This project tackles the classic problem of handwritten digit recognition. It uses a machine learning model to learn the patterns and features associated with each digit (0-9) from a training dataset . Once trained, the model can then be used to predict the digit represented in new handwritten input, which could be provided as an image or through other means. This is a fundamental problem in computer vision and machine learning, with applications in postal code recognition, form processing, and more.
-
Data Loading and Preprocessing: Loads and preprocesses the training data (e.g., MNIST dataset). This often involves resizing images, normalizing pixel values, and one-hot encoding labels. 🔢
-
Model Training: Trains a machine learning model (CNN, SVM, MLP, etc.) on the training data. ⚙️
-
Model Evaluation: Evaluates the trained model's performance on a test dataset to assess its accuracy. 📊
-
Prediction: Provides functionality to make predictions on new handwritten digit input. This could involve loading an image, preprocessing it, and then feeding it to the trained model. 🔮
-
Python: The core programming language for machine learning and image processing. 🐍
-
NumPy: For numerical operations and array manipulation. 🔢
-
Scikit-learn: For machine learning algorithms and model evaluation. ⚙️
-
Machine Learning Beginners: A great introductory project to learn about image classification and neural networks. 🧑🎓
-
Computer Vision Enthusiasts: Exploring basic computer vision techniques. 👀
-
Python Developers: Interested in practical applications of machine learning. 👨💻👩💻