How to install scikit-learn on Ubuntu or Debian

On Ubuntu and Debian, operating-system tools and user projects can otherwise share the same Python interpreter. A project virtual environment gives scikit-learn its own package directory, so model code can use published wheels without placing pip packages into system-managed paths.

APT should provide Python 3, venv, and pip, while the activated virtual environment receives scikit-learn from PyPI. The upstream installation path recommends an isolated environment on Linux because mixing pip packages with distribution-managed Python files can create dependency conflicts.

The Debian and Ubuntu repositories also provide python3-sklearn packages for hosts that must stay entirely distribution-managed. A project-local .venv is the better fit when the project needs the current upstream scikit-learn release and a quick estimator smoke test confirms that the active interpreter can import sklearn and run model code.

1. Open a terminal with sudo privileges.
2. Refresh the APT package index.

   $ sudo apt update

3. Install Python 3, venv, and pip.

   $ sudo apt install --assume-yes python3 python3-venv python3-pip
   Reading package lists...
   Building dependency tree...
   Reading state information...
   python3 is already the newest version (3.14.3-0ubuntu2).
   Installing:
     python3-pip  python3-venv
   ##### snipped #####
   Setting up python3-pip (25.1.1+dfsg-1ubuntu2) ...
   Setting up python3-venv (3.14.3-0ubuntu2) ...

   The exact package versions change with the active Ubuntu or Debian release. python3-venv provides the virtual-environment module, and python3-pip provides the package installer used inside that environment.

4. Confirm that Python 3 is available.

   $ python3 --version
   Python 3.14.4

5. Create a project directory.

   $ mkdir -p ~/ml-project

6. Change to the project directory.

   $ cd ~/ml-project

7. Create a virtual environment in .venv.

   $ python3 -m venv .venv

   The command normally returns no output when the environment is created.
   Related: Create a Python virtual environment

8. Activate the virtual environment in the current shell.

   $ source .venv/bin/activate

   Activation changes only the current terminal session. Run the same command again in each new terminal before using the project environment.
   Related: Activate a Python virtual environment

9. Confirm that python resolves inside .venv.

   (.venv) $ command -v python
   /home/analyst/ml-project/.venv/bin/python

10. Upgrade pip inside the virtual environment.

    (.venv) $ python -m pip install --upgrade pip
    Requirement already satisfied: pip in ./.venv/lib/python3.14/site-packages (25.1.1)
    Collecting pip
    ##### snipped #####
    Successfully installed pip-26.1.2

11. Install scikit-learn into the activated environment.

    (.venv) $ python -m pip install --upgrade scikit-learn
    Collecting scikit-learn
    Collecting numpy>=1.24.1 (from scikit-learn)
    Collecting scipy>=1.10.0 (from scikit-learn)
    Collecting joblib>=1.4.0 (from scikit-learn)
    Collecting narwhals>=2.0.1 (from scikit-learn)
    Collecting threadpoolctl>=3.5.0 (from scikit-learn)
    ##### snipped #####
    Installing collected packages: threadpoolctl, numpy, narwhals, joblib, scipy, scikit-learn
    Successfully installed joblib-1.5.3 narwhals-2.22.1 numpy-2.5.0 scikit-learn-1.9.0 scipy-1.18.0 threadpoolctl-3.6.0

    Use python -m pip from the activated environment instead of sudo pip. The exact wheel and dependency versions depend on the active Python version, CPU architecture, and package index state.

12. Confirm that pip recorded scikit-learn inside .venv.

    (.venv) $ python -m pip show scikit-learn
    Name: scikit-learn
    Version: 1.9.0
    Summary: A set of python modules for machine learning and data mining
    Home-page: https://scikit-learn.org
    ##### snipped #####
    Location: /home/analyst/ml-project/.venv/lib/python3.14/site-packages
    Requires: joblib, narwhals, numpy, scipy, threadpoolctl
    Required-by:

13. Check the installed dependency metadata.

    (.venv) $ python -m pip check
    No broken requirements found.

    If pip check reports a conflict, recreate the virtual environment or pin compatible package versions before using it for training.

14. Run an estimator smoke test through the active interpreter.

    (.venv) $ python - <<'PY'
    import sklearn
    from sklearn.datasets import load_iris
    from sklearn.tree import DecisionTreeClassifier
    
    X, y = load_iris(return_X_y=True)
    model = DecisionTreeClassifier(random_state=0).fit(X, y)
    
    print(f"scikit-learn: {sklearn.__version__}")
    print(f"classes: {model.classes_.tolist()}")
    print(f"training accuracy: {model.score(X, y):.1f}")
    PY
    scikit-learn: 1.9.0
    classes: [0, 1, 2]
    training accuracy: 1.0