* Designed performed data analytics, and designed machine learning and deep learning models
using Python, Keras, TensorFlow, sklearn and xgboost to perform signal processing of
time-series events.
* Designed experiments, tested code, and built machine learning models that estimate the
probable peak demand for electric-load and extrapolating far beyond the limited historical
data.
* Analyzed the national grid and conducted detailed graph-theoretic analysis, using python,
Jupyter notebooks and networkx, to determine route-cause of alarm types. Stored, saved, and
pushed code to GitHub repository for version control and stored model artifacts using Joblib
for machine learning models.
* Designed machine learning models using RandomForestRegressor, StackingRegressor,
VotingRegressor to generate innovative modeling schema for time series models. Implemented,
configured, and tested machine learning and deep learning libraries and platforms (e.g.,
TensorFlow, Keras, XGBoost, LightGBM) for model testing and experimentation.