Data Cleaning 101

In this repo, I have used the Kaggle Dataset to explore data preparation techniques.

Code

The missing_data_practice.ipynb notebook contains the code for the data preparation techniques.

Concepts

• Missingness Types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR)
• Univariate Imputation Techniques: Mean/Median/Mode Imputation, Random Sample Imputation
• Multivariate Imputation Techniques: KNN Imputation, MICE Imputation

Pyhton libraries used:

• pandas
• numpy
• matplotlib
• missingno
• fastimpute
• sklearn