A Practical Approach to Timeseries Forecasting Using Python - Visualize the Data

The video tutorial covers using matplotlib to visualize COVID-19 data, focusing on confirmed cases and deaths. It explains how to set up plots with specific attributes like figure size, DPI, and colors. The tutorial also demonstrates plotting multiple data columns in a single line plot to analyze relationships between confirmed, cured, and death cases. The video concludes with a brief mention of using area plots for further analysis in a subsequent video.

Which library is used for visualizing data in this tutorial?

• Matplotlib

• Pandas

• Seaborn

• NumPy

What is the first command used to start plotting in matplotlib?

• PLT.figure

• PLT.show

• PLT.plot

• PLT.title

What is the purpose of setting 'DPI' in PLT.figure?

• To define the plot title

• To determine the number of pixels

• To specify the plot size

• To set the color of the plot

What is the maximum number of deaths visualized in the plot?

• 10000

• 15000

• 20000

• 25000

What type of plot is used to visualize the relationship between multiple columns?

• Bar plot

• Scatter plot

• Pie chart

• Line plot

Which axis represents the years in the combined plot?

• X-axis

• Y-axis

• Z-axis

• None

Which columns are visualized in the combined line plot?

• Cured, Active, Confirmed

• Deaths, Active, Recovered

• Confirmed, Deaths, Cured

• Confirmed, Recovered, Active