Autism-Detection

Autism Detection using Machine Learning

This project focuses on the early detection of Autism Spectrum Disorder (ASD) in individuals through feedback analysis using various machine learning classification algorithms.

Table of Contents

• Introduction
• Features
• Technologies Used
• Getting Started
• Project Structure
• Usage
• Contributing
• License

Introduction

This project aims to develop a classification model that can aid in the early detection of ASD by analyzing feedback. We implemented different machine learning algorithms, such as K-Nearest Neighbors (KNN) and Naive Bayes, to accurately identify signs of ASD. Additionally, we created a research paper that details the construction of the project, our findings, and the potential impact on early ASD detection.

Features

• Classification Model: A machine learning model for detecting ASD based on feedback data.
• Algorithm Implementation: Implemented KNN and Naive Bayes classification algorithms.
• Research Paper: A comprehensive document explaining the project, discoveries, and implications for early ASD detection.

Technologies Used

• Python
• Scikit-learn
• Pandas
• NumPy
• Jupyter Notebook

Getting Started

To get a local copy up and running, follow these simple steps:

Prerequisites

Ensure you have Python and Jupyter Notebook installed. You will also need to install the necessary Python libraries.

Installation

1. Clone the repo:

    git clone https://github.com/yourusername/autism-detection-ml.git
   

2. Install the required libraries:

    pip install -r requirements.txt
   

3. Open the project in Jupyter Notebook:

    jupyter notebook
   

Project Structure

• data/: Contains the dataset used for training and testing the model.
• notebooks/: Jupyter notebooks with code and explanations for the project.
• models/: Saved models for later use.
• research-paper/: The research paper detailing the project.
• requirements.txt: List of Python libraries required.
• README.md: Project documentation.

Usage

1. Open the Jupyter Notebook in the notebooks/ directory to explore the data and see the implementation of the classification algorithms.
2. Run the cells to train and test the models.
3. Refer to the research-paper/ directory to read the detailed explanation of the project and its findings.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

License

Distributed under the MIT License. See LICENSE for more information.

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Thank you for checking out our project on Autism Detection using Machine Learning. If you have any questions or suggestions, please feel free to contact us.