Week 14 - Machine Learning - (Supervised Learning) Visualizing Data for Classification for german bank credit from Principles of M.L. Python by Microsoft Learning

Liaw Bei Le · June 25, 2021

Learning   Python   Machine Learning   Matplotlib   Seaborn   Pandas
  • Visualizing Data for Classification
  • Exploratory data analysis
  • Examine classes and class imbalance
  • Visualize class separation by numeric features
    • Box plots
    • Violin plots
  • Visualize class separation by categorical features
    • Bar charts

These are my notes taken from Microsoft Learning’s Principles of Machine Learning in Python - Module 2.

What I learnt:

Visualizing Data for Classification

Method: Visualize and explore data (exploratory data analysis)

  • Examine the imbalance in the label cases using a frequency table.
  • Find numeric or categorical features that separate the cases using visualization.

Goal: To explore a dataset about automobile pricing to determine which features may be useful in predicting the customers with bad credit.

  • for classification problems, we are looking for features that help separate the label categories.
  • To understand which features are useful for class separation.

I have reviewed and went through Visualizing Data for Classification for german bank credit in this link.

Twitter, Facebook