https://github.com/shanmukhsrisaivedullapalli/automatic-ticket-classification
This project processes customer complaint data using pandas for data manipulation and applies text preprocessing techniques, including lemmatization, to clean and normalize complaint text. The `tqdm` library provides progress bars for efficient tracking of text processing tasks.
https://github.com/shanmukhsrisaivedullapalli/automatic-ticket-classification
matplotlib neural-networks nlp numpy pandas python3 scikit-learn seaborn tensorflow tqdm wordcloud
Last synced: 7 months ago
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This project processes customer complaint data using pandas for data manipulation and applies text preprocessing techniques, including lemmatization, to clean and normalize complaint text. The `tqdm` library provides progress bars for efficient tracking of text processing tasks.
- Host: GitHub
- URL: https://github.com/shanmukhsrisaivedullapalli/automatic-ticket-classification
- Owner: shanmukhsrisaivedullapalli
- Created: 2025-01-21T06:31:56.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-21T06:45:36.000Z (9 months ago)
- Last Synced: 2025-01-21T07:27:18.490Z (9 months ago)
- Topics: matplotlib, neural-networks, nlp, numpy, pandas, python3, scikit-learn, seaborn, tensorflow, tqdm, wordcloud
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Automatic Ticket Classification
This project implements a machine learning-based pipeline to classify customer complaints into predefined categories using Natural Language Processing (NLP) and a neural network model. The aim is to automate the classification of complaints into topics like "Bank Account Services" or "Mortgage/Loan," enhancing customer service efficiency.
## Features
- Preprocessing of textual data, including:
- Text cleaning (lowercasing, punctuation removal, etc.).
- Lemmatization using SpaCy.
- Part-of-Speech (POS) filtering.
- Visualization of word frequencies and n-grams (uni-grams, bi-grams, tri-grams).
- Topic modeling using NMF (Non-Negative Matrix Factorization).
- Deep learning-based classification using TensorFlow/Keras.
- Real-time predictions for new complaints.
## Data Source
- The dataset is loaded from a JSON file containing customer complaints and associated categories.
## Requirements
- Python 3.8+
- Jupyter Notebook or a Python environment
- Key Libraries:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- SpaCy
- TensorFlow
- Scikit-learn
- WordCloud
- TQDM
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/automatic-ticket-classification.git
cd automatic-ticket-classification
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Download the SpaCy language model:
```bash
python -m spacy download en_core_web_sm
```
4. Place the dataset file (https://www.kaggle.com/datasets/venkatasubramanian/automatic-ticket-classification) in the `input/` directory.
## Usage
1. Open the `Automatic Ticket Classification` Jupyter Notebook:
```bash
jupyter notebook automatic_ticket_classification.ipynb
```
2. Run the cells step by step to:
- Preprocess the data.
- Train the classification model.
- Generate visualizations.
- Make predictions.
3. To predict a topic for new complaints, use the pre-trained model and follow the steps in the notebook.
## Model Architecture
The deep learning model is built using TensorFlow/Keras:
- Input layer with 128 neurons.
- Hidden layers with ReLU activation and dropout regularization.
- Output layer with a softmax function for multi-class classification.
## Visualizations
- N-gram frequency plots.
- Word clouds for complaint text.
- Topic-wise distribution of complaints.
## Example Prediction
```python
text_sample = "I want a loan of rupees 400000 from Axis bank"
predicted_topic = predict(text_sample)
print(f"Predicted Topic: {predicted_topic}")
# Output: "Mortgage/Loan"
```
## Results
- **Accuracy**: Achieved high accuracy on the test dataset.
- **Topics**:
- Bank Account Services
- Credit Card or Prepaid Card
- Others
- Theft/Dispute Reporting
- Mortgage/Loan
## Contributing
Contributions are welcome! Submit a pull request or raise an issue to discuss potential improvements.