A Practical Approach to Timeseries Forecasting Using Python - Area Plot

The video tutorial explains how to create and customize an area plot using a data frame. It covers setting plot parameters like kind, stacked option, and figure size. The tutorial also demonstrates adding titles and labels to the plot, and explains the significance of color coding for different data columns such as confirmed, cured, and death cases. It further explores customizing colors for better visualization and concludes with a brief introduction to upcoming topics like autocorrelation and statistical measures.

What type of plot is being discussed in this section?

• Bar plot

• Line plot

• Scatter plot

• Area plot

What is the purpose of setting the figure size in an area plot?

• To determine the color of the plot

• To specify the dimensions of the plot

• To decide the type of plot

• To set the data range

What label is set for the y-axis in the plot?

• Number of COVID cases

• Number of positive cases

• Confirmed cases

• Date

Why is the date used on the x-axis?

• Because it is the most important data

• Because it is set as an index

• Because it is easier to read

• Because it is the default setting

What color is used to represent cured cases in the plot?

• Black

• Green

• Blue

• Red

Which data column is represented by the darkest color in the plot?

• Confirmed cases

• Recovered cases

• Cured cases

• Deaths

Why are the columns 'Confirmed' and 'Cured' placed closer together in the visualization?

• Because they have similar values

• Because they are less important

• Because they are more related to each other

• Because they are the same data