Linear Model in Oceanography

Time-series data provides a context in which the "slope" of the linear model represents a "rate-of-change".

In this exercise, you will use measurements of sea level change from 1970 to 2010, build a linear model of that changing sea level and use it to make a prediction about the future sea level rise.

This exercise is part of the course

Introduction to Linear Modeling in Python

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Exercise instructions

Import and use LinearRegression(fit_intercept=True) to initialize a linear model.
Pass in the pre-loaded and reshaped years and levels data into model.fit() to fit the model.
Use model.predict() to predict a single future_level for future_year = 2100 and print() the result.
Use model.predict() to forecast many levels_forecast and plot the result with the pre-defined plot_data_and_forecast().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import LinearRegression class, build a model, fit to the data
from sklearn.linear_model import ____
model = ____(fit_intercept=True)
model.fit(years, levels)

# Use model to make a prediction for one year, 2100
future_year = np.array(2100).reshape(1, -1)
future_level = model.predict(____)
print("Prediction: year = {}, level = {:.02f}".format(future_year, future_level[0,0]))

# Use model to predict for many years, and over-plot with measured data
years_forecast = np.linspace(1970, 2100, 131).reshape(-1, 1)
levels_forecast = model.predict(____)
fig = plot_data_and_forecast(years, levels, ____, ____)

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