In Seaborn, KDE stands for Kernel Density Estimation. KDE is a non-parametric method for estimating the probability density function of a random variable. It is used to visualize the probability density function of a continuous variable in a dataset.
Seaborn provides a convenient way to create KDE plots through the sns.kdeplot() function, which can be used to create a smooth representation of the distribution of a dataset. The KDE plot is essentially a smoothed histogram, where the smoothness is controlled by the bandwidth parameter. The bandwidth parameter controls the degree of smoothing and can be adjusted to produce different levels of smoothness in the KDE plot.
KDE plots are useful for visualizing the distribution of a continuous variable, and can be used to identify features such as modes, skewness, and tails in the distribution. They are commonly used in data exploration and analysis to gain insights into the underlying distribution of the data.