Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Imputing with Model)

This video tutorial covers the final lesson on handling missing data, focusing on using models for imputation. It introduces the iterative imputer from the Scikit-learn library, which estimates missing values by modeling each column as a function of others. The tutorial provides a practical demonstration in Jupyter Lab, showing how to implement this method for numerical columns. It highlights the importance of setting minimum and maximum bounds for imputed values to ensure they remain within a reasonable range. The lesson concludes by saving the imputed data for future use in learning about outliers.

What is the main advantage of using models for imputing missing data?

• They do not require any prior data preparation.

• They can handle both numerical and categorical data without conversion.

• They can estimate missing values based on relationships with other columns.

• They are faster than simple imputation methods.

Which package inspired the iterative imputer in Scikit-learn?

• Pandas

• TensorFlow

• MICE

• NumPy

Why is the iterative imputer currently considered experimental?

• It only supports numerical columns.

• It has not been tested on large datasets.

• It is not compatible with Python 3.

• It requires a lot of computational power.

What is the purpose of setting minimum and maximum values in the iterative imputer?

• To reduce memory usage during imputation.

• To allow imputation of categorical data.

• To speed up the imputation process.

• To ensure imputed values fall within a reasonable range.

What is the first step in using the iterative imputer in Jupyter Lab?

• Importing the data from a CSV file.

• Enabling the experimental feature in Scikit-learn.

• Converting categorical data to numerical data.

• Setting up a new Jupyter notebook.

After fitting and transforming the data with the iterative imputer, what is the next step?

• Visualizing the imputed data.

• Running a statistical analysis on the imputed data.

• Exporting the data to a new file format.

• Reassigning the imputed values back to the original dataframe.

What is the significance of saving the imputed data frame at the end of the lesson?

• To use it for future lessons on outliers.

• To share it with other students.

• To ensure data integrity.

• To create a backup of the original data.