Create README.md

README.md

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+---
+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.