AI Complaint Detector

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  Supports bulk complaint classification via CSV upload, enabling fast and efficient analysis of large datasets with automated predictions.

🚀 Overview: AI Complaint Detector is an NLP-based application designed to automatically classify financial customer complaints into meaningful categories. It helps reduce manual effort and improves response efficiency.

💡 Problem: Financial institutions receive a large volume of customer complaints daily. Manually categorizing these complaints is time-consuming, error-prone, and inefficient.

🎯 Solution: This project uses Natural Language Processing (NLP) and a deep learning model to automatically classify complaints into predefined categories, enabling faster and smarter handling.

📊 Categories Supported: The model predicts the following 5 financial complaint categories:

• Credit Reporting
• Debt Collection
• Mortgages & Loans
• Credit Card
• Retail Banking

⚙️ How it Works:

• User inputs a complaint in text form
• Text is preprocessed using tokenization
• Converted into sequences and padded
• Passed into a trained TensorFlow/Keras model
• Model predicts the most relevant category

🛠️ Tech Stack:

• Python
• TensorFlow / Keras
• Streamlit
• NumPy & Pandas
• NLP (Tokenizer + Padding)

🏆 Key Features:

• Real-time complaint classification
• Simple and interactive UI
• Deep learning-based prediction
• Domain-specific financial categorization

🌍 Impact (Social Good): This system can help financial organizations automate complaint handling, reduce response time, and improve customer satisfaction by quickly identifying the nature of issues.

🚧 Challenges Faced:

• Handling text preprocessing and input formatting
• Managing model compatibility and input shapes
• Integrating the trained model into a Streamlit app
• Environment and dependency setup

📚 What I Learned:

• Practical implementation of NLP in real-world problems
• Working with deep learning models using TensorFlow
• Building and integrating ML models into web apps
• Using GitHub for project version control

🔮 Future Improvements:

• Improve model accuracy with more data
• Add more complaint categories
• Deploy as a live web application
• Enhance UI/UX for better experience

Built With

• keras
• numpy
• pandas
• python
• scikit-learn
• streamlit
• tensorflow