AI Notebooks - Tutorial - Build your spam classifier

How to build your Spam classifier thanks to Machine Learning

Last updated 22nd November, 2022.


This tutorial will show you 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 learnt the principal methods to build your own spam classifier.


We will be able to create this model with the dataset named SMSSPamCollection. Find it on the SMS Spam Collection Dataset link.



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, refer to the following steps.

Code editor

Choose the Jupyterlab code editor.


In this tutorial, the Miniconda framework is used.

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.


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

  • If you are interested in NLP (Natural Language Processing), familiarise yourself with speech to text by following this tutorial.


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