A Python demo for classifying e-commerce complaints with rules, Qwen, and LangChain, storing results in MySQL and generating analysis reports.
This repository is made in lieu of submission towards the solution of problem statement 2 of the OPEN AI NLP hackathon. The objective here is to classify the voice recordings of a call center proceeding by treating them as consumer complaints into the said categories of the automotive industry.
Consumer complaint classification using Complement Naive Bayes and Logistic Regression — built from scratch without sklearn
Machine learning pipeline for classifying consumer complaint texts into product categories, with code, results, and screenshots. Built for Kaiburr Assessment 2025.
Predictive road maintenance system forecasting pavement risks 3–6 months ahead using ML
Legacy Python-based complaint intake system prototype integrating speech-to-text and LLM classification to transform multilingual user feedback into structured data.
AI-powered platform for citizens to report civic issues with real-time analysis, urgency detection, and interactive dashboards.
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