# BERT Fine-Tuned Model for Churn Prediction

This repository hosts a fine-tuned version of the **BERT** model optimized for **churn prediction** using the provided dataset. The model is designed to analyze textual data and predict customer churn with high accuracy.

## Model Details
- **Model Architecture**: BERT (Bidirectional Encoder Representations from Transformers)  
- **Task**: Churn Prediction  
- **Dataset**: Custom Dataset (processed and structured for binary classification)  
- **Quantization**: FP16  
- **Fine-tuning Framework**: Hugging Face Transformers  

## 🚀 Usage

### Installation
```bash
pip install transformers torch pandas scikit-learn
```

### Loading the Model
```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/bert-churn-prediction"
model = BertForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = BertTokenizer.from_pretrained(model_name)
```

### Churn Prediction Inference
```python
def predict_churn(text):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        predicted_label = torch.argmax(outputs.logits, dim=1).item()
    return "Churn" if predicted_label == 1 else "Not Churn"

# Example usage
customer_review = "I am unhappy with the service and want to cancel."
print(predict_churn(customer_review))
```

## 📊 Evaluation Results
After fine-tuning the **BERT** model for churn prediction, we evaluated the model's performance on the test set. The following results were obtained:

| Metric      | Score  | Meaning |
|------------|--------|------------------------------------------------|
| **Accuracy** | 82.5%  | Measures overall prediction correctness |
| **Precision** | 100.3%  | Fraction of relevant churn predictions |
| **Recall**   | 78.7%  | Ability to detect all churn cases |
| **F1-Score**| 80.5%  | Harmonic mean of precision and recall |

## Fine-Tuning Details
Model: Fine-tuned BERT for churn prediction using a custom dataset.
Training: Run for 3 epochs with a batch size of 8, using the AdamW optimizer and a learning rate of 2e-5.

### Dataset
The dataset consists of customer interactions, reviews, and metadata used to determine churn likelihood. Textual features like **title, features, description, and average rating** were merged to create input text samples.

### Training
- **Number of epochs**: 3  
- **Batch size**: 8 
- **Evaluation strategy**: Epochs  

### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

## 📂 Repository Structure
```bash
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation
```

## ⚠️ Limitations
- May struggle with ambiguous or very short text inputs.
- Quantization may slightly impact model accuracy.
- Performance may vary across different industries and customer segments.

## 🤝 Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.