Histogram | Scatter and boxplots in Python | Machine Learning

In this tutorial, we will explore three popular types of plots used in machine learning analysis: Histograms, Scatterplots, and Boxplots. We will demonstrate how to create these plots using Python's data visualization libraries, specifically Matplotlib and Seaborn.

First, we will cover Histograms, which are used to visualize the distribution of a single continuous variable. We will show you how to create a histogram in Python, including how to choose the number of bins and how to customize the plot to make it more informative.

Next, we will move on to Scatterplots, which are used to visualize the relationship between two continuous variables. We will show you how to create a scatterplot in Python, including how to customize the marker shape, size, and color to make the plot more visually appealing.

Finally, we will cover Boxplots, which are used to visualize the distribution of a continuous variable across different categories. We will show you how to create a boxplot in Python, including how to customize the whiskers, outliers, and colors to make the plot more informative.

By the end of this tutorial, you will have a solid understanding of how to create and customize histograms, scatterplots, and boxplots in Python, which will be useful for analyzing and visualizing data in machine learning applications.

LAB TASK QUESTIONS IN VIDEO:

1. Perform the second example related to histogram and customize the output by using given parameters and the output should be like as given in figure.

2. Write a Python Program to plot a histogram of two datasets given below. G1 = [1, 3, 5, 10, 15, 17, 18, 16, 19, 21,
23, 28, 30, 31, 33, 38, 32, 40, 45,
43, 49, 55, 53, 63, 66, 85, 80, 57,
75, 93, 95]

G2 = [6, 4, 15, 17, 19, 21, 28, 23, 31, 36,
39, 32, 50, 56, 59, 74, 79, 34, 98, 97,
95, 67, 69, 92, 45, 55, 77, 76, 85]


3. Write a Python Program to Increase the size of Scatter Points of any dataset in Matplotlib.


4. Write a Python Program for boxplot of any dataset other then the example performed above and interpret the graph.