#importing the dataset
from sklearn.datasets import load_digits
digits = load_digits() # Print to show there are 1797 images (8 by 8 images for a dimensionality of 64)
print(“Image Data Shape” , digits.data.shape)
import numpy as np
import matplotlib.pyplot as plt plt.figure(figsize=(20,4))
for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):
plt.subplot(1, 5, index + 1)
plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)
plt.title(‘Training: %i\n’ % label, fontsize = 20)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=0)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=0)
from sklearn.linear_model import LogisticRegression
