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:

Interest: Combinations of interests (Mountains, Wildlife, Adventure, Culture, etc.)
Goal: Travel goals (Adventure, Exploration, Photography, Trekking, etc.)
Climate: Weather conditions (Temperate, Cold, Moderate, Cool, Warm, etc.)
Solo/Group: Travel type (Solo, Group, or Solo/Group)
Access: Transportation options (Road, Trek, Air, Boat, etc.)
Distance: Numerical value (10-1500 km)
Latitude: Numerical value (24-37)
Longitude: Numerical value (60-78)
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

# 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:

@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.