?? Day28 of #100DaysOfPython ??

?? Day28 of #100DaysOfPython ??

Today, we're diving into another technique for handling missing values known as Frequent Categories Imputation!

Frequent Categories Imputation is used to handle missing values by replacing them with the most frequent category.

Checkout more techniques on handling missing values!

Let's dive into an example on imputing missing observations in the feature by replacing missing values with most frequent categories:

Link to titanic dataset

1. observing missing values across selected features


2. observing frequency of missing values in BsmtQual


3. observing frequency of missing values across GarageType


4. observing frequency of missing values across FireplaceQu


5. replacing missing values with most frequently occuring label in each feature

Advantages:

  • Easy & Quick to implement

Disadvantages:

  • Distorts the original data distribution therefore it should not be used in cases where number of missing values are high


Stay tuned for more ways of handling missing values in data through feature engineering techniques in python!





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