Kernel Density Estimation

Kernel Density Estimation (KDE) is a statistical technique used to estimate the probability density function of a random variable. Instead of relying on a fixed distribution, KDE places a smooth, bell-shaped curve (called a kernel) over each data point in a dataset. By adding these curves together, KDE creates a continuous curve that represents the likelihood of different values occurring in the data. This method helps visualize how data is distributed, making it easier to identify patterns, peaks, and areas of concentration without being limited by rigid assumptions of traditional statistical methods.