Autism spectrum disorders (ASD) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, life-long nature, complexity and heterogeneity. Autism Brain Imaging Data Exchange (ABIDE)(DiMartino, 2014), a grassroots consortium aggregating and openly sharing existing resting-state functional magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI and phenotypic information from 487 individuals with ASD and 557 age-matched typical controls. ABIDE II has 1004 samples and each sample has 1446 features. Here in this project, I use different machine learning models to correctly classify ASD from controls.