Tabular Classification
Keras
Scikit-learn
English
tensorflow
random-forest
cnn
clustering
nlp
computer-vision
recommendation-system
time-series
streamlit
Instructions to use OKTAYBBS/DataScientst-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OKTAYBBS/DataScientst-models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://OKTAYBBS/DataScientst-models") - Scikit-learn
How to use OKTAYBBS/DataScientst-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("OKTAYBBS/DataScientst-models", "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
| Proje,Tip,Skor,Skor Adı | |
| gold,Regresyon,0.9904,R² | |
| student,Regresyon,0.8489,R² | |
| uber,Regresyon,0.7775,R² | |
| mobile,Sınıflandırma,0.8125,Accuracy | |
| wine,Sınıflandırma,0.675,Accuracy | |
| churn,Sınıflandırma,0.7889,Accuracy | |
| nba,Kümeleme,0.452,Silhouette | |
| creditcard,Kümeleme,0.5309,Silhouette | |
| spotify,Kümeleme,0.3269,Silhouette | |
| mask,Sınıflandırma,0.825,Accuracy | |
| sms,Sınıflandırma,0.9803,Accuracy | |
| imdb_sentiment,Sınıflandırma,0.873,Accuracy | |
| fake_news,Sınıflandırma,0.9756,Accuracy | |
| movie_rec,Metriksiz,,- | |
| book_rec,Metriksiz,,- | |
| song_rec,Metriksiz,,- | |
| stock,Regresyon,0.975,R² | |
| weather,Regresyon,0.912,R² | |
| walmart,Regresyon,0.7669,R² | |
| social_media_viz,Metriksiz,,- | |
| co2_viz,Metriksiz,,- | |
| ecommerce_viz,Metriksiz,,- | |
| pneumonia,Derin Öğrenme,0.925,Val_Accuracy | |
| face_emotion,Derin Öğrenme,0.6542,Val_Accuracy | |
| text_gen,Metriksiz,,- | |