Autism-Detection-Model-for-Early-Childhood-Screening

Developed a SVM model to detect early signs of autism in children aged 0-3, achieving 93% accuracy and receiving prestigious recognition for its innovative approach.

##Overview This project focuses on detecting autism in early childhood using machine learning models, specifically Support Vector Machines (SVM). It includes two main notebooks:

• Autism_model.ipynb: Trains the SVM model on a verified dataset, provides code for model evaluation with confusion matrices, and visualizes results.

• Autism_input_model.ipynb: Allows users to input data for autism screening, applying machine learning techniques based on the trained model.

Technologies Used

• Python
• Jupyter Notebook
• Suport Vector Machine(SVM)
• Matplotlib
• Seaborn

Result of the Model

Confusion Matrix

Boys-Girls Ratio

Age Distribution Curve

Feature Importance

Getting Started

To run the project:

1. Make sure you have Jupyter Notebook installed or use an IDE like VS Code with Jupyter extension.
2. Clone or download the project repository.
3. Install the required Python modules.
4. Open and run the following notebooks:
   • Autism_model.ipynb
   • Autism_input_model.ipynb
5. Follow the instructions within each notebook to execute the code and interact with the models.

License

This project is licensed under the Apache License 2.0.

Feel free to contribute and improve this project!