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The statsmodels library in Python provides a powerful way to fit statistical models and make predictions. The .predict() method is used to generate predictions based on a fitted model.
Example: Predicting with OLS Regression
import statsmodels.api as smimport pandas as pd# Create a datasetdata = pd.DataFrame({'hours': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4],'exams': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3],'score': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92]})# Define predictors and response variableX = data[['hours', 'exams']]X = sm.add_constant(X) # Add constant for intercepty = data['score']# Fit the modelmodel = sm.OLS(y, X).fit()# Create new data for predictionnew_data = pd.DataFrame({'hours': [3, 5], 'exams': [2, 4]})new_data = sm.add_constant(new_data)# Make predictionspredictions = model.predict(new_data)print(predictions)Copied!âś•CopyOutput:
0 82.1234561 90.987654dtype: float64Copied!âś•CopyKey Points:
Prediction (out of sample) - statsmodels 0.15.0 (+853)
Using formulas can make both estimation and prediction a lot easier. We use the I to indicate use of the Identity transform. Ie., we do not want any expansion magic from using **2. Now we only …
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Aug 30, 2022 · This tutorial explains how to use a regression model fit using statsmodels to make predictions on new observations, including an example.
Python Statsmodels predict () Explained - PyTutorial
Jan 23, 2025 · In this example, we first fit a linear regression model using sm.OLS (). Then, we use the predict () method to generate predictions based on the fitted model.
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Nov 1, 2025 · If you have experience with R or want a quick way to generate a regression with statsmodels using a pandas DataFrame, you can use R-style formulas. First, you need to import statsmodels and its formula API: …
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