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3.4 EDA

Slides

Notes

The EDA for this project consisted of:

  • Checking missing values
  • Looking at the distribution of the target variable (churn)
  • Looking at numerical and categorical variables

Functions and methods:

  • df.isnull().sum() - returns the number of null values in the dataframe.
  • df.x.value_counts() returns the number of values for each category in x series. The normalize=True argument retrieves the percentage of each category. In this project, the mean of churn is equal to the churn rate obtained with the value_counts method.
  • round(x, y) - round an x number with y decimal places
  • df[x].nunique() - returns the number of unique values in x series

The entire code of this project is available in this jupyter notebook.

⚠️ The notes are written by the community.
If you see an error here, please create a PR with a fix.

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