A complete machine-learning system that predicts AI assistant user satisfaction using behavioral signals such as device, usage category, time features, session metrics, and model metadata. Includes full ML pipeline, SHAP explainability, evaluation suite, and an interactive Streamlit analytics dashboard.

A Python-based NLP project that classifies airline tweets as positive, neutral, or negative with ~80% accuracy using Logistic Regression. Features text preprocessing with NLTK and TF-IDF, and visualizations with Matplotlib/Seaborn. Built to analyze user feedback, with applications in UX analytics and social impact.

Track custom event properties to add more context to your events inside Air360

Track technical errors to detect them inside Air360.

Track every user interaction with your website to collect clicks, mouse movements, page views, scrolls, and more.

Collect user properties inside Air360 to create powerful segments

Track users across multiple domains or devices to identify them inside Air360

Track purchases to collect eCommerce properties inside Air360.