This notebook demonstrates a machine learning workflow for predicting reading outcomes from demographic and neuroimaging data. It loads a tab-separated dataset containing variables such as age, gender, cognitive scores, and a diffusion MRI measure (L_SLFp_FA), then builds and evaluates classification models using scikit-learn’s ExtraTreesClassifier and LogisticRegressionCV. The code employs a Pipeline with StandardScaler for preprocessing, and uses StratifiedKFold for cross-validation to assess model performance. Visualizations include a dataframe preview rendered as an HTML table via pandas, with the analysis executed in an inline matplotlib environment using seaborn for styling.