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