Kernel Density Estimation in R | Non-Parametric estimation | Probability Density Function|Statistics

#kde #kerneldensityestimation #nonparametricstatistics #econometrics #machinelearning #datascience
Kernel density estimation is a non parametric way to estimate the probability density function of a random variable and it is also used for data smoothing.
Join this channel to get access to perks:
/ @analyticsuniversity
#finance #machinelearning #datascience
For courses on Credit risk modelling, Market Risk Analytics, Marketing Analytics, Supply chain Analytics and Data Science/ML projects contact [e-mail]
For Study Packs : analyticunivers...
Complete Data Science Course : bit.ly/34Sucmb
Access All Coursera Plus courses @ $400 : bit.ly/2ZL51Dd
Discounted courses on Udemy (for $11): bit.ly/2LYU6hp
Free access to Skillshare: bit.ly/2thklJu
Coursera :
Data Science : bit.ly/37nABr6
Data Science Python : bit.ly/2ZK5oMm
Recommended Data Science Books on Amazon :
Python for Data Science: geni.us/Python...
R for Data Science : geni.us/DataSc...
Machine Learning using Tensorflow: geni.us/MLinTe...
Data Science from Scratch: geni.us/DataSc...
Python programming: geni.us/LearnP...
Artificial Inteligence: geni.us/LearnAI
Data Vizualization : geni.us/DataViz

[@oshinleygarcia1559]

can you tell me what is the measure of this bandwith that you calculate? 2.47 is cm?

[@battarmoh9539]

how to construct a kernel pdf from a given data using the kde package?