The dataset which we are going to use is made up of 1797 8𝗑8 images. Each image is of a hand-written digit.

In order to utilize an 8𝗑8 figure, we have to first transform it into a feature vector with length 64.

# Import dataset

from om sklearn.datasets import load_digits

# Import the sklearn for SVM

from sklearn import svm

digits = load_digits( )

# Each datapoint is a 8𝗑8 image of digit

# Plot the image

plt.gray( )

plt.matshow(digits.image[20])

plt.show

print digits.images [20]

' ' '

[ [ 0. 0. 3. 13. 11. 7. 0. 0.]

[ 0. 0. 11. 16. 16. 16. 2. 0.]

[ 0. 4. 16. 9. 1. 14. 2. 0.]

[ 0. 4. 16. 0. 0. 16. 2. 0.]

[ 0. 0. 16. 1. 0. 12. 8. 0.]

[ 0. 0. 15. 9. 0. 13. 6. 0.]

[ 0. 0. 9. 14. 9. 14. 1. 0.]

[ 0. 0. 2. 12. 13. 4. 0. 0.]

' ' '

clf = svm.SVC( )

# Train the model

clf.fit (digits.data[:-1], digits.target[ :-1])

# Test the model

prediction = clf.predict(digits.data[20:21])

print "Predicted Digit ->" , prediction

' ' '

Predicted Digit -> [0]

' ' '

In order to utilize an 8𝗑8 figure, we have to first transform it into a feature vector with length 64.

**We will be using Super Vector Machine (SVM).****Plot 8𝗑8 image**# Import dataset

from om sklearn.datasets import load_digits

# Import the sklearn for SVM

from sklearn import svm

digits = load_digits( )

# Each datapoint is a 8𝗑8 image of digit

# Plot the image

plt.gray( )

plt.matshow(digits.image[20])

plt.show

**Print the 8𝗑8 array which represents each pixel**print digits.images [20]

' ' '

**Output :-**[ [ 0. 0. 3. 13. 11. 7. 0. 0.]

[ 0. 0. 11. 16. 16. 16. 2. 0.]

[ 0. 4. 16. 9. 1. 14. 2. 0.]

[ 0. 4. 16. 0. 0. 16. 2. 0.]

[ 0. 0. 16. 1. 0. 12. 8. 0.]

[ 0. 0. 15. 9. 0. 13. 6. 0.]

[ 0. 0. 9. 14. 9. 14. 1. 0.]

[ 0. 0. 2. 12. 13. 4. 0. 0.]

' ' '

**Train and Test the model**clf = svm.SVC( )

# Train the model

clf.fit (digits.data[:-1], digits.target[ :-1])

# Test the model

prediction = clf.predict(digits.data[20:21])

print "Predicted Digit ->" , prediction

' ' '

**Output :**Predicted Digit -> [0]

' ' '

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