Machine-Learning-Tutorial

Welcome to the repository for my individual assignment on machine learning! This project includes a tutorial that explains a specific machine learning technique in detail, with accompanying code, visualizations, and references.

Overview

This tutorial is focused on [ Support Vector Machines (SVM)]. It explains the technique's key concepts, demonstrates its implementation using Python, and provides guidance on how to apply it effectively in machine learning projects.

The tutorial is designed to help both beginners and advanced learners understand and utilize this technique. It includes:

• A detailed explanation of the technique.
• Python code implementation.
• Visualizations to explain the results.

Repository Structure

├── LICENSE                     # License file
├── README.md                   # This readme file
├── Parth_ML ASS.pdf                # The tutorial document
├── code/                       # Directory containing all code files
│   └── Diabetes_Prediction.ipynb # Jupyter Notebook with code and explanations
├── data/                       #  datasets used in the project
│   └── diabetes.csv             # dataset

Getting Started

To explore and run the code in this repository, follow these steps:

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Jupyter Notebook
• Required Python libraries (listed below)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/repository-name.git
   cd repository-name

2. Install the required libraries:

   pip install -r requirements.txt

Running the Code

1. Open the Jupyter Notebook:

   jupyter notebook code/tutorial_notebook.ipynb

2. Follow the steps in the notebook to explore the implementation and results.

Features

• Detailed Tutorial: Explains the machine learning technique step-by-step.
• Code Implementation: Includes Python code to demonstrate the technique on a sample dataset.
• Visualizations: Generates plots to illustrate the behavior and performance of the technique.

Accessibility

• All plots are color-blind friendly.
• The tutorial document includes alt-text for all images.
• Subtitles are available for the video tutorial (if applicable).

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Special thanks to [Professor Peter Scicluna] for guidance and support during the course.

Feel free to explore, use, and contribute to this project. If you have any questions, please contact me at [bhalaniparth00@gmail.com] or open an issue on GitHub!