MLimputer: Missing Data Imputation Framework for Supervised Machine Learning
-
Updated
Jun 2, 2024 - Python
MLimputer: Missing Data Imputation Framework for Supervised Machine Learning
This script analyses the relationship between the Human Development Index (HDI), population, and non-religious groups in various countries. Plots visualise relationships between HDI, population, and non-religious groups and using scatterplots and a linear regression model to predict.
In this project, we have a set of data related to cyclists, which we intend to analyze, and it should be known that cyclists are very sensitive to air temperature.
Add a description, image, and links to the missing-data-handling topic page so that developers can more easily learn about it.
To associate your repository with the missing-data-handling topic, visit your repo's landing page and select "manage topics."