First steps building a deep learning model

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

It basically feels like lego-blocks you are building your model by layering didn’t took me long and got my first locally trained model : 1 layer, activation function: relu

Github link to the full ipynb notebook:

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)