# 🌟 Machine Learning Projects Repository
Welcome to the **ML** repository! This is your go-to space for exploring various machine learning projects. Here, you'll find implementations and examples using popular algorithms and libraries.
## 🚀 Overview
Machine learning is a fascinating field that focuses on teaching computers to learn from data. This repository contains projects that demonstrate key machine learning concepts using Python. The projects range from basic algorithms to more advanced techniques, helping you to understand the core principles of machine learning.
## 🛠️ Technologies Used
- **Python**: The primary programming language for all implementations.
- **NumPy**: A library for numerical operations in Python.
- **Pandas**: Used for data manipulation and analysis.
- **Matplotlib**: A plotting library for visualizing data.
- **Jupyter Notebook**: An interactive environment to run and document code.
- **Scikit-learn**: A library that provides simple and efficient tools for data mining and data analysis.
## 🗂️ Project Topics
- **Bayes**: Implementations of Naive Bayes classifiers.
- **Decision Trees**: Understanding how decision trees work and their applications.
- **Linear Regression**: Exploring regression techniques to predict continuous values.
- **Support Vector Machines (SVM)**: An introduction to SVM and its use in classification tasks.
## 🔍 Getting Started
To get started with the projects in this repository, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/jonathanmutu2005/ML.git-
Install Required Packages:
Navigate to the project directory and install the required packages using pip:
pip install -r requirements.txt
-
Run the Jupyter Notebook:
Launch Jupyter Notebook in your browser:
jupyter notebook
Open the desired notebook and explore the code.
To download the latest versions of the projects, visit the Releases section. Each release contains packaged files that you can download and execute.
The repository is organized as follows:
ML/
├── bayes/
│ ├── bayes_classifier.py
│ └── bayes_notebook.ipynb
├── decision_tree/
│ ├── decision_tree_classifier.py
│ └── decision_tree_notebook.ipynb
├── linear_regression/
│ ├── linear_regression_model.py
│ └── linear_regression_notebook.ipynb
├── svm/
│ ├── svm_classifier.py
│ └── svm_notebook.ipynb
└── requirements.txt
The Bayes folder contains a notebook that demonstrates how to build a Naive Bayes classifier. You'll learn how to preprocess data, fit the model, and make predictions.
In the Decision Tree section, you can explore how decision trees work. The notebook guides you through building a decision tree model, visualizing it, and understanding its strengths and weaknesses.
This section covers linear regression in depth. You will work through the mathematical foundations and see how to implement a regression model using Python.
The SVM folder provides insights into support vector machines. You will learn how to use SVM for classification tasks and understand the concept of hyperplanes.
Visualization plays a critical role in machine learning. Each project includes relevant graphs and charts generated using Matplotlib. These visuals help you interpret the model's performance and the underlying data.
Here are some example outputs from the notebooks:
Contributions are welcome! If you'd like to improve this repository or add new projects, please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature/YourFeature. - Make your changes and commit them:
git commit -m 'Add some feature'. - Push to the branch:
git push origin feature/YourFeature. - Open a pull request.
- Jonathan Mutu - Initial work and project creator.
For inquiries or feedback, please reach out to me via email at [email protected].
This project is licensed under the MIT License. See the LICENSE file for details.
Thank you for visiting this repository! Dive into the code and start learning!