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  1. Python ML examples
    Diverse Python machine learning examples covering algorithms, data preparation, and practical applications
  2. Copilot Search Branding

    Machine Learning in Python can be implemented quickly using scikit-learn. Below is a minimal example that loads the famous Iris dataset, trains a model, and makes predictions.

    # Import libraries
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC
    from sklearn.metrics import accuracy_score

    # Load dataset
    iris = load_iris()
    X, y = iris.data, iris.target

    # Split into training and test sets (80% train, 20% test)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create and train the model (Support Vector Classifier)
    model = SVC(gamma='auto')
    model.fit(X_train, y_train)

    # Make predictions
    predictions = model.predict(X_test)

    # Evaluate accuracy
    print("Accuracy:", accuracy_score(y_test, predictions))
    Copied!

    This script:

    • Loads a dataset (load_iris from scikit-learn).

    • Splits it into training and testing sets.

    • Trains a Support Vector Machine classifier.

    • Predicts on unseen data.

    • Prints the accuracy score.

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