How to install scikit-learn on Windows

Windows data-science projects often share a workstation with notebooks, editors, and several Python runtimes. Installing scikit-learn inside a project virtual environment keeps its numerical dependencies tied to that project instead of changing the base runtime used by other tools.

The supported Windows path uses 64-bit Python 3, the built-in venv module, and pip. Activating .venv\Scripts\Activate.ps1 in PowerShell places the project interpreter first on PATH, and running python -m pip keeps the package operation bound to that interpreter.

Use a recent 64-bit Python release that has compatible scikit-learn wheels. If pip cannot find a matching distribution, update Python before trying source builds or compiler workarounds. A package metadata check, dependency check, and small estimator run confirm that the active environment can import sklearn and execute model code.

1. Open PowerShell in the project directory where scikit-learn should be installed.
2. Confirm that Python 3 responds from the current shell.

   PS C:\Users\analyst\ml-project> python --version
   Python 3.14.6

   If python is missing or opens the Microsoft Store, install or repair Python before creating the environment.
   Related: Install Python on Windows

3. Confirm that the runtime is 64-bit.

   PS C:\Users\analyst\ml-project> python -c "import platform; print(platform.architecture()[0])"
   64bit

   If this prints 32bit, install a 64-bit Python runtime before continuing because current scikit-learn wheels target 64-bit Windows.

4. Create a project virtual environment in .venv.

   PS C:\Users\analyst\ml-project> python -m venv .venv

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

5. Activate the virtual environment in the current PowerShell session.

   PS C:\Users\analyst\ml-project> .\.venv\Scripts\Activate.ps1
   (.venv) PS C:\Users\analyst\ml-project>

   If PowerShell blocks .venv\Scripts\Activate.ps1, confirm the policy with your administrator before changing the CurrentUser execution policy. Python documents Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser as the user-scoped fix.

   Related: Activate a Python virtual environment

6. Confirm that pip belongs to the activated environment.

   (.venv) PS C:\Users\analyst\ml-project> python -m pip --version
   pip 26.1.2 from C:\Users\analyst\ml-project\.venv\Lib\site-packages\pip (python 3.14)

   Use python -m pip instead of a standalone pip command when several Python installs are present.
   Related: Install pip on Windows

7. Install scikit-learn into the activated environment.

   (.venv) PS C:\Users\analyst\ml-project> python -m pip install --upgrade scikit-learn
   Collecting scikit-learn
     Downloading scikit_learn-1.9.0-cp314-cp314-win_amd64.whl.metadata (11 kB)
   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

   --upgrade asks pip for the newest compatible release visible to the active package index. Exact wheel names and dependency versions change over time.

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

   (.venv) PS C:\Users\analyst\ml-project> 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: C:\Users\analyst\ml-project\.venv\Lib\site-packages
   Requires: joblib, narwhals, numpy, scipy, threadpoolctl
   Required-by:

   The Location path should point inside the project environment, not C:\Program Files\Python314 or another base interpreter location.

9. Check the installed dependency set.

   (.venv) PS C:\Users\analyst\ml-project> python -m pip check
   No broken requirements found.

   If this command reports a conflict, recreate the environment or install the project's tested dependency set before training models.

10. Run an estimator smoke test through the active environment.

    (.venv) PS C:\Users\analyst\ml-project> @'
    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"first prediction: {model.predict([X[0]])[0]}")
    '@ | python
    scikit-learn: 1.9.0
    classes: [0, 1, 2]
    first prediction: 0