Scikit Learn

Scikit-learn is an open-source Python machine learning library used for data analysis, predictive modeling, classification, regression, clustering, preprocessing, model selection, and model evaluation. It provides a simple and efficient framework for building machine learning workflows on Ubuntu 26.04 Azure virtual machines.

The solution supports common machine learning workflows including dataset loading, feature preprocessing, model training, prediction, accuracy testing, and evaluation. It is ideal for data science, AI/ML development, analytics, research, automation, and production-ready machine learning experiments.

Version: Scikit-learn 1.8.0

Product Type: Python Machine Learning Library / AI & Data Science SDK

Features of Scikit-learn:

• Open-source Python library for machine learning and data mining.
• Supports classification, regression, clustering, and dimensionality reduction.
• Includes preprocessing, feature selection, model validation, and evaluation tools.
• Works with popular Python packages such as NumPy, SciPy, pandas, and matplotlib.
• Provides built-in datasets and simple APIs for model training and testing.
• Suitable for AI/ML development, analytics, research, and predictive modeling.

Usage instructions for Scikit-learn on Ubuntu 26.04 Azure
$ sudo su
$ apt update && apt upgrade -y
$ apt install -y python3 python3-pip python3-venv build-essential
$ cd /opt
$ python3 -m venv scikit-learn-env
$ source scikit-learn-env/bin/activate
$ pip install --upgrade pip setuptools wheel
$ pip install -U scikit-learn

Check installed version:
$ python -c "import sklearn; print(sklearn.__version__)"

Testing Scikit-learn:

$ cat > /opt/test_sklearn.py <<'EOF'
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("Scikit-learn test successful")
print("Accuracy:", accuracy)
EOF

$ python /opt/test_sklearn.py

Expected output:
Scikit-learn test successful
Accuracy: 1.0

Access Information:
Scikit-learn does not run as a web service and does not require browser access.

Default Port: Not required
Service Type: Python Library / CLI-based ML Toolkit
Import Name: sklearn
Install Package: scikit-learn

Disclaimer: Scikit-learn is provided “as is” under applicable open-source licenses. Users are responsible for validating machine learning models, datasets, dependencies, security settings, and production configurations. This solution is best suited for machine learning development, testing, data science workflows, and AI/ML experimentation on Ubuntu 26.04 Azure environments.