Customer Support Ticket Intelligence Platform

A production-grade, portfolio-ready Deep Learning and Natural Language Processing (NLP) system designed to automate customer support ticket classification and priority routing.

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🎯 Project Objectives

1. Business Objective

• Automatically classify customer support tickets to minimize manual triaging and routing delays.
• Reduce average first-response times and improve customer satisfaction scores (CSAT).
• Align technical model performance (Accuracy, F1) to concrete business ROI (hours saved, auto-triaged tickets).

2. Learning Objective

• Systematically master sequence modeling in PyTorch:
  • From text preprocessing and vocabulary building,
  • Through dense word embedding learning,
  • To recurrent foundations (Simple RNNs, LSTM, GRU, BiLSTM, and Deep stacked recurrent architectures).
• Understand theoretical optimization challenges (vanishing/exploding gradients) and regularizations (recurrent dropout, weight decay).

3. Portfolio Objective

• Demonstrate rigorous deep learning engineering practices: modularized code structure, reproducible experiment logs, model benchmarking (latency, memory, parameters), error analysis profiling, and explainability dashboards.

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🏗️ Project Structure

customer-support-ticket-intelligence-platform/
├── data/                  # Raw and processed support tickets (CSV, mappings)
├── notebooks/             # Step-by-step jupyter notebooks for theory & EDA
├── src/                   # Reusable production-grade python modules
│   ├── preprocessing.py   # Cleaning, tokenization, sequence preparation
│   ├── vocabulary.py      # Vocabulary construction, OOV tracking, integer indexing
│   ├── datasets.py        # PyTorch Dataset & DataLoader utilities
│   ├── models.py          # Sequence models (RNN, LSTM, GRU, BiLSTM, Deep architectures)
│   ├── training.py        # Custom PyTorch training loops with metrics logging
│   ├── evaluation.py      # Performance metrics, inference benchmarking, confusion matrices
│   ├── explainability.py  # Feature attribution, critical words extraction
│   └── utils.py           # Helper logic, path utilities, serialization
├── streamlit_app/         # Streamlit-based model dashboards and web interface
│   ├── app.py             # App entry point
│   └── pages/             # App views (predict, benchmarking, explainability, metrics)
├── docs/                  # Educational writeups, data profiles, and decisions logs
├── outputs/               # Saved model checkpoints, evaluation artifacts, and plots
└── README.md              # Project overview

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📚 Sequence Modeling Learning Roadmap

We follow a linear pedagogical sequence to model text:

1. Representations: Exploratory Data Analysis → Word Embeddings (Dense Learned Embeddings, One-Hot vs. Dense, Dimension Sweeps).
2. Simple RNNs: Explaining recurrence, hidden states, cell calculations, and vanishing gradients.
3. Gated RNNs: Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRU).
4. Advanced Recurrent Nets: Bidirectional RNNs, Deep Stacked recurrent networks, and capacity/complexity tradeoffs.
5. Regularization & Optimization: Dropout, Weight Decay, and optimizer studies (SGD, RMSprop, Adam, AdamW).
6. Interpretability & Deploy: Explainability techniques (e.g., attribution scores) and Streamlit dashboard hosting.

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🔧 Setup & Installation

Detailed installation guidelines will be populated in docs/DEPLOYMENT_GUIDE.md.

Prerequisites

• Python 3.8+
• PyTorch 2.0+
• Streamlit

# Clone the repository
git clone https://github.com/yourusername/customer-support-ticket-intelligence-platform.git
cd customer-support-ticket-intelligence-platform

# Install dependencies
pip install -r requirements.txt