MNISTClassification

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

Loading and checking the data

Fetch dataset from openml by name or dataset id. Datasets are uniquely identified by either an integer ID or by a combination of name and version

from sklearn.datasets import fetch_openml
mnist_jay = fetch_openml('mnist_784', version=1, as_frame=True)
mnist_jay.keys()

#Assign the data and target to a ndarray
X_jay, y_jay = mnist_jay['data'], mnist_jay['target']
X_jay.shape, y_jay.shape

some_digit1 = X_jay.to_numpy()[7]
some_digit2 = X_jay.to_numpy()[5]
some_digit3 = X_jay.to_numpy()[0]

# Use imshow method to plot the values of the three variables you defined in the above point.   
plt.imshow(some_digit1.reshape(28, 28), cmap=mpl.cm.binary, interpolation='nearest')
display(plt.show())
plt.imshow(some_digit2.reshape(28, 28), cmap=mpl.cm.binary, interpolation='nearest')
display(plt.show())
plt.imshow(some_digit3.reshape(28, 28), cmap=mpl.cm.binary, interpolation='nearest')
display(plt.show())

Pre-processing the data

#	The current target values range from 0 to 9 i.e. 10 classes. 
#Transform the target variable to 3 classes as follows:
# a.	Any digit between 0 and 3 inclusive should be assigned a target value of 0
# b.	Any digit between 4 and 7 inclusive should be assigned a target value of 1
# c.	Any digit between 8 and 11 inclusive should be assigned a target value of 9
# d.	Use the following code to do this:
y_jay_new = np.where(y_jay < 4, 0, y_jay)
y_jay_new = np.where((y_jay_new > 3) & (y_jay_new < 8) , 1, y_jay_new)
y_jay_new = np.where((y_jay_new > 7) & (y_jay_new < 12), 9, y_jay_new)
print(y_jay_new)

# Print the frequencies of each of the three target classes in y_jay_new
unique, counts = np.unique(y_jay_new, return_counts=True)
print(np.asarray((unique, counts)).T)

# Split your data into train test. Assign the first 60,000 records for training and the last 10,000 records for testing. 
X_train, X_test = X_jay[:60000], X_jay[60000:]
y_train, y_test = y_jay_new[:60000], y_jay_new[60000:]

Build the classification model

# Train a KNN classifier using the training data. Name the classifier KNN_clf_firstname.
from sklearn.neighbors import KNeighborsClassifier
KNN_clf_jay = KNeighborsClassifier()
KNN_clf_jay.fit(X_train, y_train)

# Predict the class labels for the test data using the trained classifier. Assign the result to y_pred_firstname.
y_pred_jay = KNN_clf_jay.predict(X_test)
print(y_pred_jay)

# Predict the class labels for the test data using the trained classifier. Assign the result to y_pred_firstname2.
y_pred_jay2 = KNN_clf_jay.predict(X_test)
# Use the classifier to predict the three variables you defined in point 7 above.
print(KNN_clf_jay.predict([some_digit1]))
print(KNN_clf_jay.predict([some_digit2]))
print(KNN_clf_jay.predict([some_digit3]))

print(KNN_clf_jay.score(X_test, y_test) * 100)