Emmanuel Blonkowski is a data scientist with a Master’s in statistics who tackles IoT industry challenges.
He uses machine learning to solve problems and uncover insights in healthcare IoT, agriculture, and energy. His work focuses on time series forecasting, classification, and optimization using R and Python.
Emmanuel specializes in Deep Learning with multi-modal data. He combines weather, satellite, and review data to accurately forecast sensor readings, improving healthcare and industrial processes.
This portfolio showcases 3 projects highlighting different aspects of the data science process.
These projects use Python and time series analysis to benefit IoT businesses. They demonstrate Deep Learning, Supervised Learning, and signal processing techniques.
Import
Model
Forecast with Confidence Estimates to Improve Decision Making
This project demonstrates how to enhance business decision-making using probabilistic forecasts on time series data. It walks through a practical technique that has proven successful in real-world scenarios. While using simplified data for clarity, the method comes from the well-cited DeepAR article and can be applied to actual business problems.
- Repository
- Write-up in Towards Data-Science
Python Tensorflow Prediction Maximum likelihood estimator time-series