AFI Finance Business School · Co-op (Mar 2021 - Feb 2022)
Project focused on the creation and optimisation of predictive models for forecasting short-term peninsular electricity demand in Spain. Using a comprehensive and analytical approach, several stages are covered, from the study of critical variables affecting electricity demand, data collection and processing, to the comparison of different predictive modelling methods. The aim of the project is to gain an in-depth understanding of the problem and to develop models that provide accurate and reliable predictions.
Project information:
▪ Programming language: Python
▪ Main libraries: Numpy, Pandas, Scikit-Learn
▪ Data visualisation: Matplotlib, Seaborn, Plotnine
▪ Neural Networks (Deep Learning): TensorFlow, Keras
▪ Models: Random Forest, XGBoost, MultiLayer Perceptron, Long Short-Term Memory
Skills: Exploratory Data Analysis · Machine Learning · Business Storytelling · Statistical Analysis · Data Visualization