statsmodels valueerror: data type not inexact in python

The ValueError: data type not inexact error in Statsmodels usually occurs when you try to fit a model with non-floating-point data. This error is specifically thrown when the data type is not an "inexact" or floating-point data type.

To resolve this error, you can ensure that your data is in a correct floating-point format before fitting the model. Here's an example:

main.py
import statsmodels.api as sm
import numpy as np

# Create your data (example)
X = np.array([1, 2, 3, 4, 5])  # Non-floating-point data
y = np.array([2, 4, 6, 8, 10])

# Convert X to floating-point format
X = X.astype(float)

# Fit the model
model = sm.OLS(y, X)
results = model.fit()

# Print the results
print(results.summary())
330 chars
17 lines

In this example, we convert the non-floating-point array X to the floating-point format using the astype method from the NumPy library. This ensures that the X variable has the correct data type required by the Statsmodels library.

By following this approach, you can avoid the ValueError: data type not inexact and fit the model successfully.