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The sequestration of CO2 in subsurface formations is one of the attractive means of limiting CO2 concentration in the atmosphere1. The parameters that affect the CO2 trapping by different mechanisms (Structural, dissolution, mineralization, and residual) depend on various factors or predictors. Assume that you are given a set of seven predictors A through G and the final sequestration by different trapping mechanisms.
For this open-ended project you should implement three algorithms: LR, RF and one additional advanced machine learning algorithm of your choice to predict the amount of CO2 trapped by different mechanisms which are given in the attached dataset.
You need implement a reasonable train /test set so that you can compare the performance metrics such as coefficient of determination(R2) and mean absolute error (MAE) for each case. You should also plot true vs predicted plot to show how well each of your model works.
Dataset for the Assignment:
1. Dissolved.csvDownload Dissolved.csv
2. Mineral.csvDownload Mineral.csv
3. Supercritical.csvDownload Supercritical.csv
4. Trapped.csvDownload Trapped.csv
To submit:
- A single PDF with sufficient plots and graphs for each of the three algorithms and their performance metrics.
- The .ipynb file or .py file with the code for your project