AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine

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.

25
/ 100
Experimental

This tool helps product managers and UX researchers understand why users are satisfied or dissatisfied with AI assistants. By analyzing behavioral data like device, usage category, session length, and the AI model used, it predicts user satisfaction ratings (1-5). You get an interpretable model that pinpoints which factors drive user happiness or frustration, enabling targeted improvements to the AI product.

Use this if you need to identify key behavioral patterns and product features that influence user satisfaction with your AI assistant and want clear, actionable insights.

Not ideal if you're looking for a simple survey analysis tool without integrating complex behavioral data or if your AI product doesn't collect detailed usage metrics.

AI Product Management User Experience (UX) Research Customer Satisfaction Analysis Behavioral Analytics Product Improvement
No Package No Dependents
Maintenance 6 / 25
Adoption 6 / 25
Maturity 13 / 25
Community 0 / 25

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Stars

20

Forks

Language

Python

License

MIT

Last pushed

Dec 05, 2025

Commits (30d)

0

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