Case Study 2: Telecommunication


Factored built an easy-to-use pipeline so that simple configuration files could be used to generate ensemble and boosting models. This delivered automatic feature interpretability and prediction capabilities.

The Challenge:

To automatically generate models for a telco company by using just input metadata and a configuration file, and extract the feature importance with directionality.

The Solution:

Implemented a pipeline that is able to train tree-based and boosting models for regression and classification problems, allowing the use of hyperparameter optimization and random/custom splits as well as the execution of different methods for feature importance extraction.

The Outcome:

An easy-to-use pipeline that receives one input and one modeling configuration file and automatically produces a fitted model with its evaluation and a new file containing a summary of the feature interpretability.

Tech Stack & Skills:

Treeinterpreter, SHAP, Scikit-learn, LightGBM, SciPy, Hyperopt, AWS EC2, XLWings.

Model Interpretability, Ensemble and Boosting Methods, Model Evaluation, Excel-Python Integration.