https://github.com/rahul-404/finance_complaint
This project uses NLP and machine learning to analyze consumer complaint texts and predict whether a consumer will dispute the resolution. By processing complaint descriptions and other features, it helps identify high-priority cases, enabling more efficient customer service and proactive issue resolution.
https://github.com/rahul-404/finance_complaint
Last synced: 2 months ago
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This project uses NLP and machine learning to analyze consumer complaint texts and predict whether a consumer will dispute the resolution. By processing complaint descriptions and other features, it helps identify high-priority cases, enabling more efficient customer service and proactive issue resolution.
- Host: GitHub
- URL: https://github.com/rahul-404/finance_complaint
- Owner: Rahul-404
- License: mit
- Created: 2025-01-17T09:49:01.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-17T09:50:50.000Z (4 months ago)
- Last Synced: 2025-01-17T10:36:07.574Z (4 months ago)
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Consumer Complaint Classification for Dispute Prediction
This project utilizes Natural Language Processing (NLP) and machine learning techniques to analyze consumer complaints and predict whether a consumer will dispute the resolution. The system processes complaint descriptions and other relevant features to classify complaints as potentially leading to disputes, enabling more efficient customer service and issue resolution.
## Project Overview
The goal of this project is to build an automated system that can:
- Analyze textual complaints.
- Predict whether the complaint will lead to a consumer dispute.
- Prioritize high-risk cases for businesses to address promptly.
By using machine learning models and NLP techniques, the system can identify patterns in consumer complaints and improve decision-making processes in customer support teams.
## Features
- **Text Classification**: Analyzes the content of consumer complaints to classify them into dispute or no-dispute categories.
- **Stratified Model**: Ensures that the model considers class imbalances and maintains accuracy across different complaint types.
- **Efficient Preprocessing**: Handles text data using tokenization, lemmatization, and vectorization (e.g., TF-IDF) for effective learning.
- **Machine Learning Models**: Trains models such as Logistic Regression, Random Forest, SVM, and XGBoost to predict disputes.
- **Deployment**: The trained model can be deployed for real-time predictions in customer service applications.
## Technologies Used
- **Python**: Programming language for model implementation and NLP processing.
- **scikit-learn**: For machine learning models, data preprocessing, and evaluation metrics.
- **NLTK / spaCy**: For text processing, tokenization, lemmatization, and stop-word removal.
- **pandas & NumPy**: For data manipulation and preprocessing.
- **Matplotlib / Seaborn**: For data visualization and model evaluation metrics.
- **Flask/Django (Optional)**: For deploying the trained model as a web service (API).
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/consumer-complaint-dispute-prediction.git
cd consumer-complaint-dispute-prediction
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Download the dataset (if not included in the repository) and place it in the `/data` folder.
## How It Works
1. **Data Preprocessing**: The complaint data is preprocessed by cleaning and tokenizing the text. Common NLP techniques such as stop-word removal and lemmatization are applied to prepare the data for model training.
2. **Feature Engineering**: In addition to the text data, additional features (like complaint type, consumer demographics, and product type) are included to improve the model's accuracy.
3. **Model Training**: Several machine learning models are trained to predict whether a complaint will lead to a dispute. These models are evaluated using accuracy, precision, recall, and F1-score to determine the best-performing model.
4. **Prediction**: Once trained, the model can be used to predict new complaints. It outputs whether the complaint is likely to escalate into a dispute, helping businesses prioritize their responses.
## Running the Project
To train the model and evaluate it:
1. Preprocess the data:
```bash
python preprocess.py
```
2. Train the machine learning models:
```bash
python train_model.py
```
3. Evaluate model performance:
```bash
python evaluate_model.py
```
4. For real-time prediction (optional):
- You can deploy the trained model via a Flask or Django API for use in a live system.
## Example Usage
Once the model is trained, you can use it to predict whether a new complaint will result in a dispute:
```python
from model import predict_dispute
complaint_text = "I was charged an unauthorized fee on my credit card and nobody is responding to my inquiries!"
# Predict if the complaint will lead to a dispute
prediction = predict_dispute(complaint_text)
print("Will the consumer dispute? ", prediction)
```
## Evaluation Metrics
The model's performance is evaluated using several common metrics:
- **Accuracy**: The proportion of correct predictions (dispute vs. no dispute).
- **Precision**: The proportion of true positives (dispute) among all predicted disputes.
- **Recall**: The proportion of true positives (dispute) among all actual disputes.
- **F1-Score**: The harmonic mean of precision and recall.
## Contributing
Contributions to this project are welcome! If you have any ideas for improvements or find bugs, please feel free to open an issue or submit a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
### Contact
- Author: [Rahul Shelke]
- GitHub: [Rahul-404](https://github.com/Rahul-404)
- Email: [[email protected]]