Run Scikit-learn Online – Free Sklearn Online Compiler

Train machine learning models in your browser with our free online sklearn compiler. No installation or signup required - Try It Now.

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Try This Scikit-learn Example

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, classification_report

# --- Load the classic Iris dataset ---
iris = load_iris()
X, y = iris.data, iris.target

# --- Split into training and test sets ---
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

print(f"Training samples: {len(X_train)}")
print(f"Test samples:     {len(X_test)}")

# --- Train a Random Forest classifier ---
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# --- Evaluate on the test set ---
y_pred = clf.predict(X_test)
print(f"\nAccuracy: {accuracy_score(y_test, y_pred):.2%}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))
Open in full editor →Loads instantly in your browser. No install.

What You Can Do With Scikit-learn Online

Classification & Regression

Train classifiers like RandomForest, SVM, and LogisticRegression, or build regressors with LinearRegression and DecisionTreeRegressor — all running directly in your browser.

Built-in Datasets & Metrics

Load classic datasets like Iris, Wine, and Digits instantly. Evaluate models with accuracy_score, classification_report, confusion_matrix, and more.

No Setup Needed

Scikit-learn, NumPy, and Pandas are pre-installed. Open the editor and start building ML models immediately — zero configuration required.

How to Build ML Models with Scikit-learn Online

Ready to train machine learning models without any local setup? Our sklearn online compiler gives you immediate access to the full scikit-learn library right inside your browser. Here is a quick workflow:

  1. Import the Library: Start by typing from sklearn.ensemble import RandomForestClassifier or any estimator you need in the code editor.
  2. Load or Create Data: Use built-in datasets like load_iris() or create your own with NumPy arrays and Pandas DataFrames.
  3. Train Your Model: Split data with train_test_split(), then call .fit() on your chosen estimator to train it.
  4. Evaluate Results: Use accuracy_score(), classification_report(), or confusion_matrix() to measure model performance and print the results.

If you want to dive deeper into feature engineering, hyperparameter tuning, or pipelines, head over to the official scikit-learn documentation.

Frequently Asked Questions

Can I run scikit-learn online without installing Python?

Yes. PythonHere runs Python entirely in your browser using WebAssembly (Pyodide). Scikit-learn, NumPy, and Pandas are pre-loaded — no installation required.

Which sklearn models are supported?

All major scikit-learn estimators are available — classification (RandomForest, SVM, KNN), regression (LinearRegression, Ridge), clustering (KMeans, DBSCAN), and preprocessing tools (StandardScaler, LabelEncoder).

Is it free?

100% free, forever. No account, no credit card, no time limit.

Can I use NumPy and Pandas with sklearn here?

Yes. NumPy and Pandas are available alongside scikit-learn. Use import numpy as np and import pandas as pd directly in the editor.

Start Running Scikit-learn in Your Browser

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