Scikit-learn is the go-to library for classical machine learning in Python. It provides consistent APIs for preprocessing, model training, and evaluation.

Installation 

  pip install scikit-learn pandas numpy matplotlib

Load and Explore Data 

  from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['species'] = df['target'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})

print(df.head())
print(df.describe())

Train/Test Split

Always evaluate on data the model hasn’t seen:

  from sklearn.model_selection import train_test_split

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, stratify=y
)

Classification 

  from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
print(classification_report(y_test, y_pred, target_names=iris.target_names))

Preprocessing Pipeline

Combine preprocessing and modeling into a single pipeline:

  from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', SVC(kernel='rbf')),
])

pipeline.fit(X_train, y_train)
score = pipeline.score(X_test, y_test)
print(f"Pipeline accuracy: {score:.2f}")

Regression 

  from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

housing = fetch_california_housing()
X_train, X_test, y_train, y_test = train_test_split(
    housing.data, housing.target, test_size=0.2, random_state=42
)

model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.2f}")
print(f"R²: {r2_score(y_test, y_pred):.2f}")

Clustering 

  from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

X, _ = make_blobs(n_samples=300, centers=4, random_state=42)

kmeans = KMeans(n_clusters=4, random_state=42)
labels = kmeans.fit_predict(X)
centers = kmeans.cluster_centers_

Cross-Validation

More reliable than a single train/test split:

  from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')
print(f"CV scores: {scores}")
print(f"Mean: {scores.mean():.2f} (+/- {scores.std() * 2:.2f})")

Hyperparameter Tuning 

  from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20],
}

grid = GridSearchCV(RandomForestClassifier(), param_grid, cv=5, scoring='accuracy')
grid.fit(X_train, y_train)
print(f"Best params: {grid.best_params_}")
print(f"Best score: {grid.best_score_:.2f}")

Scikit-learn’s consistent API makes it easy to experiment with different algorithms and find the best model for your data.