Univariate Analysis - Understanding Each Feature

This article discusses the concept of univariate analysis, which involves examining each feature in a dataset individually. The author uses the analogy of a fruit inspector to explain the process of looking at the distribution, skewness, and outliers of numeric features like Age and Fare.

Details

The article explains that univariate analysis is the first step in exploratory data analysis, where you examine each feature in the dataset independently. The author uses the analogy of a fruit inspector checking each piece of fruit individually before making buying decisions. For numeric features like Age and Fare, the author recommends starting with a histogram to understand the shape of the data. Key things to look for are whether the distribution is symmetric (mean and median close), skewed (long right or left tail), bimodal (two peaks), or has outliers. Skewness can be addressed through log or square root transformations, while bimodal distributions may indicate the need to split the data into subgroups. The author provides a table summarizing these patterns and the appropriate actions to take.