Skip to main content

AI Notebooks - Tutorial - Build your spam classifier

Stephanie R.
Updated

Learn how to build a simple spam classifier with OVHcloud AI Notebooks.

You will be able to learn the concepts of logistic regression, dimension reduction, stop words, quantiles, and much more. A very simple Machine Learning model will be used: the logistic regression.

At the end of this tutorial, you will have learned the principal methods to build your own spam classifier.

PaaS_AI_notebooks_spamClassifier01.png

We can create this model with the dataset SMSSPamCollection, which you can find on the SMS Spam Collection Dataset link.

 

Requirements

 

Instructions

You can launch your notebook from the OVHcloud Control Panel or via the ovhai CLI.

 

Launching a Jupyter notebook with "Miniconda" via UI

To launch your notebook from the OVHcloud Control Panel, use the following specifications when creating your AI Notebook.

 

Code editor

Choose the Jupyterlab code editor.

 

Framework

In this tutorial, the Miniconda framework is used.

NOTE: With Miniconda, you will be able to set up your environment by installing the Python libraries you need.

You can choose the conda version you want.

The default version of conda is functional for this tutorial.

 

Resources

You can choose the number of CPUs or GPUs you want.

Here, using 4 CPU is sufficient.

 

Launching a Jupyter notebook with "Miniconda" via CLI

If you want to launch it with the CLI, choose the jupyterlab editor and the conda framework.

To access the different versions of conda available, run the following command.

ovhai capabilities framework list -o yaml

If you do not specify a version, your notebook starts with the default version of conda.

Choose the number of CPUs (<nb-cpus>) to use in your notebook and use the following command.

ovhai notebook run conda jupyterlab \
    --name <notebook-name> \
    --framework-version <conda-version> \
  --cpu <nb-cpus>

You can then reach your notebook’s URL once the notebook is running.

 

Accessing the notebook

Once the repository has been cloned, find your notebook by following this path: ai-training-examples > notebooks > natural-language-processing > text-classification > miniconda > spam-classifier > notebook-spam-classifier.ipynb.

A preview of this notebook can be found on GitHub here.

 

Go further

For more information and tutorials, please see our other AI & Machine Learning support guides or explore the guides for other OVHcloud products and services.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

Related articles