Earth's radius calculation

import numpy as np
from sklearn.linear_model import LinearRegression

# Earth's radius in kilometers
earth_radius_km = 6371.0

# Generate some example data (latitude and longitude) for demonstration
# In a real ML project, you would replace this with your actual dataset.
# Let's assume you have latitude and longitude coordinates as your features.
latitude = np.array([40.7128, 34.0522, 51.5074, 48.8566])  # Example latitudes
longitude = np.array([-74.0060, -118.2437, -0.1278, 2.3522])  # Example longitudes

# Calculate distances from the Earth's center (useful for some applications)
distances_km = earth_radius_km * np.sqrt(latitude**2 + longitude**2)

# Create a machine learning model (in this case, a simple linear regression)
model = LinearRegression()

# Train the model using your data, including the distances as a feature
X = np.vstack((latitude, longitude, distances_km)).T  # Stack features vertically
y = np.array([1, 2, 3, 4])  # Replace with your target variable

model.fit(X, y)

# Make predictions using the trained model
# In practice, you would use new data for prediction.
new_latitude = np.array([35.682839])
new_longitude = np.array([139.759455])
new_distances_km = earth_radius_km * np.sqrt(new_latitude**2 + new_longitude**2)

new_data = np.array([[new_latitude, new_longitude, new_distances_km]])
prediction = model.predict(new_data)

print(f"Predicted value for new data: {prediction[0]}")