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Download this code from https://codegive.com Certainly! Smoothing a probability distribution plot in Python often involves reducing noise or visualizing a smoother representation of the data. One way to achieve this is by using kernel density estimation (KDE). Here's a step-by-step tutorial using Python's matplotlib and seaborn libraries: If you haven't installed matplotlib and seaborn yet, you can install them via pip: Start by importing the necessary libraries: Create or load the data you want to plot. For demonstration purposes, let's create a sample dataset: Create a probability distribution plot using seaborn: This code generates a basic probability distribution plot with the KDE overlay on top of the histogram. To adjust the smoothness of the KDE plot, you can modify parameters like the bandwidth. The bandwidth determines how wide or narrow each kernel is. You can also experiment with other parameters to adjust the smoothness, such as the kernel type (e.g., Gaussian, Epanechnikov, etc.), which can be specified with the kernel parameter. By utilizing the seaborn library's histplot function and adjusting KDE parameters like bandwidth and kernel type, you can create smooth probability distribution plots in Python to visualize data in a more interpretable manner. Adjusting these parameters allows you to tailor the level of smoothness according to your preferences or data characteristics. ChatGPT