## Overview:

- In statistics,
**Kernel Density Estimation**is a non-parametric technique that calculates and plots the probability distribution(the probability density) of a continuous random variable. i.e., The calculation does not assume the underlying data to be following the assumptions of a normal distribution or any distribution. - In simple terms,
**Kernel Density Estimate**is like a smoothened counterpart of a**histogram**without the line of**histogram intervals**and their**end-points**. - Such a smoothened curve for the probability density of a given data is obtained by drawing individual estimates for the data points and summing them up to produce the final contour.
- The bandwidth 'h' used in the estimation plays a role in the level of smoothness of the estimated curve. The lower the 'h' - more closer to the data and more spiky the curve is. When the value of 'h' is higher the resultant curve is over smoothend.

## KDE Plot in seaborn:

- Probablity Density Estimates can be drawn using any one of the kernel functions - as passed to the parameter
**"kernel"**of the**seaborn.kdeplot()**function. By default, a Guassian kernel as denoted by the value "gau" is used. The kernels supported and the corresponding values are given here.

Name of the kernel function |
Value of the parameter |

Guassian kernel | "gau" |

Cosine | "cos" |

Biweight | "bi" |

Triweight | "trw" |

Triangular | "tri" |

Epanechnikov | "epa" |

- In seaborn the bandwidth of the KDE plot is controlled through the function parameter
**"bw"**.

Example:

# Example Python program that draws a KDE plot # Generate data points # Use gaussian kernel to plot the Kernel Density Estimation |

Output: