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**Recognizing Handwritten Digits with scikit-learn**

**Data analytics** is the science of analysing raw data in order to make about that information. Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also focus on applying data patterns towards effective decision making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming and operational research to quantify performance .Data analysis is not limited to numbers and strings, because images and sounds can also be analysed and classified.

**Recognizing handwritten text** is a problem that traces 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 to sort mail automatically and efficiently. Included among the other applications that may come to mind is **OCR (Optical Character Recognition)** software.

Scikit-learn (Sklearn) is **the most useful and robust library for machine learning in Python**. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python.

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 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.

The scikit-learn library has a package of datasets. These datasets are useful for getting a handle on a machine-learning algorithm or library feature.

After loading the dataset, we can read the information about the dataset by calling the **DESCR** attribute.

The textual description of the dataset, the authors who contributed to its creation, and the references will appear as shown in the output

Each dataset in the scikit-learn library has a field containing all the information.

The numerical values represented by images, i.e., the targets, are contained in the **digit.targets** array.

**Dimensions **of the dataset can be obtained using **data.shape()** function.

The output shows that the dataset has 1797 images of 8x8 size.

The images of the handwritten digits are contained in an array. Each element of this array is an image that is represented by an 8x8 matrix of numerical values that correspond to grayscale from white, with a value of 0, to black, with the value 15.

we can visually check the contents of this result using the **matplotlib** library.

By running this command, we will obtain the grayscale image as follows

Using the NumPy and matplotlib libraries, we can display each digit from 0 to 9 which are in the form of an array as images.

The inputs are 8x8 grayscale images. we can produce a flat array of 64-pixel values so that each pixel corresponds to a column for the classifier.

It was reported that the dataset is a training set consisting of 1,797 images. We determined that it is true.

An estimator that is useful in this case is sklearn.svm.SVC, which uses the technique of *Support Vector Classification (SVC).*

** “Support Vector Machine” (SVM)** is a supervised machine learning algorithm that is mostly used in classification problems.

Import the SVM module of the scikit-learn library and create an estimator of SVC type and then choose an initial setting, assigning the values C and gamma generic values.

once we define a predictive model, we must instruct it with a training and test set. The **training set **is a set of data in which you already know the belonging class and the **test set** is a secondary data set that is used to test a machine learning program after it has been trained on initial training.

Here we have split the data by assigning 0.01 as test size.

we can train the svc estimator that we defined earlier using** **the** fit()** function.

After a short time, the trained estimator will appear with text output.

we can test our estimator by making it interpret the digits of the test set using **predict()** function.

We obtain the results in the form of an array.

We can plot the images of the predicted digits from the array using the following code.

It is able to recognize the handwritten digits and interprete all the digits of the validation set correctly.

The f1score of the model can be obtained using the **score()** function.

A **confusion matrix** is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known.

A **Classification report** is used to measure the quality of predictions from a **classification** algorithm.

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.

Tested the model with 3 different ranges

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 f1 score 95% of the times.

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