Tabular Regression
Scikit-learn
tabular-classification
machine-learning
random-forest
clustering
k-means
feature-engineering
eda
data-science
predictive-analytics
predictive-modeling
flight-prices
aviation
airlines
travel-tech
tourism
transportation
pricing-optimization
economics
pandas
Eval Results (legacy)
Instructions to use matanzig/flight-price-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use matanzig/flight-price-prediction with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("matanzig/flight-price-prediction", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
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To discover hidden market segments and feed them into our supervised model as highly predictive features, we applied unsupervised learning techniques.
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### 🧩Unsupervised Learning (Clustering & PCA)
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To discover hidden market segments and feed them into our supervised model as highly predictive features, we applied unsupervised learning techniques.
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