in python

Using the sklearn library:

Write classification code by utilizing several scikit-learn classifiers:

(i) perceptron,

(ii) logistic regression,

(iii) linear support vector machine (SVM),

(iv) non-linear SVM using Radial Basis Function (RBF) kernel,

(v) decision tree, and

(vi) KNN.

Testing should use load_digits dataset from sklearn. Please partition the data to be training and testing (80:20). Each classifier should report:

-accuracy of the models performance on both the training and testing data. (clearly stat your parameter values)

-performance when tuning one (one is sufficient)hyper-parameter. For example, you can tune \eta for the perceptron model; for logistic regression, you can tune the C value.

-report both the training and testing time.