Machine Learning: Random Forest with Python from Scratch - Dealing with Missing Values

The video tutorial covers data cleaning, focusing on handling missing values. It explains two main approaches: deleting rows with missing data or imputing values using mean, median, or mode. The tutorial demonstrates these techniques using a Jupyter Notebook with the Titanic dataset. It concludes by saving the cleaned dataset to a CSV file.

What will happen if we remove too many rows with missing values?

What command is used to write a DataFrame to a CSV file?

Why is it important to set 'index' to false when writing to a CSV file?

What is the next topic to be covered in the following lecture after data cleaning?