SHAP Algorithm Adaptation for Sales Forecasting
Budget: €30 – €250 EUR
I'm looking for an expert in machine learning and data science to adapt the SHAP (SHapley Additive exPlanations) algorithm for a zero-shot multivariate time series forecasting model (IBMs Tiny Time Mixers), specifically for sales data.
The primary aim of this project is to enhance the interpretability of the model. In particular, we need to focus on understanding the impact of different features on the model's predictions.
Skills and Experience Required:
- Proficiency in Python and relevant data science libraries (e.g., pandas, numpy, scikit-learn)
- Strong understanding of machine learning algorithms and their interpretability
- Specific experience with SHAP or similar interpretability tools would be highly advantageous
- Experience in time series forecasting
The primary aim of this project is to enhance the interpretability of the model. In particular, we need to focus on understanding the impact of different features on the model's predictions.
Skills and Experience Required:
- Proficiency in Python and relevant data science libraries (e.g., pandas, numpy, scikit-learn)
- Strong understanding of machine learning algorithms and their interpretability
- Specific experience with SHAP or similar interpretability tools would be highly advantageous
- Experience in time series forecasting