Suppose there is a dataset having variables with missing values of mor

1.	Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?
Answer» Depending on the size of the dataset, we FOLLOW the below WAYS: In case the datasets are small, the MISSING values are substituted with the mean or average of the remaining data. In pandas, this can be done by USING mean = df.mean() where df represents the pandas dataframe representing the dataset and mean() calculates the mean of the data. To substitute the missing values with the calculated mean, we can use df.fillna(mean). For LARGER datasets, the rows with missing values can be removed and the remaining data can be used for data prediction.

Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?

Answer»

Depending on the size of the dataset, we FOLLOW the below WAYS:

In case the datasets are small, the MISSING values are substituted with the mean or average of the remaining data. In pandas, this can be done by USING mean = df.mean() where df represents the pandas dataframe representing the dataset and mean() calculates the mean of the data. To substitute the missing values with the calculated mean, we can use df.fillna(mean).
For LARGER datasets, the rows with missing values can be removed and the remaining data can be used for data prediction.