Recognizing Handwritten Digits with Scikit-Learn: A Project in Machine Learning

Handwritten digit recognition is a popular problem in the field of machine learning. In this tutorial, we will use the Scikit-Learn library in Python to build a handwritten digit recognition system. This project can be used as a simple example to understand how machine learning algorithms work and how they can be applied to real-life problems.

To get started with this project, you will need to have a basic understanding of Python programming language, machine learning concepts, and some familiarity with Scikit-Learn library. If you are new to machine learning, it’s recommended to first go through some introductory tutorials on basic concepts such as supervised learning, classification algorithms, and the Scikit-Learn library.

Step 1: Importing Required Libraries

The first step is to import the required libraries for our project. We need Numpy for numerical operations, Matplotlib for plotting graphs, and Scikit-Learn for machine learning algorithms.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

Step 2: Loading and Preprocessing the Dataset

Scikit-Learn provides a built-in dataset called ‘digits’ which contains images of handwritten digits along with their corresponding labels. We will load this dataset and preprocess it before training our model.

digits = datasets.load_digits()

X = digits.data
y = digits.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Training the Machine Learning Model

For this project, we will use a simple logistic regression model to train our algorithm. Logistic regression is a popular classification algorithm that is widely used for problems like this.

model = LogisticRegression()

model.fit(X_train, y_train)

Step 4: Evaluating the Model Performance

Once the model is trained, we need to evaluate its performance on the test dataset. We will calculate the accuracy score of our model to see how well it can recognize handwritten digits.

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Step 5: Making Predictions

Now that our model is trained and evaluated, we can use it to make predictions on new inputs. We can pass a new handwritten digit image to our model and get the predicted label as output.

# Randomly select an image from the test set
random_idx = np.random.randint(0, len(X_test))
input_img = X_test[random_idx].reshape(8, 8)

# Plot the input image
plt.imshow(input_img, cmap='gray')
plt.show()

# Make a prediction on the input image
prediction = model.predict([X_test[random_idx]])
print("Predicted digit:", prediction[0])

Conclusion

In this tutorial, we have built a simple handwritten digit recognition system using Scikit-Learn library in Python. We have successfully trained a logistic regression model to recognize handwritten digits with a good level of accuracy. This project is a great starting point for beginners to get hands-on experience with machine learning algorithms and understand how they can be applied to real-world problems.