Скачать с ютуб Handling Missing Data Easily Explained| Machine Learning в хорошем качестве

Handling Missing Data Easily Explained| Machine Learning

Data can have missing values for a number of reasons such as observations that were not recorded and data corruption. Handling missing data is important as many machine learning algorithms do not support data with missing values. In this tutorial, you will discover how to handle missing data for machine learning with Python. Specifically, after completing this tutorial you will know: How to marking invalid or corrupt values as missing in your dataset. How to remove rows with missing data from your dataset. How to impute missing values with mean values in your dataset. Github link: https://github.com/krishnaik06/EDA1 You can buy my book where I have provided a detailed explanation of how we can use Machine Learning, Deep Learning in Finance using python url: https://www.amazon.in/Hands-Python-Fi...