freeCodeCamp/curriculum/challenges/english/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	Sea Level Predictor	10	462370	sea-level-predictor

--description--

You will be working on this project with our Replit starter code.

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)
• Learn Python Video Course (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.

Development

For development, you can use main.py to test your functions. Click the "run" button and main.py will run.

Testing

We imported the tests from test_module.py to main.py for your convenience. The tests will run automatically whenever you hit the "run" button.

Submitting

Copy your project's URL and submit it to 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--

It should pass all Python tests.

--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.