Mini Machine Learning Project: From Scratch

Overview

This repository contains a mini machine learning project where three algorithms are implemented from scratch:

• One Rule (OneR)
• k-Nearest Neighbors (k-NN)
• Naive Bayes

The purpose of this project is to understand the foundational concepts of these algorithms by implementing them without using any external machine learning libraries.

Table of Contents

• Installation
• Project Structure
• Algorithms Implemented
• Contributing
• License
• Contact Information

Installation

To get started, clone the repository and install the dependencies:

git clone https://github.com/ali1sba/ML_mini_project.git
cd ML_mini_project
pip install -r requirements.txt

Project Structure

.
├── dataset
│   └── raw_data.csv              # TODO
│   └── sample_submission.csv     # Sample submission data
│   └── test.csv                  # features related to patient, hospital. Need to predict the Length of stay for each case_id
│   └── train.csv                 # Features related to patient, hospital and Length of stay on case basis
│   └── train_data_dict.csv       # Information of the features in train file
├── KNN
|   ├── dataset
|   │   └── raw_test01.csv            # TODO
|   │   └── raw_train01.csv           # TODO
|   │   └── sample_submission.csv     # Sample submission data
|   │   └── test.csv                  # features related to patient, hospital. Need to predict the Length of stay for each case_id
|   │   └── train.csv                 # Features related to patient, hospital and Length of stay on case basis
|   │   └── train_data_dict.csv       # Information of the features in train file
|   ├── KNN.ipynb                 # k-Nearest Neighbors algorithm implementation
├── Naive Bayes
│   └── ML MINI PROJET CAT_VERSION.ipynb                # Naive Bayes algorithm implementation
│   └── train.csv                 # TODO
├── ONE RULE.ipynb                # One Rule algorithm implementation
├── README.md                     # This README file
└── requirements.txt              # TODO

Algorithms Implemented

One Rule (OneR)

A simple classification algorithm that creates a single rule based on one feature. The rule is selected by evaluating which single feature provides the highest accuracy.

k-Nearest Neighbors (k-NN)

A non-parametric method used for classification and regression. It classifies data points based on the majority label of their k nearest neighbors in the feature space.

Naive Bayes

A probabilistic classifier based on Bayes' theorem with strong (naive) independence assumptions between the features. Often used for text classification and spam filtering.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch git checkout -b feature/new-feature
3. Commit your changes git commit -m 'Add new feature'
4. Push to the branch git push origin feature/new-feature
5. Open a Pull Request

License

This project is licensed under the MIT License.

Contact Information

For any questions or suggestions, please contact:

Email: HARIRI Ali

GitHub: ali1sba