means) with feature importance & SHAP explanations. |
| [CLTV Prediction](/products/customer-data-platform/machine-learning/automl/notebook-solutions/cltv-prediction) | Estimate Customer Lifetime Value (CLTV). |