Project Title: Handwritten Digit Recognition
Project Objective: The objective of this project is to develop a machine learning model capable of accurately recognizing handwritten digits. Handwritten digit recognition has various real-world applications, such as automated data entry, postal code recognition, and digit-based CAPTCHA solving. This project aims to leverage a Random Forest classifier, a powerful machine learning algorithm, to achieve this goal.
Project Description:
In this project, we are building a system to recognize handwritten digits, which is essential for a wide range of applications in the field of image processing and character recognition.
Data: We have a dataset containing handwritten digits, which are represented as arrays of pixel values. Each digit is a 8x8 grid of grayscale pixels. For example, a digit "5" might be represented as follows:
[ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13., 15., 10., 15., 5., 0., 0., 3., 15., 2., 0., 11., 8., 0., 0., 4., 12., 0., 0., 8., 8., 0., 0., 5., 8., 0., 0., 9., 8., 0., 0., 4., 11., 0., 1., 12., 7., 0., 0., 2., 14., 5., 10., 12., 0., 0., 0., 0., 6., 13., 10., 0., 0., 0.] Visualization: Throughout the project, we can use various data visualizations to understand the model's behavior better. These visualizations may include confusion matrices, accuracy and loss curves, and sample digit recognition results.
The ultimate goal of this project is to create an accurate and reliable handwritten digit recognition system that can be used in various applications where automatic digit recognition is required.