MLS-C01 Question 105
Select 4You are working on a machine learning project to predict customer churn for a subscription-based service. The dataset contains customer demographics, subscription details, and service usage history. After initial exploration, you notice that the dataset has missing values, categorical variables, and some numerical features with skewed distributions. Which of the following actions should you perform to properly analyze and visualize the data for machine learning?
- A
Impute missing values using mean, median, or mode based on feature type and distribution.
- B
Use a boxplot to identify outliers in numerical features and handle them.
- C
One-hot encode categorical variables to prepare them for analysis and visualization.
- D
Log-transform skewed numerical features to normalize their distributions.
- E
Directly train a machine learning model on the raw dataset without any preprocessing.
Show answer and explanation
Correct answers: A, B, C, D
Explanation
Properly analyzing and visualizing data for machine learning involves handling missing values, identifying and addressing outliers, transforming skewed data, and encoding categorical variables. These steps ensure the dataset is clean and prepared for meaningful insights and model training. Skipping preprocessing can lead to poor model performance and misleading visualizations.
- A. Correct.
Imputing missing values is essential for ensuring that your dataset is complete for analysis and visualization. Choosing the appropriate imputation method (mean, median, or mode) depends on the feature type and its distribution.
- B. Correct.
Using boxplots to identify outliers is a common exploratory data analysis (EDA) technique. Handling outliers can improve the quality of analysis and model performance.
- C. Correct.
One-hot encoding is necessary to convert categorical variables into a numerical format, which is required for visualizations and machine learning algorithms.
- D. Correct.
Log-transforming skewed numerical features helps normalize their distributions, improving interpretability in visualizations and reducing potential bias in modeling.
- E. Incorrect.
Directly training a machine learning model on raw data without preprocessing is not recommended, as raw datasets often contain missing values, categorical variables, and other issues that hinder model performance.