MLS-C01 Question 66
Select 3You are working on a machine learning project to predict customer churn and have received a dataset containing numerical, categorical, and missing values. Before building your model, you conduct exploratory data analysis (EDA). Which of the following actions are best practices for addressing the dataset's issues during EDA?
- A
Analyze the distribution of numerical features to identify skewness or outliers.
- B
Drop all rows containing missing values to ensure the dataset is complete.
- C
Use correlation analysis to identify highly correlated features that may cause multicollinearity issues.
- D
Convert categorical features into numerical representations using one-hot encoding or label encoding.
- E
Randomly shuffle the dataset before performing exploratory data analysis to ensure unbiased inspection.
Show answer and explanation
Correct answers: A, C, D
Explanation
EDA is a critical step in the machine learning pipeline to understand the dataset, identify issues, and prepare it for modeling. Best practices include examining numerical feature distributions, handling categorical features, and identifying relationships or patterns such as correlations. Avoiding unnecessary data loss and random operations like shuffling ensures the integrity of insights gained during EDA.
- A. Correct.
Analyzing the distribution of numerical features is a critical EDA step to identify skewness or outliers, which may affect model performance. Appropriate transformations or outlier handling can be applied later.
- B. Incorrect.
Dropping all rows with missing values is usually not recommended because it can lead to data loss, especially in large datasets. Imputation or other techniques are generally preferred.
- C. Correct.
Correlation analysis is a vital EDA step to detect highly correlated features, as they can introduce multicollinearity, negatively impacting model interpretability and performance.
- D. Correct.
Converting categorical features into numerical representations is a key preprocessing step for most machine learning algorithms, and it is a best practice to consider this during EDA.
- E. Incorrect.
Randomly shuffling the dataset is unnecessary during the EDA phase because it does not contribute to understanding the data's structure, issues, or relationships.