Autism Prediction

Autism Prediction Using Machine Learning

This project focuses on building a classification model to predict Autism Spectrum Disorder (ASD) using machine learning techniques. The model analyzes behavioral and demographic features from a structured dataset to determine whether an individual is likely to have autism.

The workflow includes data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation using algorithms such as Logistic Regression, SVM, and Gradient Boosting. Performance is assessed using metrics like accuracy, precision, recall, and ROC-AUC to select the best model.

The final result is a predictive system that can assist in early autism screening by identifying patterns in behavioral data.

Tools: Python, Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, Seaborn.