Instructions to use Mekam/salary_prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Mekam/salary_prediction with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Mekam/salary_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
inference: false tags:
- sklearn
- polynomial-regression library_name: mlconsole metrics:
- mae
- loss datasets:
- salary_prediction model-index:
- name: salary_prediction
results:
- task:
type: polynomial-regression
name: polynomial-regression
dataset:
type: csv
name: Salary prediction of Data Peofessions
metrics:
- type:R^2 score in training name: accuracy in training value: 0.9205775022717194
- type:R^2 score in test name: accuracy in test value: 0.8852521574169744
- type: mae in training name: Mean absolute error in training value: 0.1487798237760919
- type: loss name: Model loss in training value: 0.07942249772828057
- type: mae in test name: Mean absolute error in test value: 0.15617568153541936
- type: loss name: Model loss in test value: 0.14840390509540233
- task:
type: polynomial-regression
name: polynomial-regression
dataset:
type: csv
name: Salary prediction of Data Peofessions
metrics:
- Downloads last month
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