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Train a xgboost model to predict housing prices in Bangalore
pip install tid-home-prices-prediction
PyPI declares 121 unique dependency rules for this release. Environment markers are shown when supplied by the project.
==0.1.3==21.3.0==21.2.0==2.0.5==21.4.0==0.2.0==4.11.1==5.0.0==0.8.0==2022.6.15==1.15.0==2.1.0==8.1.3==0.4.4==0.9.1==0.11.0==1.6.0==5.1.1The compact report shows 18 of 121 declarations. The interactive dependency graph loads the complete metadata.
tid-home-prices-prediction publishes 1 wheel and 1 source archive for version 0.0.4. Wheel platform tags: any.
Declared Python classifiers: 3.6, 3.7, 3.8, 3.9.
PyPI lists 1 release with files. The first dated release is ; 1 release falls within the 365 days preceding the latest dated release. The current release files were uploaded on .