Which method is best for visualizing data relationships before modeling?

The method of visualizing data relationships before modeling is crucial for understanding the underlying patterns, distributions, and potential correlations in the data. Each of the proposed visualization techniques plays a unique role in this process.

Creating scatter plots is particularly effective for visualizing the relationship between two numerical variables. By plotting individual data points on a two-dimensional grid, scatter plots can reveal trends, associations, or clusters that may exist between the variables. This visual representation can help identify whether a linear relationship exists or if there are any outliers that may impact the modeling process.

Using correlation matrices provides a comprehensive overview of the relationships among multiple variables at once. This matrix encapsulates the correlation coefficients between pairs of variables, allowing for a quick assessment of which variables are positively or negatively correlated. This insight helps in selecting features that might be informative for the modeling process, potentially reducing dimensionality and improving the model's efficiency by focusing on relevant relationships.

Employing box plots is another effective means to visualize the distribution of numerical data across different categories. Box plots summarize key statistics, such as the median, quartiles, and potential outliers within the data. By providing a clear view of the data's spread and central tendency, box plots can help identify patterns related to categorical variables and highlight any