{"id":20814566,"url":"https://github.com/spignelon/letter-recognition_project-ml","last_synced_at":"2025-10-26T04:03:00.466Z","repository":{"id":179004622,"uuid":"656350616","full_name":"spignelon/Letter-Recognition_Project-ML","owner":"spignelon","description":"A machine learning project for letter recognition using SVM, KNN, and Decision Tree, Random Forest, and Naive Bayes algorithms. 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The project implements machine learning algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Naive Bayes.\n\n## Table of Contents\n- [Project Description](#project-description)\n- [Dataset](#dataset)\n- [Code](#code)\n- [Report](#report)\n- [Blog Post](#blog-post)\n- [Google Colab](#google-colab)\n- [Installation](#installationrunning)\n- [License](#license)\n\n## Project Description\n\nThe Letter Recognition Project aims to develop a machine learning model that can accurately classify letters based on their visual features. The project utilizes various classification algorithms, including SVM, KNN, Decision Trees, Random Forest, and Naive Bayes, to achieve high accuracy in letter classification. It includes data preprocessing steps, model training, evaluation, and visualization of results.\n\n## Dataset\n\nThe dataset used in this project is the \"Letter Recognition Dataset\" from the UCI Machine Learning Repository. It consists of 20,000 samples, with each sample representing a letter of the alphabet. The dataset contains 16 numerical features that capture different attributes of each letter. You can download the dataset from [here](https://archive.ics.uci.edu/dataset/59/letter+recognition) or [alternative link](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/letter%2Brecognition.zip).\n\n## Code\n\nThe code is implemented in Python and is available in the [Letter_Recognition.ipynb](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/Letter_Recognition.ipynb) Jupyter Notebook. It includes data loading, preprocessing, model training, evaluation, and visualization. The code is well-documented and organized into sections for easy understanding and reproducibility.\n\nAdditionally, you can find the exported Python code from the Jupyter Notebook in the [letter_recognition.py](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/letter_recognition.py) file.\n\n## Report\n\nThe research report for the Letter Recognition Project is available in the [LetterRecognition_Research_Report.pdf](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/LetterRecognition_Research_Report.pdf) file. The report provides a detailed analysis of the project, including the problem statement, methodology, experimental results, and conclusions. It also discusses future directions for potential enhancements and improvements.\n\n## Blog Post\n\nFor a detailed explanation of the project, you can refer to the blog post titled \"Letter Recognition: Machine Learning for Classifying Capital Letters\" available [here](https://paper.wf/spignelon/letter-recognition-machine-learning-for-classifying-capital-letters).\n\n## Google Colab\n\nTo directly run and experiment with the project in an interactive environment, you can access the Google Colab notebook [here](https://colab.research.google.com/drive/1Q5wslzpK6YVoUwgkRtORaxXFScUUOJNw).\n\n## Installation/Running\n\nTo run the code locally, follow these steps:\n\n1. Clone this GitHub repository:\n   ```\n   git clone https://github.com/spignelon/Letter-Recognition_Project-ML.git\n   ```\n\n2. Install the required dependencies.\n\n3. Open and run the [Letter_Recognition.ipynb](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/Letter_Recognition.ipynb) Jupyter Notebook in your preferred environment.\n\n## License\n\nThis project is licensed under the terms of the [MIT License](https://github.com/spignelon/Letter-Recognition_Project-ML/blob/main/LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspignelon%2Fletter-recognition_project-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspignelon%2Fletter-recognition_project-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspignelon%2Fletter-recognition_project-ml/lists"}