Data Preprocessing

Data Preprocessing

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When it comes to Machine Learning a common mistake that people tend to make is training the model directly on the raw data. This often adds unnecessary variables to the equation. The three steps of Data Preprocessing are Data Cleaning, Data Transformation, and Feature Selection. In this class you will learn about various Data Preprocessing techniques using Python.

At the end of this workshop you will be able to:

  • Load dataset into our Jupyter notebook file.
  • We can distinguish two types of variables: categorical and numerical.
  • Finding the missing or incorrect values.
  • Replacing missing values
  • Transform all the object type values to numerical values.

Requirements:

  • Computer with at least Window 10
  • Download Jupyter Notebooks or Anaconda Navigator

Tutor: Ilyes Nasraoui

Mentorship at Nestria

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