Missing data imputation with fancyimpute in python

Missing data imputation with fancyimpute in python

fancyimpute is a Python library that provides a variety of matrix completion and imputation algorithms. Some of the popular algorithms included are KNN (K-Nearest Neighbors), SoftImpute, IterativeImputer (formerly MICE), and others.

Here's how to perform missing data imputation using fancyimpute:

1. Installation

First, you need to install fancyimpute:

pip install fancyimpute

2. Usage

Let's go through two of the popular imputation methods provided by fancyimpute: KNN and IterativeImputer.

2.1. KNN Imputation:

This method uses the k-nearest neighbors approach. For every missing value, it looks at k nearest neighbors (rows) and fills in the missing value based on the values of its neighbors.

import numpy as np from fancyimpute import KNN # Sample data with missing values (use np.nan for missing values) data = np.array([ [1, 2, np.nan], [4, 5, 6], [7, 8, 9], [np.nan, 3, 3], [7, np.nan, 2] ]) # Perform KNN imputation knn_imputer = KNN(k=3) # Use 3 nearest rows data_imputed = knn_imputer.fit_transform(data) print(data_imputed)

2.2. IterativeImputer (formerly MICE):

This method uses a series of regression models. Initially, missing values are imputed using a simple method, e.g., by column means. Then, the imputer goes through each missing value, treats it as a dependent variable, and uses the other columns as predictors in a regression.

from fancyimpute import IterativeImputer # Using the same data as before # Perform Iterative imputation iterative_imputer = IterativeImputer() data_imputed = iterative_imputer.fit_transform(data) print(data_imputed)

These are just two of the many imputation methods provided by fancyimpute. You can choose an appropriate imputation technique based on your data and the nature of the missingness.

Note: Always validate the results of imputation and ensure that the imputed values make sense in the context of your specific application.

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