Recognizing Handwritten Digits By Scikit-Learn

 

Recognizing Handwritten Digits with Scikit-Learn


Scikit-learn library:

The scikit-learn library (http://scikit-learn.org/) enables us to approach this type of data analysis in a way that differs slightly from what we’ve used in the previous project. I closely related the data to be analyzed to numerical values or strings, but can also involve images and sounds.

Aim:

The primary aim of this project involves predicting a numeric value, and then reading and interpreting an image that uses a handwritten font.

We will have an estimator with the task of learning through a fit() function, and once it has reached a degree of predictive capability (a model sufficiently valid), it will produce a prediction with the predict() function. Then we will discuss the training set and validation set created this time from a series of images.

The Digits data set of the scikit-learn library provides numerous datasets that are useful for testing many problems of data analysis and prediction of the results. Some Scientist claims that it predicts the digit accurately 95% of the times. Perform data Analysis to accept or reject this Hypothesis.

The Digits Dataset

The Scikit-learn library provides many datasets that are useful for testing many problems of data analysis and prediction of the results. Also in this case there is a dataset of images called digits. This dataset comprises 1,797 images that are 8x8 pixels in size. Each image is a handwritten digit in grayscale.












Given the large number of elements contained in the Digits dataset, we will certainly obtain a very effective model, i.e., one that’s capable of recognizing with good certainty.

We test the hypothesis by using these cases, each case for a different range of training and validation sets.

After performing the data analysis on the dataset with three different test cases, we can conclude that the given hypothesis is true i.e., the model predicts the digit accurately 95% of the time.

Source code: GitHub

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

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