annalamboglia/minicontest2

Minicontest2

Modern society strongly relies on oil. Indeed, despite more and more progress has been made in the production of green (e.g. renewable) energy, our daily and industrial life still makes use of fossil fuels. Thus, while building a society totally based on green energy is the final aim, industries are looking for alternatives able to sustain the energy transition process. A possible solution is the use of biofuels, namely fuels compatible with machinery designed for fossil fuels but obtained by processing other sources instead of oil. Among all, raw feedstocks processing (e.g. by using pyrolysis) is a promising approach to produce biofuels from raw vegetable wastes (e.g. agricultural, lumberjacking, etc.).

One of the problems of such an approach is in the high variability of the transformation efficiency, which is strongly affected by factors associated with the characteristics of the source material, pre-processing stages, storage type, manipulation etc. The aim of the second Machine Learning Mini-Contest (MC2) for the academic year 2021/2022 is to predict the Oxygen/Carbon ratio (numeric prediction) for a given raw feedstock sample described by its properties and characteristics.

Contest Rules

Each student has to predict (regression) the Oxygen/Carbon ratio realising one or more prediction models using data analysis and Machine Learning techniques. The performance measure to minimise is the Mean Absolute Errore (MAE). It is mandatory for the student who will achieve the best performance on the test dataset, to discuss the process steps followed in order to reach the development of the final model. The winning student presentation will be held during the lesson on December the 3rd. If the presentation and the proposed solution will be judged positively, the author will be relieved from the final contest. Each participant is free to use external tools (i.e. Weka, Knime, MatLab, etc.).

Kaggle: https://www.kaggle.com/c/unina-machine-learning-2122-minicontest-n2