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svm_classifier / data_generator.py

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62004ae almost 2 years ago

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import numpy as np
import pandas as pd
from sklearn.datasets import make_moons

def generate_simulated_data():
    """Generates a simulated business classification dataset and saves it to a CSV file."""
    np.random.seed(42)
    X, y = make_moons(n_samples=300, noise=0.2, random_state=42)
    
    # Convert to DataFrame
    df = pd.DataFrame(X, columns=["Feature1", "Feature2"])
    df["Target"] = y
    
    # Save to CSV
    df.to_csv("business_data.csv", index=False)

if __name__ == "__main__":
    generate_simulated_data()
    print("Simulated business dataset saved as 'business_data.csv'.")

	import numpy as np
	import pandas as pd
	from sklearn.datasets import make_moons

	def generate_simulated_data():
	"""Generates a simulated business classification dataset and saves it to a CSV file."""
	np.random.seed(42)
	X, y = make_moons(n_samples=300, noise=0.2, random_state=42)

	# Convert to DataFrame
	df = pd.DataFrame(X, columns=["Feature1", "Feature2"])
	df["Target"] = y

	# Save to CSV
	df.to_csv("business_data.csv", index=False)

	if __name__ == "__main__":
	generate_simulated_data()
	print("Simulated business dataset saved as 'business_data.csv'.")