feat: add scikit-learn classification example using logistic regression

advanced/scikit_classification.py

@@ -0,0 +1,39 @@
+#scikit classification example - golixco
+"""
+Advanced Example: Scikit-learn Classification
+
+This script demonstrates using scikit-learn to perform a classification task.
+It loads a built-in dataset (breast cancer), preprocesses features,
+trains a logistic regression model, and evaluates performance on a test set.
+"""
+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.metrics import accuracy_score, classification_report
+
+# Load the dataset: features (X) and binary target (y)
+data = load_breast_cancer()
+X, y = data.data, data.target  # Features and labels
+
+# Split into training and testing sets (70% train, 30% test)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42)
+
+# Preprocess features by standardizing (mean=0, variance=1)
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)  # fit on train, transform train
+X_test_scaled = scaler.transform(X_test)        # use same transform on test
+
+# Train a logistic regression classifier
+clf = LogisticRegression()
+clf.fit(X_train_scaled, y_train)  # fit model to training data
+
+# Predict on the test set
+y_pred = clf.predict(X_test_scaled)
+
+# Evaluate the classifier: accuracy and detailed report
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Test Accuracy: {accuracy:.2f}")
+print(classification_report(y_test, y_pred, target_names=data.target_names))
+