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matakshay · Aug 20, 2019 · 02d30e1 · 02d30e1
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 # digit_recognition
 Recognise and Classify handwritten digits from the MNIST Database using kNN Algorithm
 
-The MNIST Database contains 70,000 handwritten images of different digits. 60,000 of these images are input to the algorithm, along with their correct corresponding digit values. 
-Subsequent to this, any image from the remaining 10,000 can be input as a test case. Using a conventional k Nearest Neighbours Algorithm, the model tries to identify the digit in the image. 
-This prediction is compared with the acutal correct value. Also the value of k (i.e. number of nearest neighbours to be considered), can be set to an appropriate value so as to have good accuracy while also having a small execution time.
+The MNIST Database contains around 70,000 images of handwritten digits. 60,000 of these images are fed to the algorithm, along with their correct corresponding digit values. 
+
+Subsequent to this, any image from the remaining 10,000 can be input as a test case. Using the conventional k-Nearest Neighbours Algorithm, the model identifies the digit in the image. 
+
+This prediction is compared with the acutal correct value. Also the value of k (i.e. number of nearest neighbours to be considered), can be set to an appropriate value so as to have optimal accuracy, while simultaneously having a small execution time.