freeCodeCamp/curriculum/challenges/ukrainian/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md

id, title, challengeType, forumTopicId, dashedName

id	title	challengeType	forumTopicId	dashedName
5e4f5c4b570f7e3a4949899f	Прогнозування змін рівня моря	10	462370	sea-level-predictor

--description--

Ви будете працювати над цим проєктом з нашим стартовим кодом Replit.

We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:

• Python for Everybody Video Course(14 hours)

• How to Analyze Data with Python Pandas (10 hours)

--instructions--

You will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.

Use the data to complete the following tasks:

• Use Pandas to import the data from epa-sea-level.csv.
• Use matplotlib to create a scatter plot using the Year column as the x-axis and the CSIRO Adjusted Sea Level column as the y-axix.
• Use the linregress function from scipy.stats to get the slope and y-intercept of the line of best fit. Plot the line of best fit over the top of the scatter plot. Make the line go through the year 2050 to predict the sea level rise in 2050.
• Plot a new line of best fit just using the data from year 2000 through the most recent year in the dataset. Make the line also go through the year 2050 to predict the sea level rise in 2050 if the rate of rise continues as it has since the year 2000.
• The x label should be Year, the y label should be Sea Level (inches), and the title should be Rise in Sea Level.

Unit tests are written for you under test_module.py.

The boilerplate also includes commands to save and return the image.

Розробка

Для розробки ви можете використати main.py, щоб протестувати свої функції. Натисніть кнопку «run» і main.py запуститься.

Тестування

Ми перенесли тести з test_module.py в main.py для вашої зручності. Тести запустяться автоматично, коли ви натиснете на кнопку «run».

Надсилання

Скопіюйте URL-адресу свого проєкту та відправте її до freeCodeCamp.

Data Source

Global Average Absolute Sea Level Change, 1880-2014 from the US Environmental Protection Agency using data from CSIRO, 2015; NOAA, 2015.

--hints--

Він повинен пройти усі тести Python.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.