MLA-C01 Question 80
Select 3A company is building a machine learning model to predict customer churn. The dataset includes missing values in some columns, duplicate records, and inconsistent formatting of categorical values. As an AWS Certified Machine Learning Engineer, how should you ensure the data integrity and prepare it for modeling?
- A
Remove duplicate records to avoid data redundancy.
- B
Impute missing values using appropriate statistical techniques or domain knowledge.
- C
Ignore missing values to preserve the dataset's original structure.
- D
Standardize categorical values to maintain consistency.
- E
Split the dataset into training and testing sets before addressing data integrity issues.
Show answer and explanation
Correct answers: A, B, D
Explanation
To ensure data integrity and prepare data for modeling, it is essential to address issues such as duplicate records, missing values, and inconsistent formatting. Removing duplicates prevents redundancy, imputing missing values ensures no information is lost, and standardizing categorical values ensures uniformity. Ignoring missing values or splitting the dataset before addressing data quality issues can lead to unreliable model performance and biased results.
- A. Correct.
Removing duplicate records is a necessary step to ensure data integrity and avoid biased results during model training. This helps maintain the quality of the dataset.
- B. Correct.
Imputing missing values is critical to handling incomplete data and ensures the dataset is usable for modeling. The imputation method should align with the nature of the data.
- C. Incorrect.
Ignoring missing values is not a recommended practice as it could lead to data loss or biased models, especially in datasets with a significant number of missing entries.
- D. Correct.
Standardizing categorical values ensures consistency in the dataset, preventing issues during feature encoding and model training.
- E. Incorrect.
Splitting the dataset into training and testing sets is an important step in the machine learning workflow, but it should be done after ensuring data integrity and preparing the data to avoid introducing biases or inconsistencies.