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Now, how do **Scikit-learn** and **Spark** play together? Although Scikit-learn is designed to run on a single machine, you can integrate it with Spark for larger datasets. This combination allows you to leverage Spark’s distributed computing capabilities for data preprocessing and feature engineering, which are then fed into Scikit-learn models. Imagine the following scenario: You have a *huge dataset* that needs extensive preprocessing before you can apply a machine-learning model. This is where Spark steps in. You can use Spark to load the data, clean it, transform features, and prepare it for model training. Once the data is preprocessed, you can pass it to Scikit-learn to train and evaluate your models. The key to this is a library called **Spark MLlib**, which provides some of the algorithms from Scikit-learn to be executed on Spark clusters. But it is important to know that MLlib may not contain all Scikit-learn algorithms.
