Data exploration uses statistical and visualization techniques to understand dataset characteristics and highlight key features. Univariate analysis summarizes each variable individually using measures such as mean, median, mode, variance, standard deviation, and range to describe data distribution and spread. Correlation, a multivariate analysis technique, measures the strength and direction of relationships between variables. It helps determine how changes in one feature affect another—for example, larger lot sizes may correlate with higher house prices—enabling better insights for modeling and feature selection.