Create Kernel Density Estimation.md

https://en.wikipedia.org/wiki/Kernel_density_estimation

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Time Series Visualization of CSV Data Extracted from PCAP Files/Kernel Density Estimation (KDE)/Kernel Density Estimation.md

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+# Kernel Density Estimation (KDE)
+- Description: A non-parametric method that uses kernel functions (e.g., Gaussian) to smooth the data and estimate the PDF.
+- Implementation: Libraries like scipy.stats.gaussian_kde or seaborn.kdeplot in Python.
+- Pros: Smooth representation, handles irregular data distributions.
+- Cons: Requires tuning the bandwidth parameter, may struggle with highly autocorrelated data.