Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Distribution of the Data

The video tutorial explains how to plot scatter points to visualize data distribution. It covers setting up graph labels for area and price, using the scatter method to plot data points, and customizing the plot with color and marker options. The tutorial concludes by interpreting the graph, suggesting that the data distribution is suitable for a linear regression model as the points trend upwards and to the right.

What is the purpose of plotting scatter points in data analysis?

• To determine the exact values of data points

• To eliminate outliers from the data

• To understand the distribution of data points

• To calculate the average of data points

Which labels are set for the X and Y axes in the scatter plot?

• Length in meters and price in U.S. dollars

• Volume in cubic feet and cost in U.S. dollars

• Area in square feet and price in U.S. dollars

• Area in square meters and price in Euros

What does the scatter method do in the context of plotting?

• It calculates the average of the data points

• It changes the color of the graph background

• It draws the XY coordinate points on the graph

• It removes outliers from the data set

Which optional arguments can be used with the scatter method?

• Color and marker

• Size and shape

• Width and height

• Font and alignment

What conclusion can be drawn from the scatter plot regarding the data distribution?

• The data is randomly distributed

• The data is suitable for a polynomial regression model

• The data is not suitable for any regression model

• The data is suitable for a linear regression model