scikit-learn
The standard machine learning library for Python — clean API for classification, regression, clustering, and preprocessing.
You have labeled data and want to build a predictive model. Or unlabeled data you want to cluster. Or features you need to preprocess. scikit-learn gives you consistent, well-tested implementations of hundreds of ML algorithms without having to implement them from scratch.
Everything follows the same pattern: create an estimator (model), call .fit(X, y) to train, call .predict(X) to infer. Transformers additionally have .transform() and .fit_transform(). Pipelines chain these steps so preprocessing + model become one object.
Train a classifier with a pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import numpy as np
# Load your data
X, y = np.random.randn(500, 10), np.random.randint(0, 2, 500)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
pipe = Pipeline([
('scaler', StandardScaler()),
('clf', RandomForestClassifier(n_estimators=100, random_state=42))
])
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
print(classification_report(y_test, y_pred))Use it when
Classical ML (not deep learning): tabular data, structured features, interpretable models, baseline modeling, feature engineering, cross-validation, and hyperparameter tuning.
Skip it when
Deep learning / neural networks (use PyTorch or TensorFlow), unstructured data like images or text at scale, real-time inference at very high throughput.
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