Training a Linear Regression Model with Couchbase & a Jupyter Notebook: Step-by-Step Tutorial

Jupyter Notebooks are ideal for data visualization and machine learning tasks due to their ability to create shareable documents with narrative text, equations, and other content types. The Couchbase document database can store vast amounts of semi-structured and unstructured data such as social media posts, equations, and more. By establishing connectivity between a Couchbase cluster and a Jupyter Notebook, users can pull data from Couchbase and use it to train linear regression models for machine learning tasks. A sample dataset is loaded into the Couchbase bucket using cbimport, and then connected to in a Jupyter Notebook for analysis. The notebook includes code to create boxplots and scatter plots to detect outliers and determine correlation between variables, split the dataset into training and testing sets, and train a linear regression model using the Ordinary Least Squares method.

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