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import numpy as np
from sklearn.linear_model import LinearRegression

# Sample data
aviator_coefficients = np.array([1, 2, 3, 4, 5])
multiplier = 2  # You can adjust this multiplier based on your requirements

# Generating corresponding dependent variable values
aviator_values = multiplier * aviator_coefficients

# Reshape the data for fitting into the model
aviator_coefficients = aviator_coefficients.reshape(-1, 1)
aviator_values = aviator_values.reshape(-1, 1)

# Creating and fitting the linear regression model
model = LinearRegression().fit(aviator_coefficients, aviator_values)

# Predicting future values
future_coefficients = np.array([6, 7, 8])
future_coefficients = future_coefficients.reshape(-1, 1)

predicted_values = model.predict(future_coefficients)

# Displaying the predictions
for i in range(len(future_coefficients)):
    print(f"Predicted value for Aviator coefficient {future_coefficients[i][0]}: {predicted_values[i][0]}")
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