Data Cleaning

Cleaning data and handling the lack of some values typically involves several steps:

1. Identify missing or inconsistent data: We first have to scan the dataset for null values, anomalies, or formatting issues that could be caused by errors.
2. Assess the impact of missing values: We then evaluate how much data is missing and determine how critical those fields are to the analysis.
3. Select a handling strategy: Next, we choose whether to fill in missing data (imputation), exclude affected rows, or flag incomplete records. It all depends on the business context, of course.
4. Impute or remove values: If you're going to impute data, use methods such as mean, median, or mode imputation, to calculate the missing values in a way that makes sense to the context of the data. Otherwise, just remove records with excessive gaps if necessary.
5. Verify the cleaned dataset: Run data validation checks to ensure that the cleaning process preserved data integrity and did not introduce bias.