# Data Science Lab

- [DS Model-to-Sandbox Pipeline](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/how-to-guides-and-tutorials/data-science-lab/ds-model-to-sandbox-pipeline.md): To create a project in DS Lab, train and register a model, and build a pipeline that writes model outputs to the Data Sandbox for analysis and reuse.
- [Create Forecasting Models in DS Lab](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/how-to-guides-and-tutorials/data-science-lab/create-forecasting-models-in-ds-lab.md): Workflow 2 in DS Lab demonstrates the power and simplicity of creating and optimizing forecasting models by leveraging in-built algorithms, boilerplate code, and utility/artifact features.
- [Perform Churn Analysis Using DS Lab and Explainable AI](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/how-to-guides-and-tutorials/data-science-lab/perform-churn-analysis-using-ds-lab-and-explainable-ai.md): To perform churn analysis using DS Lab Notebooks, apply explainable AI for model insights, and integrate results into a pipeline and data sandbox to drive customer retention strategies.
- [Build and Deploy a Sentiment Analysis Model as an API in DS Lab](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/how-to-guides-and-tutorials/data-science-lab/build-and-deploy-a-sentiment-analysis-model-as-an-api-in-ds-lab.md): To build a Sentiment Analysis model in DS Lab, register it as an API through the Admin Module, and validate its response via API requests.
- [Leverage AutoML for Super Market Data](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/how-to-guides-and-tutorials/data-science-lab/leverage-automl-for-super-market-data.md): Harnessing AutoML for Supermarket data to apply preparation, classification, and regression for uncovering insights and driving data-driven decisions.


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