{"repo":"AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine","clone":"git clone https://github.com/AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine.git","description":"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.","language":"Python","stars":18,"topics":["ai-analytics","behavioral-analysis","behavioral-modeling","classification-model","data-science","data-visualization","explainable-ai","feature-engineering","human-ai-interaction","machine-learning"],"license":"MIT","category":"machine-learning","readme_excerpt":"AI Assistant Satisfaction Prediction Engine A behavioral machine-learning workflow for turning AI-assistant session logs into satisfaction predictions , ordinal-aware evaluation , calibrated probabilities , SHAP-based explanations , and an interactive analytics dashboard . Important: This project is a portfolio and research demo , not a production analytics or user-measurement system. The dataset is synthetic and generated by a documented process , so the model, metrics, and \"behavioral insights\" exist to demonstrate a clean, honest ML workflow. They recover a signal that was designed into the data and should not be read as real findings about how people feel about AI assistants. --- Table of Contents - Project Overview - What This Project Does - What This Project Does Not Do - Key Features - System Workflow - Project Structure - Installation - Quick Start - Training and Evaluation - Ordinal vs Flat Comparison - Calibration and Reliability - Scoring New Sessions - Streamlit Dashboard - Evaluation Metrics - Visual Reports - Testing and CI - Code Quality - Limitations - Responsible Use - Future Improvements - Tech Stack - Author - License --- Project Overview Predicting user satisfaction is not only a classification problem. To be useful, a model score has to support a defensible interpretation: - predict a 1-5 satisfaction rating from session behavior - measure how close predictions are, not just whether they are exact - report calibrated confidence, not raw scores - explain w","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}