A data collection containing variables with more than 30% of their values missing is handed to you. How are you going to handle them?

How to We can easily delete the rows with missing data values if the data set is huge. We use the remaining data to forecast the values, making it the quickest method.manage missing data values includes the following:

Using the panda's data frame in Python, we may replace missing values with the mean or average of the remaining data for smaller data sets. There are other methods for doing this, including df.mean() and df.fillna (mean).