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Includes classification, regression, clustering, dimensionality reduction, and model selection.
Optimized for performance and scalable to large datasets with clean integration into pipelines.
Works seamlessly with Pandas, NumPy, Matplotlib, and other Python data science tools.
Backed by active contributors and widely used in academia, industry, and Kaggle competitions.
Use pip or conda to install the library along with dependencies like NumPy and SciPy.
Use built-in datasets or load your own using Pandas or NumPy arrays.
Apply scaling, encoding, and feature selection using `sklearn.preprocessing` tools.
Choose an algorithm (e.g., SVM, Random Forest) and fit it to your training data.
Use metrics, cross-validation, and GridSearchCV to assess and optimize performance.
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Generate synthetic data
X, y = make_regression(n_samples=100, n_features=10, noise=0.1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Predict and evaluate
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print("MSE:", mse)Spam detection, image recognition, and medical diagnosis using SVM, logistic regression, etc.
Predict stock prices, housing values, or drug response using linear models and ensembles.
Customer segmentation and pattern discovery using k-Means, DBSCAN, and hierarchical methods.
Use PCA and feature selection to visualize and simplify high-dimensional data.
Explore Scikit-learn’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Scikit-learn’s capabilities, usage, and ecosystem.