MLS-C01 Question 108
Select 3You are working on a machine learning project to predict customer churn for a subscription-based service. After collecting the data, you notice that some features have missing values, while others have skewed distributions. Your team wants to analyze and visualize the data to ensure it is ready for model training. Which steps should you take to effectively analyze and visualize the data?
- A
Create a correlation heatmap to identify relationships between numerical features.
- B
Use box plots to identify potential outliers in the data.
- C
Drop all rows containing missing values to simplify the dataset.
- D
Generate histograms to inspect the distribution of numerical features.
- E
Perform one-hot encoding on categorical features before visualizing the data.
Show answer and explanation
Correct answers: A, B, D
Explanation
To analyze and visualize data for machine learning, it is important to use techniques that provide insights into relationships (e.g., correlation heatmaps), identify data issues (e.g., box plots for outliers), and understand feature distributions (e.g., histograms). These steps help in understanding the data and preparing it for preprocessing and model training. Dropping rows with missing values or performing one-hot encoding without justification can harm the dataset or add unnecessary complexity at this stage.
- A. Correct.
Creating a correlation heatmap is a common practice to analyze relationships between numerical features. It helps identify multicollinearity and features that may not add much predictive value.
- B. Correct.
Box plots are used to visualize the presence of outliers in the data, which can significantly impact machine learning models and may require preprocessing.
- C. Incorrect.
Dropping all rows with missing values is not recommended as it may lead to significant data loss. Alternative strategies like imputation are preferred.
- D. Correct.
Histograms are effective for visualizing the distribution of numerical features, allowing you to identify skewness and decide on potential transformations.
- E. Incorrect.
One-hot encoding is a preprocessing step for categorical features, but it is not necessary before analyzing or visualizing the raw data.