Tabular Regression
Scikit-learn
Joblib
Indonesian
gradient-boosting
regression
salary-prediction
tabular
Instructions to use Minee12/ds-salary-gradient-boosting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Minee12/ds-salary-gradient-boosting with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Minee12/ds-salary-gradient-boosting", "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
Download preprocessing_config.json from Minee12/ds-salary-gradient-boosting: direct link, hf CLI and curl.
- Browser
- Download file 6.06 kB
-
https://huggingface.co/Minee12/ds-salary-gradient-boosting/resolve/main/preprocessing_config.json
- Command line
-
hf download hf://Minee12/ds-salary-gradient-boosting/preprocessing_config.json
-
curl -L -o preprocessing_config.json https://huggingface.co/Minee12/ds-salary-gradient-boosting/resolve/main/preprocessing_config.json
6.06 kB
| { | |
| "target": "salary_in_usd", | |
| "urutan_fitur_input": [ | |
| "work_year", | |
| "experience_level", | |
| "employment_type", | |
| "job_title", | |
| "employee_residence", | |
| "remote_ratio", | |
| "company_location", | |
| "company_size" | |
| ], | |
| "fitur_numerik": [ | |
| "work_year", | |
| "remote_ratio" | |
| ], | |
| "fitur_kategorikal": [ | |
| "experience_level", | |
| "employment_type", | |
| "job_title", | |
| "employee_residence", | |
| "company_location", | |
| "company_size" | |
| ], | |
| "kategori_valid": { | |
| "experience_level": [ | |
| "EN", | |
| "EX", | |
| "MI", | |
| "SE" | |
| ], | |
| "employment_type": [ | |
| "CT", | |
| "FL", | |
| "FT", | |
| "PT" | |
| ], | |
| "job_title": [ | |
| "3D Computer Vision Researcher", | |
| "AI Developer", | |
| "AI Programmer", | |
| "AI Scientist", | |
| "Analytics Engineer", | |
| "Applied Data Scientist", | |
| "Applied Machine Learning Engineer", | |
| "Applied Machine Learning Scientist", | |
| "Applied Scientist", | |
| "Autonomous Vehicle Technician", | |
| "Azure Data Engineer", | |
| "BI Analyst", | |
| "BI Data Analyst", | |
| "BI Data Engineer", | |
| "BI Developer", | |
| "Big Data Architect", | |
| "Big Data Engineer", | |
| "Business Data Analyst", | |
| "Business Intelligence Engineer", | |
| "Cloud Data Architect", | |
| "Cloud Data Engineer", | |
| "Cloud Database Engineer", | |
| "Compliance Data Analyst", | |
| "Computer Vision Engineer", | |
| "Computer Vision Software Engineer", | |
| "Data Analyst", | |
| "Data Analytics Consultant", | |
| "Data Analytics Engineer", | |
| "Data Analytics Lead", | |
| "Data Analytics Manager", | |
| "Data Analytics Specialist", | |
| "Data Architect", | |
| "Data DevOps Engineer", | |
| "Data Engineer", | |
| "Data Infrastructure Engineer", | |
| "Data Lead", | |
| "Data Management Specialist", | |
| "Data Manager", | |
| "Data Modeler", | |
| "Data Operations Analyst", | |
| "Data Operations Engineer", | |
| "Data Quality Analyst", | |
| "Data Science Consultant", | |
| "Data Science Engineer", | |
| "Data Science Lead", | |
| "Data Science Manager", | |
| "Data Science Tech Lead", | |
| "Data Scientist", | |
| "Data Scientist Lead", | |
| "Data Specialist", | |
| "Data Strategist", | |
| "Deep Learning Engineer", | |
| "Deep Learning Researcher", | |
| "Director of Data Science", | |
| "ETL Developer", | |
| "ETL Engineer", | |
| "Finance Data Analyst", | |
| "Financial Data Analyst", | |
| "Head of Data", | |
| "Head of Data Science", | |
| "Head of Machine Learning", | |
| "Insight Analyst", | |
| "Lead Data Analyst", | |
| "Lead Data Engineer", | |
| "Lead Data Scientist", | |
| "Lead Machine Learning Engineer", | |
| "ML Engineer", | |
| "MLOps Engineer", | |
| "Machine Learning Developer", | |
| "Machine Learning Engineer", | |
| "Machine Learning Infrastructure Engineer", | |
| "Machine Learning Manager", | |
| "Machine Learning Research Engineer", | |
| "Machine Learning Researcher", | |
| "Machine Learning Scientist", | |
| "Machine Learning Software Engineer", | |
| "Manager Data Management", | |
| "Marketing Data Analyst", | |
| "Marketing Data Engineer", | |
| "NLP Engineer", | |
| "Power BI Developer", | |
| "Principal Data Analyst", | |
| "Principal Data Architect", | |
| "Principal Data Engineer", | |
| "Principal Data Scientist", | |
| "Principal Machine Learning Engineer", | |
| "Product Data Analyst", | |
| "Product Data Scientist", | |
| "Research Engineer", | |
| "Research Scientist", | |
| "Software Data Engineer", | |
| "Staff Data Analyst", | |
| "Staff Data Scientist" | |
| ], | |
| "employee_residence": [ | |
| "AE", | |
| "AM", | |
| "AR", | |
| "AS", | |
| "AT", | |
| "AU", | |
| "BA", | |
| "BE", | |
| "BG", | |
| "BO", | |
| "BR", | |
| "CA", | |
| "CF", | |
| "CH", | |
| "CL", | |
| "CN", | |
| "CO", | |
| "CR", | |
| "CY", | |
| "CZ", | |
| "DE", | |
| "DK", | |
| "DO", | |
| "DZ", | |
| "EE", | |
| "EG", | |
| "ES", | |
| "FI", | |
| "FR", | |
| "GB", | |
| "GH", | |
| "GR", | |
| "HK", | |
| "HN", | |
| "HR", | |
| "HU", | |
| "ID", | |
| "IE", | |
| "IL", | |
| "IN", | |
| "IQ", | |
| "IR", | |
| "IT", | |
| "JE", | |
| "JP", | |
| "KE", | |
| "KW", | |
| "LT", | |
| "LU", | |
| "LV", | |
| "MA", | |
| "MD", | |
| "MK", | |
| "MT", | |
| "MX", | |
| "MY", | |
| "NG", | |
| "NL", | |
| "NZ", | |
| "PH", | |
| "PK", | |
| "PL", | |
| "PR", | |
| "PT", | |
| "RO", | |
| "RS", | |
| "RU", | |
| "SE", | |
| "SG", | |
| "SI", | |
| "SK", | |
| "TH", | |
| "TN", | |
| "TR", | |
| "UA", | |
| "US", | |
| "UZ", | |
| "VN" | |
| ], | |
| "company_location": [ | |
| "AE", | |
| "AL", | |
| "AM", | |
| "AR", | |
| "AS", | |
| "AT", | |
| "AU", | |
| "BA", | |
| "BE", | |
| "BO", | |
| "BR", | |
| "BS", | |
| "CA", | |
| "CF", | |
| "CH", | |
| "CL", | |
| "CN", | |
| "CO", | |
| "CR", | |
| "CZ", | |
| "DE", | |
| "DK", | |
| "DZ", | |
| "EE", | |
| "EG", | |
| "ES", | |
| "FI", | |
| "FR", | |
| "GB", | |
| "GH", | |
| "GR", | |
| "HK", | |
| "HN", | |
| "HR", | |
| "HU", | |
| "ID", | |
| "IE", | |
| "IL", | |
| "IN", | |
| "IQ", | |
| "IR", | |
| "IT", | |
| "JP", | |
| "KE", | |
| "LT", | |
| "LU", | |
| "LV", | |
| "MA", | |
| "MD", | |
| "MK", | |
| "MT", | |
| "MX", | |
| "MY", | |
| "NG", | |
| "NL", | |
| "NZ", | |
| "PH", | |
| "PK", | |
| "PL", | |
| "PR", | |
| "PT", | |
| "RO", | |
| "RU", | |
| "SE", | |
| "SG", | |
| "SI", | |
| "SK", | |
| "TH", | |
| "TR", | |
| "UA", | |
| "US", | |
| "VN" | |
| ], | |
| "company_size": [ | |
| "L", | |
| "M", | |
| "S" | |
| ] | |
| }, | |
| "encoding": "pd.get_dummies(drop_first=True) saat pelatihan; saat inferensi: get_dummies lalu reindex ke model_columns.json", | |
| "model_membutuhkan_scaling": false, | |
| "metrik_uji": { | |
| "RMSE": 52027.30882920043, | |
| "MAE": 39212.72439070619, | |
| "MSE": 2706840864.008997, | |
| "R2": 0.3811622356088731 | |
| }, | |
| "parameter_terbaik": { | |
| "learning_rate": 0.1062, | |
| "loss": "huber", | |
| "max_depth": 4, | |
| "max_features": 0.2, | |
| "min_samples_leaf": 13, | |
| "min_samples_split": 7, | |
| "n_estimators": 287, | |
| "subsample": 0.9018 | |
| }, | |
| "versi": { | |
| "scikit-learn": "1.6.1", | |
| "python": "3.13.18" | |
| } | |
| } |