Recognizing Handwritten Digits with scikit-learn
Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently.

The development of CNN has taken recognizing things to another level. But in this article, we are going to use a simple RandomForest Classifier to recognize handwritten digits.

Let's get started importing the necessary libraries and datasets.

Let's see how our digits look like using matplotlib.imshow()

Now it's time to train our model and evaluate it. As previously said we are going to use a simple Random Forest Classifier model from scikit-learn library. We will use data from 1 to 1000 to train and from 1000 to 1796 for evaluation.

After training and obtaining predicted results, let's compare results with the original values.

Our model did pretty well. It predicted 94% of the digits rightly which is quite impressive.

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.
['Aadesh Baral']
['Random Forest', 'Machine Learning', 'Scikit Learn', 'Digits Recognition']