- Jupyter notebook tutorial français how to#
- Jupyter notebook tutorial français full#
- Jupyter notebook tutorial français code#
Import the required libraries, shown in the screenshot here. Use your username and password, of course, instead of Administrator and 123456. 
Jupyter notebook tutorial français code#
In the new Jupyter Notebook, use the code shown below to connect to the Couchbase server. The categorical variables in our example are TV, Radio and Newspaper. We will use a simple linear regression equation in our example. The simplest form of the regression equation is y = mx + c, where y represents the target variable, x represents a single categorical variable and m and c are constants. The linear regression model is powerful for predictive analysis, letting us determine the strength of categorical or independent variables and forecasting the effect of those variables and identifying trends in the data.Īs you might deduce from the name linear regression, the “curve” we use to fit the data is a line. In the Admin Console, create a primary index for the data bucket to make the data queryable, as you see here:. Your Documents menu should now look something like this: Select the file you wish to import and the data bucket where the documents reside: Go to Documents > Import, as shown here:. This example uses the Advertising Dataset from Kaggle. The file can be any data you want to work on. For this example we’ll upload a CSV file using cbimport. Or uploading a list of JSON documents or a CSV file. In the Couchbase cluster’s Admin Console, go to Buckets > Add Bucket to create a new bucket, as shown here:Īdd documents to your bucket by either navigating to Documents > Add Document, like this:. 
To kick things off, proceed through these steps to load the sample dataset: We’ll walk through an example to predict a target variable’s value using categorical variables via a linear regression equation.
Jupyter notebook tutorial français how to#
In this post, you’ll learn how to establish connectivity between a Couchbase cluster and a Jupyter Notebook, then pull data from Couchbase and use it to train a linear regression model for machine learning.
Jupyter notebook tutorial français full#
Couchbase lets you store and process vast amounts of data (semi-structured and unstructured) at scale and support the kinds of data the world is full of: narrative text (social media posts, etc.), equations and more. Why? The Jupyter Notebook web application lets you create and share documents that contain narrative text, equations and the like for use cases such as data visualization and machine learning. Data scientists love Jupyter Notebooks – and it makes a natural pairing with the Couchbase document database.
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