Good Grades Predictor
Evolution and Learning in Computational and Robotic Agents
MSE 2400 Dr. Tom Way
Description
- This activity enables you to use a neural network to select classes to
take where you are likely to get a good grade.
Getting Started
Training
- Add more course information of your own as training or validation data,
using the existing columns:
- First column - name of person taking class
- Subject - brief name of class
- Lk teacher? - did you like the teacher? true or false
- Lk subject? - did you like the subject? true or false
- Friend? - did you have a friend in the class with you? true or false
- Time - time of day class met (1 = morning, 2 = midday, 3 = afternoon, 4
= evening
- Hard - how hard was the class on a scale of 0=super easy, 10=extremely
hard
- Worked - how hard did you work in the class, 0=not at all, 10=extremely
hard
- Is boring? - was the class boring? true or false
- Is fun? - was the class fun? true or false
- Learn lots? - did you learn a lot? true or false
- Days/week - how many days per week did the class meet?
- Missed % - what percentage of class meetings did you miss or skip?
- Grade (%) - what point grade did you earn (0-100)
- Good Grade? - did you get a "good" grade? true or false
- Train the network
Testing
- Using the same columns of data, add some querying rows for classes you
are thinking of taking
- This time, because you are creating querying rows, you won't enter
values for Grade (%) or Good Grade?
- If your neural network is well-trained, the output values for your
querying rows will be filled in automatically!
- Look at the output of your querying rows to see if the network thinks
you should take the class or not!
Discussion
- How accurate were the predictions?
- If you redefine some classes you did take in the past as querying rows,
how does it do?
- Is this a good way to select classes?
- What data is missing in predicting or selection which classes to take?