MNIST_digit_recognition

A small project comparing various machine learning techniques for recognizing handwritten digits, using the MNIST dataset.

Installation

Install this project using poetry

petry install

Training data

This project is based on this exercise from Kaggle.com

To run this project, you need to download the data (train.csv) from this exercise and put it under ./data directory

Commands

This package offers several executable scripts:

• fit - train all models and save them in the ./models directory
• score - calculate accuracy of the models
• confusion_matrix - calculate confusion matrices for the models
• app - run the drawing app allowing to test the models

The main script executes these steps in sequence

To run the script using poetry run:

poetry run [script]

App

TODO

Available models

Currently supported models are:

• Naive Bayes (from sklearn)
• Decision Tree (from sklearn)
• Random Forest (from sklearn)
• K Nearest Neighbours (k=5, from sklearn)
• Support Vector (from sklearn)