Keywords
Handwritten digit recognition, Logistic Regression, k-Nearest Neighbors, Convolutional Neural Networks, MNIST dataset, machine learning
Abstract
This project explores and compares the performance of various machine learning classifiers for handwritten digit recognition using the MNIST dataset. The classifiers include Logistic Regression, k-Nearest Neighbors, and Convolutional Neural Networks. Each classifier is evaluated based on accuracy, precision, recall, F1-score, and confusion matrix analysis.
Semester
Fall 2025
Course Name
STA 6366 Data Science 1
Instructor Name
Dr. Rui Xie
College
College of Sciences
STARS Citation
Ahiduzzaman, Md, "Handwritten Digit Recognition using Machine Learning Classifiers" (2025). Data Science and Data Mining. 48.
https://stars.library.ucf.edu/data-science-mining/48
Accessibility Status
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Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Survival Analysis Commons
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