Day 8: Mastering Data Preprocessing | Handling Missing Values | Python Demo

Published: 05 September 2024
on channel: Protorials By Saif

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In this video, we explore different techniques to handle missing and duplicate values in datasets, a crucial part of data preprocessing. You'll learn how to fill missing data using methods like mean, median, mode, forward fill (ffill), and backward fill (bfill). We'll also cover advanced methods like interpolation for continuous data. For duplicate values, we demonstrate how to detect and remove duplicates efficiently using pandas. By the end of this video, you'll be equipped with practical strategies to clean your data for more accurate analysis and modeling.

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