Autism-Prediction-Project

Autism Prediction Project is an end-to-end Machine Learning solution designed to predict the likelihood of Autism Spectrum Disorder (ASD) in adults based on behavioral and demographic data

The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, and handling class imbalance using SMOTE. Multiple classification models were evaluated, including Random Forest, Gradient Boosting, and XGBoost, with hyperparameter tuning performed using RandomizedSearchCV

The final XGBoost model was deployed through a Streamlit web application that enables users to enter assessment data and receive real-time predictions with probability scores

Technologies used include Python, Pandas, NumPy, Scikit-learn, XGBoost, Streamlit, Matplotlib, Seaborn, and FuzzyWuzzy