https://github.com/captaincodercool/handwritten-digit-recognition-using-k-nearest-neighbors-knn-
This project implements a K-Nearest Neighbor classifier in Python to recognize handwritten digits from the UCI PenDigits dataset. It reads training and test files, computes Euclidean distances, handles tie-based accuracy scoring, and outputs predictions with average accuracy to result.txt.
https://github.com/captaincodercool/handwritten-digit-recognition-using-k-nearest-neighbors-knn-
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic links in README
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (1.5%) to scientific vocabulary
Repository
This project implements a K-Nearest Neighbor classifier in Python to recognize handwritten digits from the UCI PenDigits dataset. It reads training and test files, computes Euclidean distances, handles tie-based accuracy scoring, and outputs predictions with average accuracy to result.txt.
Basic Info
- Host: GitHub
- Owner: CAPTAINCODERCOOL
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 0 Bytes
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
Owner
- Login: CAPTAINCODERCOOL
- Kind: user
- Repositories: 1
- Profile: https://github.com/CAPTAINCODERCOOL
GitHub Events
Total
- Push event: 1
- Create event: 2
Last Year
- Push event: 1
- Create event: 2