Recognizing Handwritten Digits with Scikit-Learn

https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. @www.suvenconsultants.com Handwritten digits recognition is a challenging problem in recent years. Classifying handwritten text or numbers is important for many real-world scenarios. The applications of digit recognition includes in postal mail sorting, bank check processing, form data entry, etc. The raw data comprises images that are scaled segments from five-digit ZIP codes. In this blog we are going to recognize Handwritten single digits(0–9) correctly using the digits data set from Scikit-Learn, which is a Python Library that contains numerous useful algorithms that can easily be implemented and altered for the purpose of classification and other machine learning tasks, and by using a classifier called Logistic Regression. We are going to train a Support Vector Machine and then we will be predicting the values of a few unknown Handwritten digits. Digits data set consists of 1,797 images that are 8x8 pixels in size. Each image is a handwritten digit in grayscale.

One of the fascinating things about the Scikit-Learn library is that is has a 4-step modeling pattern. >Import the model you want to use. > Make an instance of the Model. > Training the model on the data and storing the information learned from the data. > Predicting the labels of new data, using the information the model learned during the training process. 1.Let us start by importing necessary libraries for our model and loading the dataset digits. To import the svm module of the scikit-learn library. We can create an estimator of SVC type and then choose an initial setting, assigning the values C and gamma generic values. These values can then be adjusted in a different way during the course of the analysis.

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

3. Our data-set is stored in digits. By the command given below, you will obtain a grayscale image of digit.

4. The numerical values represented by images, i.e., the targets, are contained in the digit.targets array. And also the dataset is a training set consisting of 1,797 images. We can determine if that is true.

5. Visualizing the images and labels in our Dataset. This dataset contains 1,797 elements, and so let us consider the first 1,791 as a training set and will use the last six as a validation set. We can see in detail these six handwritten digits by using the matplotlib library.

6. Now we are training the svc estimator that we have defined earlier.

Now we have to test our estimator, making it interpret the six digits of the validation set.

As we can see that the svc estimator has learned correctly. It is able to recognize the handwritten digits, interpreting correctly all six digits of the validation set 7. Now let us see the Scikit-Learn 4-Step Modeling Pattern. First let’s split our Dataset into training and test sets to make sure that after we train our model, it is able to generalize well to new data. Step 1: Importing the model we want to use. Step 2: Making an instance of the Model. Step 3: Training the Model. Step 4: Predicting the labels of new data and measuring performance of our model. 8. Confusion matrix: A confusion matrix is a table that is often used to evaluate the accuracy of a classification model. We can use Seaborn or Matplotlib to plot the confusion matrix. We will be using Seaborn for our confusion matrix.

From this article, we can see how easily we can import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and can find the accuracy of our prediction(which in our case is 95.11%). As we can clearly see above, 95% of our models the achieved accuracy is 100% . Hence we can easily conclude that our model works for more than 95% of the time.