Building a K- Nearest neighbours classifier for handwritten digit recognition

In this problem you will complete some code to build a k-nearest neighbour classifier to classify images of handwritten digits (0-9). For this purpose we will use a famous open-source dataset of handwritten digits called the MNIST that is ubiquitously used for testing a number of classification algorithms in machine learning:

class MNIST_import: """ sets up MNIST dataset from OpenML """ def __init__(self): df = pd.read_csv("data/mnist_784.csv") # Create arrays for the features and the response variable # store for use later y = df['class'].values X = df.drop('class', axis=1).values # Convert the labels to numeric labels y = np.array(pd.to_numeric(y)) # create training and validation sets self.train_x, self.train_y = X[:5000,:], y[:5000] self.val_x, self.val_y = X[5000:6000,:], y[5000:6000] data = MNIST_import()