An autonomous LLM assistant that reasons about calendar events, biometrics, and device state to execute multi-step routines without being explicitly asked. Orchestrates 10+ live integrations (Spotify, Google Calendar, Garmin, smart home) via a tool-calling agent loop. Trains local stress and sleep classifiers on wearable data using scikit-learn, with a confidence system that learns from user feedback and adapts in real time.

Fine-tuned DistilBERT on 40K+ labeled Reddit and Twitter posts for 3-class sentiment classification, achieving 73.1% F1 macro on a 10,360-sample held-out test set. Built an analytics layer that computes rolling sentiment averages across configurable time windows, detects anomaly shifts with severity scoring, and surfaces TF-IDF keywords per class with SHAP token-level explanations.

Trained Random Forest, XGBoost, and a PyTorch MLP on 89K Spotify tracks across 7 mood classes. XGBoost reached 99.6% and MLP reached 93.5% F1 macro, illustrating how tree-based and neural models learn threshold boundaries differently. Engineered 13 features from raw audio data and used SHAP TreeExplainer to generate plain-English mood explanations, powering a recommendation engine with cosine similarity ranking and fine-tune sliders.