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--- |
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language: en |
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license: mit |
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library_name: scikit-learn |
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tags: |
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- travel |
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- destination-prediction |
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- clustering |
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- recommendation-system |
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--- |
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# Destination Cluster Predictor |
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## Model Description |
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This model is a machine learning system designed to predict and recommend travel destinations based on user preferences and requirements. It uses a combination of clustering and classification techniques to group similar destinations and make personalized recommendations. |
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### Model Type |
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The model consists of three main components: |
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- A clustering model (`destination_clustering_model.pkl`) |
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- Label encoders for categorical features (`destination_label_encoders.pkl`) |
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- A scaler for numerical features (`destination_scaler.pkl`) |
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### Input Features |
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The model takes the following input features: |
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1. **Interest**: Combinations of interests (Mountains, Wildlife, Adventure, Culture, etc.) |
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2. **Goal**: Travel goals (Adventure, Exploration, Photography, Trekking, etc.) |
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3. **Climate**: Weather conditions (Temperate, Cold, Moderate, Cool, Warm, etc.) |
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4. **Solo/Group**: Travel type (Solo, Group, or Solo/Group) |
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5. **Access**: Transportation options (Road, Trek, Air, Boat, etc.) |
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6. **Distance**: Numerical value (10-1500 km) |
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7. **Latitude**: Numerical value (24-37) |
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8. **Longitude**: Numerical value (60-78) |
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9. **Activity**: Various activities and their combinations |
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### Output |
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The model outputs: |
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- A predicted destination cluster |
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- Top 5 destination recommendations based on the input preferences |
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## Training Data |
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The model was trained on a dataset of travel destinations with their associated features and characteristics. The training data is stored in `data.xlsx` and contains 125 entries. |
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## Training Procedure |
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The model uses a combination of: |
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- Label encoding for categorical variables |
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- Standard scaling for numerical features |
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- Clustering algorithm for destination grouping |
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## Evaluation |
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The model's performance is evaluated based on: |
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- Cluster coherence |
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- Recommendation relevance |
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- User preference matching |
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## Limitations |
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- The model's recommendations are limited to the destinations present in the training data |
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- Geographic coordinates are constrained to specific ranges (Latitude: 24-37, Longitude: 60-78) |
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- Distance recommendations are limited to 10-1500 km range |
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## Usage |
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```python |
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# Example usage |
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from predictor.models import DestinationPredictor |
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predictor = DestinationPredictor() |
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recommendations = predictor.predict( |
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interest="Mountains", |
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goal="Adventure", |
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climate="Temperate", |
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travel_type="Solo", |
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access="Road", |
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distance=500, |
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latitude=30, |
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longitude=70, |
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activity="Trekking" |
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) |
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``` |
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## Environmental Impact |
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The model is lightweight and can run efficiently on standard hardware. No special GPU requirements are needed for inference. |
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## Citation |
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If you use this model in your research or application, please cite: |
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```bibtex |
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@misc{destination_predictor, |
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author = {Your Name}, |
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title = {Destination Cluster Predictor}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Hub}, |
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howpublished = {\url{https://huggingface.co/your-username/destination-predictor}} |
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} |
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``` |
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## License |
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This model is licensed under the MIT License. |
