https://github.com/kmohamedalie/mnist

Recognizing handwritten digits with classical machine learning with a 97% accuracy and f1-score
https://github.com/kmohamedalie/mnist

classification computer-science-and-engineering digit-recognition machine-learning-algorithms mnist snapml yann-le-cunn

Last synced: 6 days ago
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Recognizing handwritten digits with classical machine learning with a 97% accuracy and f1-score

• Host: GitHub
• URL: https://github.com/kmohamedalie/mnist
• Owner: Kmohamedalie
• License: mit
• Created: 2023-08-14T16:27:53.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-10-19T21:07:32.000Z (about 1 year ago)
• Last Synced: 2023-10-20T04:58:16.821Z (about 1 year ago)
• Topics: classification, computer-science-and-engineering, digit-recognition, machine-learning-algorithms, mnist, snapml, yann-le-cunn
• Language: Jupyter Notebook
• Homepage: https://github.com/Kmohamedalie/MNIST/tree/master
• Size: 26.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Recognizing handwritten digits using Mnist Dataset 

https://github.com/Kmohamedalie/MNIST/assets/63104472/e223078c-4f7f-4d6d-ba54-29246968f1bf

**Source:** [Yann LeCun - Convolutional Network Demo from 1989](https://www.youtube.com/watch?v=FwFduRA_L6Q)

**Dataset:** [Kaggle](https://www.kaggle.com/datasets/hojjatk/mnist-dataset)

**Complete JupyterNotebook:** [Link](https://github.com/Kmohamedalie/MNIST/blob/master/Notebook/Mnist%20-%20SnapML(Bagging).ipynb)

**Metrics:**

| Algorithm | Precision | Recall | F1-score | Accuracy |

|-----------|-----------|--------|----------|----------|

| Random Forest(SnapML)   | 97.06%    |  97.06% | 97.06%  | 97.06%   |

## MNIST

Introduced by LeCun et al. in Gradient-based learning applied to document recognition

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.




Source:  http://yann.lecun.com/exdb/mnist/