People often follow Probabilities, Deviations and Densities that play a key role in ML modeling.

Author(s): Chandra Prakash Bathula

Originally published on Towards AI.

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Part 2: First, make sure you follow these steps, before applying Machine Learning algorithms!

We have discussed about basic plotting earlier, now let’s dive into some real time insights.

We can see the points scatter, but can we work around to get the density of points at a specific region? If there are any, will it be possible for us to know the confidence of the count?

To answer these questions we need to delve deep into concepts like Histograms, PDF & CDF.

Everything that we have discussed previously is about 2D and 3D. We realized that we could not do visual 4D and developed pair plots.

What about 1-D? Why don’t we try a 1-D scatter plot?

A 1-D scatter plot can be plotted using a single variable on the x-axis to make the y-axis zeroes.

By creating data frames based on species in the plot, we have the X-axis as the Petal Length and the Y-axis as nothing.

import numpy as npimport matplotlib.pyplot as plt# Assuming iris is a pandas DataFrameiris_setosa = iris.loc[iris["species"] == "setosa"]iris_virginica = iris.loc[iris["species"] == "virginica"]iris_versicolor = iris.loc[iris["species"] == "versicolor"]# Plotting petal lengths for each species using… Read the full blog for free on Medium.

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Published via Towards AI