Results: 83/100
This part of the Assessment was to train a dataset about energy efficiency of buildings with 3 regression models:
sklearn.neural_network.MLPRegressor(Neural Networks)sklearn.ensemble.RandomForestRegressor(Random Forest)sklearn.svm.SVR(Support Vector Regressor)
And we had to plot the cross validation scores of 10 Mean Squared Error Rates on each model on boxplots to see
- How much the trained data would decrease by, comparing it to the test data.
- Which model was the best in this scenario.
This part of the assignment was to generate an optimiser to solve a timetabling problem for a university. There is a .txt file that describes modules, and lists modules against which they cannot be scheduled. A module consists of one lecture per week and one or more lab sessions.
We had to design and implement a fitness function by taking the number of the concurrence constraints and multiplying them with the number of precedence constraints. This fitness function should be minimised – the ideal timetable is one with no constraint violations at all, in which case the function will return 0.
- A session cannot be scheduled for a time when any of its students or staff are in another session (concurrence constraints). The sessions for a module that clash are shown in the data file.
- A lab session cannot occur in the week before its corresponding lecture has taken place (precedence constraints).
- Visual Studio Code: https://code.visualstudio.com
- VS Code Extension for Juypter: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter
- Scikit-learn: https://scikit-learn.org/stable/
- Numpy: https://numpy.org
- Python 3.9.5: https://www.python.org