This library is aimed to enhance data scientists' daily work efficiency.
Main functions are listed below:
- dataframe_observer.py: exploratory data analysis
- splitter_sampler.py: a simpler way for splitting and sampling datasets
- feature_handling.py: feature engineering for training set and/or test set
- feature_generation.py: several feature generation methods
- feature_reduction.py: for feature reduction, including filtering and embedded methods
- feature_drift: visualize and detect feature drift between training set and test set
- modeling.py: fast modeling for binary/multiclass classification and regression tasks, including model validation & hyper-parameter searching
- evaluation.py: interpret model result in different aspects
The following notebooks are quick tutorials for supervised learning:
- test_modeling_bin.ipynb: binary classification
- test_modeling_mlt.ipynb: multiclass classification
- test_modeling_reg.ipynb: regression
- Download the package
- [optional] Create a virtual env and activate it
- In terminal, run
$ cd ./Layzee
$ pip3 install .- To uninstall, run
$ pip3 uninstall layzee