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Imputation of Numeric Data for Data Science in Python
Imputation means filling the missing values in the given dataset.
There are multiple methods of Imputing missing values. The methods that we'll be looking at in this notebook are
- SimpleImputer (Univariate imputation)
- Iterative Imputer (Multi-variate Imputation)
This notebook contains the Imputation techniques, their brief description and examples of each techniques.
I hope this will be a helpful resource for anyone who is trying to learn data analysis, particularly methods to deal with missing data.
Here is an outline for what we'll do in this project.
- Install and import necessary libraries.
- Download the dataset from kaggle.
- Create a train/test/validation split
- Identify input and target columns
- Identify numeric and categorical columns
- Impute (fill) missing numeric values using multiple techniques
!pip install jovian --upgrade --quiet