🧠 ASD-NeuroML

A Machine Learning Approach to Early-Stage Autism Spectrum Disorder Detection

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📌 Project Overview

ASD-NeuroML is a specialized data science project focused on enhancing the accuracy of Autism Spectrum Disorder (ASD) screenings using supervised learning. By analyzing behavioral traits and clinical markers—such as A1-A10 scores, age, and family history—this model provides a high-precision tool for identifying potential ASD indicators in the developmental cycle.

🚀 Key Features

• Feature-Driven Classification: Utilizes behavioral screening scores (AQ-10) and clinical history to predict ASD traits.
• Imbalance Handling: Implements techniques to address the "No ASD" vs. "ASD" data skew common in medical datasets.
• High Interpretability: Focuses on impactful features like Age and Jaundice to provide explainable results.
• Optimized Performance: Leverages fine-tuned supervised algorithms for maximum precision.

🛠️ Technical Stack

• Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Model Type: Supervised Learning (Classification)

📂 Repository Structure

ASD-NeuroML/
├── Datasets/           # Contains raw and processed ASD data files
├── Notebooks/          # Autism_Prediction.ipynb (Analysis & Modeling)
├── Docs/               # Project documentation and reports
└── README.md           # Main project documentation