@article{fdi:010086036,
  title = {{D}ata {S}cience {T}oolkit : an all-in-one python library to help researchers and practitioners in implementing data science-related algorithms with less effort},
  author = {{E}l {H}achimi, {C}. and {B}elaqziz, {S}. and {K}habba, {S}. and {C}hehbouni, {A}bdelghani},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ata {S}cience {T}oolkit ({DST}) is a python library built as a wrapper layer on top of several libraries to increase the abstraction level of the code, making its users more efficient and productive. {T}he current version is widely used in our ongoing research activities that focus on optimizing agricultural management practices using artificial intelligence. {DST} adopts an object-oriented approach in implementing data science algorithms and is therefore composed of multiple classes such as the {D}ata{F}rame class that adds additional functionalities to the standard pandas dataframe and the {M}odel class that facilitates the building, training, and evaluation of machine learning models.},
  keywords = {{D}ata science ; {M}achine learning ; {D}ata processing ; {D}ata visualization ; {D}ata representation},
  booktitle = {},
  journal = {{S}oftware {I}mpacts},
  volume = {12},
  numero = {},
  pages = {100240 [4 p.]},
  ISSN = {2665-9638},
  year = {2022},
  DOI = {10.1016/j.simpa.2022.100240},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086036},
}