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# An ML pipeline in Python (scikit-learn)
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
# 1) Load data
X, y = load_breast_cancer(return_X_y=True)
# 2) Train/test split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# 3) Build pipeline: scale -> logistic regression
clf = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
# 4) Train
clf.fit(X_train, y_train)
# 5) Evaluate
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"Accuracy: {acc:.3f}")
Hello, I'm Ardavan
A physicist transitioned to data science. I have over 14 years of experience in data roles — including serving as a Data Science Fellow at the National Library of Medicine and as an author with the CMS experiment, where I analyzed terabyte-scale data.
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