Handwritten Digit Recognition with Scikit-Learn | Machine Learning Tutorial

Machine learning has revolutionized the field of pattern recognition, making it possible to automatically recognize and classify handwritten digits. In this tutorial, we will use the Scikit-Learn library in Python to build a simple handwritten digit recognition model.

Step 1: Importing the necessary libraries

In this step, we will import the necessary libraries including numpy, matplotlib, and scikit-learn.

        
            import numpy as np
            import matplotlib.pyplot as plt
            from sklearn import datasets
            from sklearn.model_selection import train_test_split
            from sklearn.neural_network import MLPClassifier
            from sklearn.metrics import accuracy_score
        
    

Step 2: Loading and exploring the dataset

We will load the famous MNIST dataset, which consists of 70,000 images of handwritten digits. We will then explore the dataset by visualizing some of the images.

        
            digits = datasets.load_digits()
            X = digits.data
            y = digits.target

            fig, ax = plt.subplots(2, 4)
            for i, axi in enumerate(ax.flat):
                axi.imshow(digits.images[i], cmap='gray')
                axi.set(xticks=[], yticks=[])
    
    

Step 3: Splitting the dataset into training and testing sets

In this step, we will split the dataset into a training set and a testing set, which will allow us to evaluate the performance of our model.

        
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
    

Step 4: Building and training the model

We will use a simple multi-layer perceptron (MLP) classifier to build our handwritten digit recognition model. We will then train the model using the training set.

        
            model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000)
            model.fit(X_train, y_train)
        
    

Step 5: Evaluating the model

Finally, we will evaluate the performance of our model using the testing set and calculate the accuracy of the model.

        
            y_pred = model.predict(X_test)
            accuracy = accuracy_score(y_test, y_pred)
            print("Accuracy: ", accuracy)
        
    

By following these steps, you should be able to build a simple handwritten digit recognition model using Scikit-Learn. This tutorial serves as a starting point for understanding how machine learning can be used to recognize and classify handwritten digits, and can be expanded upon to create more complex and accurate models.