Jupyter notebooks demonstrating core operations of the Pandas library, including data wrangling, grouping, merging, and missing data handling.

This Notebook explores the impact of different missing data handling techniques on a medical dataset by analyzing missingness patterns, variable distributions, and inter-feature relationships to inform appropriate imputation strategies.

This notebook covers practical techniques for handling missing data in both numerical and categorical features, helping improve model performance. Suitable for both beginners and experienced data scientists.

In this notebook, i show a examples to implement imputation methods for handling missing values.