Machine Learning Project from A to Z

In this Post I will go through the workflow of a full machine learning project with the california housing dataset as a full Machine Learning project, from A to Z, like it is described in the book “Hands-on Machine Learning with Scikit-learn & Tensorflow” by Aurelien Geron.

Table of Contents:

  • Import Libraries
  • California housing Dataset
  • Big Picture
  • Train/Test Split
  • Extensive Data Exploration
    • Searching for Correlations
  • Data Preparation
    • How to process Categorical Attributes and Text
    • Custom transfer
    • Feature Scaling
    • Pipelines for tranformation
  • Train Models
  • Fine Tuning
  • Evaluation
  • Summary


Unfortunately with WordPress, it is only possible to display a Jupyter Notebook in a small window. Therefore I you can only view it on github, because it is better displayed there.


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