Consumer Complaint Classifier

A smart AI web app that uses NLP and ML to understand and sort customer complaints in real-time, enabling faster responses, reducing manual work, and improving customer support.

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Inspiration

Every day, businesses receive countless customer complaints. Manually sorting through them is time-consuming and often leads to delayed responses. I was inspired to build a solution that could use AI to analyze and categorize these complaints automatically — saving time and improving customer experience.

What it does

The Consumer Complaint Classifier is a smart, AI-powered web app that:

  • Analyzes customer complaints in real-time
  • Classifies complaints into categories (e.g., Account, Billing, etc.)
  • Detects urgency level (Low, Medium, High)
  • Identifies emotional tone (Neutral, Angry, etc.)
  • Provides a confidence score and summary

It's built to help businesses understand and prioritize complaints more efficiently.

How we built it

We used the following tools and technologies:

  • Python for backend logic and data processing
  • Transformers & Scikit-learn for NLP and classification
  • Gradio for the web-based UI
  • Hugging Face Spaces for deployment

The model processes the text, extracts key information, and displays results in a clean, minimal interface.

Challenges we ran into

  • Training a model that performs well on short and ambiguous complaint texts
  • Managing classification accuracy for multi-intent messages
  • Designing an interface that’s both simple and user-friendly
  • Ensuring the app runs smoothly on Hugging Face with fast response times

Accomplishments that we're proud of

  • Successfully built and deployed a working, user-friendly AI classifier
  • Achieved high confidence outputs with real complaint examples
  • Created a clear and interactive UI for users with no technical background
  • Packaged it into a ready-to-demo web app

What we learned

  • How to build and fine-tune NLP models for classification
  • The importance of UI/UX in AI applications
  • Real-world challenges in sentiment and urgency detection
  • How to deploy ML apps on Hugging Face Spaces with Gradio

What's next for Consumer Complaint Classifier

  • Add support for multilingual complaints
  • Build a dashboard for admins to view stats and trends
  • Enable CRM integrations for seamless support handling
  • Improve emotion detection with more fine-grained emotional states

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