Inspired by last weeks presentation on AI, I decided to start working with the material already myself.
- Installed Anaconda and setup my work environment Jupyter Notebook/Lab with Keras, Tensorflow, Scikit-learn and Scikeras
- Following Digital Oceans Guide to predict P(employee leaving) based on this HR dataset on Kaggle
Github link to the full ipynb notebook: https://github.com/TinkerFrank/AI_Project_0/blob/7ccf2b97ee22a2aa9fb9b4cde9d5810252d955e1/JupyterLabTest.ipynb
- To Do: dive deeper in optimization and activation functions to know when and where to apply them the best.
Also started working on the kaggle competition: Titanic – Machine Learning from Disaster, which really got me thinking about:
- prepping the datasets (how to deal with incompleteness) and making the least amount of assumptions (prove most with data analysis).
- spend more time on doing data science on the test-data validating assumptions and so
- able to reason/explain what was or were the most important features that contributed to the result (to see if it makes sense)