Скачать или смотреть 6.4 Handling Missing Values | EDA in Python | Hindi

In this video, we explore how to handle missing values in Exploratory Data Analysis (EDA) using Python. Missing values can significantly impact data quality and model accuracy. We cover various strategies — from simple replacements to advanced imputers like Iterative and KNN — with hands-on code examples.

📌 Topics Covered in this Video:
What are Missing values and why handling missing values are important in Exploratory data analysis ?
1. What are missing values and why they matter in EDA?
2. Dropping rows/columns based on threshold score.
3. Replacing null values with constants or computed values.
4. Using SimpleImputer in sklearn with examples.
5. Time series imputation techniques – forward fill & backward fill.
6. Multivariate approaches for handling missing values.
7. Iterative Imputer explained with sklearn implementation.
8. KNN Imputer explained with sklearn implementation.

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Missing Values, Data Preprocessing, EDA, Python for Data Science, Machine Learning, Feature Engineering, SimpleImputer, KNN Imputer, Iterative Imputer, Data Cleaning, Handling Missing Values, Missing Data Techniques, Dropping Null Values, Replacing Null Values, Forward Fill, Backward Fill, Multivariate Imputation, Sklearn Missing Values, Python EDA Tutorial, Data Quality, Imputation Techniques, Data Imputation Python, Missing Values in ML, DecodeAiML, ML Hindi, Learn Data Science

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