The A to Z of Missing Value Treatment | Data Preprocessing in Python | Data Science

🔍 In this comprehensive tutorial, we cover all that you need to know about missing value treatment. Starting with the basics, we'll explain what missing values are and why handling them is crucial for robust data analysis.

KNN Tutorial -

📌 Learn about the limitations of common approaches like mean or median-based imputation methods, and discover why they might not always be the best choice.

🐍 In the hands-on section, we'll walk you through practical Python implementations using Pandas. Explore essential techniques like dropna, fillna, ffill, and bfill, gaining a solid foundation in missing value handling.

🛠️ Elevate your skills with an introduction to Scikit-learn's powerful imputation tools! We'll cover the SimpleImputer for convenient data imputation.

🚀 Take your knowledge to the next level with two advanced techniques: the KNN Imputer and Iterative Imputer from Scikit-learn. Understand what makes the imputers superior and ensure your data is robust and ready for analysis.

Happy Learning!

[meetuchandra]

Thank you for this in-depth tutorial. Your complete playlist is awesome and helped clear so many of my concepts.

[rachitmakhija]

thank you for this hands on tutorial !!

[janaosama]

how will we know if the variables are independent or dependent on each other to choose the right method to fill missing values? is there a methods that we can use to know that? and if there, what are those methods .. thanks in advance