Learnings

Take work from real life datasets and expand to utilize all the latest and greatest methods

Focus is on automating the data stack from feature engineering to model selection and feature selection. Utilizing both the traditional stack as well as the genetic algorithm based stack.

Highlights:

TPOT Automated ML Talk

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http://nbviewer.jupyter.org/github/GinoWoz1/Learnings/blob/master/TPOT%20Automated%20ML%20Talk.ipynb

Wikipedia forecasting (kaggle competition) using LSTM

Ames Automated stack - Also used in TPOT automated ML Talk:

• feature engineering
• high level spot model check
• parameter tuning
• feature selection

Boston Houses

• EDA
• Feature engineering
• model selection via pipelines
• feature selection

Bank Data set

• EDA
• Feature engineering
• model selection via pipelins
• feature selection