Challenge: Predict Ozone Levels
In environmental science, predicting air quality indicators such as ozone levels is crucial for understanding pollution dynamics and informing public health decisions. You will use a simple linear regression model to predict ozone levels based on temperature data, a common approach for exploring how weather conditions relate to air pollution.
Begin by importing the necessary libraries and preparing your data. You have a small dataset of daily temperature and ozone measurements, which allows you to practice building and evaluating a predictive model.
Swipe to start coding
Fit a linear regression model using scikit-learn to predict ozone levels from temperature, using this DataFrame:
- Use the provided pandas DataFrame with columns
temperatureandozone. - Fit a linear regression model (
LinearRegression) to predictozonefromtemperature. - Store the fitted model as
model. - Predict ozone values for the input data and store them in
y_pred. - Calculate and print the mean squared error (MSE) and R² score of the predictions.
- Plot a scatter plot of the data and overlay the regression line.
The DataFrame is:
import pandas as pd
df = pd.DataFrame({
"temperature": [22, 25, 27, 23, 28, 30, 26, 29, 31, 24, 32, 33, 21, 20, 19],
"ozone": [34, 44, 49, 37, 51, 60, 46, 55, 62, 39, 65, 67, 30, 28, 25]
})
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What are the necessary libraries I need to import for this task?
Can you show me how to prepare the temperature and ozone data for modeling?
How do I load and view the sample dataset?
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In environmental science, predicting air quality indicators such as ozone levels is crucial for understanding pollution dynamics and informing public health decisions. You will use a simple linear regression model to predict ozone levels based on temperature data, a common approach for exploring how weather conditions relate to air pollution.
Begin by importing the necessary libraries and preparing your data. You have a small dataset of daily temperature and ozone measurements, which allows you to practice building and evaluating a predictive model.
Swipe to start coding
Fit a linear regression model using scikit-learn to predict ozone levels from temperature, using this DataFrame:
- Use the provided pandas DataFrame with columns
temperatureandozone. - Fit a linear regression model (
LinearRegression) to predictozonefromtemperature. - Store the fitted model as
model. - Predict ozone values for the input data and store them in
y_pred. - Calculate and print the mean squared error (MSE) and R² score of the predictions.
- Plot a scatter plot of the data and overlay the regression line.
The DataFrame is:
import pandas as pd
df = pd.DataFrame({
"temperature": [22, 25, 27, 23, 28, 30, 26, 29, 31, 24, 32, 33, 21, 20, 19],
"ozone": [34, 44, 49, 37, 51, 60, 46, 55, 62, 39, 65, 67, 30, 28, 25]
})
Рішення
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