Kernel smoothing is a statistical technique used to estimate the probability density function of a random variable. It works by placing a smooth curve, called a kernel, over each data point and then averaging these curves to create a continuous estimate. This method helps to reveal underlying patterns in data without making strong assumptions about its distribution. The choice of kernel and its bandwidth, which controls the width of the smoothing, significantly affects the results. Common kernels include Gaussian and Epanechnikov. Kernel smoothing is widely used in data analysis, machine learning, and signal processing to enhance data visualization and interpretation.