Document classification tool

Context et problematic

A large part of the data science projects conducted by the client is based on text documents. To process and model this data, Data Scientists often use the same tools/models/functions.

In order to make these steps faster, more generic and accessible to non Data Scientists, we have developed a common package that performs all the classical tasks related to document classification by Machine Learning.


  • Intuitive and very high-level user API
  • Support scikit-learn models and methods
  • Ability to customize each end of the pipeline
  • Generic code making it easy for other data scientists to contribute
  • Be able to interpret predictions
  • Be able to search for hyper parameters
  • Our intervention

    1 Data Scientist

    • Development of data pre-processing pipelines
    • Managing the link with underlying scikit-learn objects (integration)
    • Development of the interpretability module
    • Development of the hyperparameter search module
    • Publishing the package and demos to other Data scientists
    • Adding features based on specific needs of Data scientists


    Package published !
    Objectives achieved.
    Used by several projects (including the support ticket classification project).

    Technical environment

    Python (scikit-learn, pandas, optuna, lime, shap, nltk, MLflow, plotly)

    Together with our customers, we build solutions that change and facilitate their daily lives.

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