This project uses machine learning techniques to predict the likelihood of autism based on user input data. It aims to support early detection and intervention by analyzing behavioral and personal characteristics. 📌 Features
Preprocessing of input data Machine learning models (e.g., Logistic Regression, SVM, etc.) Model evaluation and comparison Visualization of results 🧠 Technologies Used
Python 🐍 Jupyter Notebook 📓 Pandas & NumPy Scikit-learn Matplotlib & Seaborn 🚀 How to Run
Clone this repository: git clone https://github.com/yourusername/your-repo-name.git Install the required packages: pip install -r requirements.txt Run the Jupyter Notebook: jupyter notebook Autism_Preidiction_using_machine_Learning.ipynb 📊 Dataset
The dataset used includes behavioral and demographic features relevant to autism screening. Make sure the dataset is available in the working directory or is loaded within the notebook. 📁 File Structure
├── Autism_Preidiction_using_machine_Learning.ipynb ├── README.md └── requirements.txt (create this file with all necessary libraries) 🧩 Future Work
Integration with a web interface Real-time predictions from user input Expansion to other age groups 🙌 Contributing
Contributions are welcome! Please open issues or submit pull requests. 📄 License
This project is licensed under the MIT License.