In this repo, I have used the Kaggle Dataset to explore data preparation techniques.
The missing_data_practice.ipynb notebook contains the code for the data preparation techniques.
- 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
- pandas
- numpy
- matplotlib
- missingno
- fastimpute
- sklearn