6.2 Data cleaning and preparation

Notes

In this section we clean and prepare the dataset for the model which involves the following steps:

Download the data from the given link.
Reformat categorical columns (status, home, marital, records, and job) by mapping with appropriate values.
Replace the maximum value of income, assests, and debt columns with NaNs.
Replace the NaNs in the dataframe with 0 (will be shown in the next lesson).
Extract only those rows in the column status who are either ok or default as value.
Split the data in a two-step process which finally leads to the distribution of 60% train, 20% validation, and 20% test sets with random seed to 11.
Prepare target variable status by converting it from categorical to binary, where 0 represents ok and 1 represents default.
Finally delete the target variable from the train/val/test dataframe.

Add notes from the video (PRs are welcome)

⚠️	The notes are written by the community. If you see an error here, please create a PR with a fix.