You are analyzing sales data and notice missing values

Solution

The first critical step when you encounter missing data is to clean the data . Missing values can significantly skew analysis if not addressed early. Data cleaning can involve either removing the rows with missing data or imputing the missing values using statistical techniques (mean, median, mode imputation, etc.) depending on the nature of the data and the extent of the missingness. Cleaning is a prerequisite before diving into modeling, visualization, or interpretation. Without addressing missing values, your analysis and conclusions may be misleading or incorrect. Why Other Options Are Wrong : A) Incorrect : Building predictive models without first cleaning the data would lead to biased and unreliable models. Models trained on incomplete or inaccurate data may not generalize well. C) Incorrect : While visualizing missing data can be informative, cleaning the data should come first before any further analysis or visualization. D) Incorrect : Handling outliers should come after dealing with missing data. Outliers can distort data distributions, but missing values need to be resolved first to ensure proper data integrity. E) Incorrect : Interpretation and business recommendations should only be made after ensuring the data is clean and ready for analysis. Premature interpretation can lead to faulty conclusions.