Deep Learning
Feature Generation and Deep Learning
Assignment on Colab
Submission: May 9, 2023 11:59 PM
1.
Please read all the instructions here and on the Colab notebook.
2.
You can open the notebook and make your own copy.
3.
Once are done, you can download the ipynb file and upload it to Brightspace.
4.
There would be no extensions for this assignment, please submit on time.
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The fourth project is the development of a notebook (code + explanation) that successfully engineers 12 unique types of features β 3 for each type of feature engineering: transforming, interacting, mapping, and extracting.
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The second part of the assignment is the development of a deep learning classification model to predict the direction of the S&P500 for the dates 2018-01-01β2018-07-12 (test set).
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The feature engineering section is unrelated to the model section, you can develop any feature, not just features that would work for deep learning models (later on you can decide which features to use in your model).
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You also have to uncomment (remove #) for all the example features and make them run successfully β every feature example has some error/s that you have to fix. Please also describe the error you fixed!
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Note that we won't be attempting to measure the quality of every feature (i.e., how much it improves the model), that is slightly too advanced for this course.
Grading
A. New features:
I. Features & Deep Learning