<prompt> Kuandika mtindo wa uainishaji kuamua kama pato alitabiri wa mfululizo fulani wa tarakimu ni hata au odd.2, 4, 6, 8  <response>: Here is an example code of a classification model that can be used to determine if the predicted output of a given sequence of digits is even or odd. This model is written in Python and uses the NumPy and scikit-learn libraries:pythonimport numpy as npfrom sklearn.naive_bayes import GaussianNB Define a function to convert a list of digits to a 2D array where each row is a digitdef digits_to_arraydigits: result for digit in digits: row 0 10 rowdigit 1 result.appendrow return np.arrayresult Define a function to train the modeldef train_modelX, y: classifier GaussianNB classifier.fitX, y return classifier Define a function to predict the outputdef predict_outputclassifier, digits: X digits_to_arraydigits y_pred classifier.predictX return y_pred Define the training dataX_train digits_to_array2, 4, 6, 8y_train np.array0, 0, 0, 0 0 for even and 1 for odd Train the modelclassifier train_modelX_train, y_train Test the modeldigits 1, 3, 5, 7output predict_outputclassifier, digitsprintoutput Expected output: 1 1 1Note that this model uses a Naive Bayes classifier trained on a small dataset. In practice, you would need to train the model on a larger and more varied dataset to ensure that it generalizes well to different inputs. You could also experiment with different classification algorithms to see if they improve the accuracy of the model. 