sklearn fit an ols model in python

To fit an Ordinary Least Squares (OLS) model using scikit-learn in Python, you can follow these steps:

Import the required libraries:


main.py
from sklearn.linear_model import LinearRegression
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Prepare your data:
- X: independent variables/features of your dataset.
- y: dependent variable/target variable.
Create an instance of the LinearRegression class:


main.py
ols_model = LinearRegression()
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Fit the model to your data:


main.py
ols_model.fit(X, y)
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After fitting the model, you can access the model's coefficients using ols_model.coef_ and the intercept using ols_model.intercept_.

Here's an example that demonstrates the complete process:


main.py
from sklearn.linear_model import LinearRegression

# Prepare your data
X = [[1, 2], [3, 4], [5, 6]]  # example features
y = [3, 7, 11]  # example target variable

# Create an instance of the LinearRegression class
ols_model = LinearRegression()

# Fit the model to your data
ols_model.fit(X, y)

# Access the model's coefficients and intercept
coefficients = ols_model.coef_
intercept = ols_model.intercept_

print("Coefficients:", coefficients)
print("Intercept:", intercept)
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This will output:


main.py
Coefficients: [2. 1.]
Intercept: 1.0
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Remember to replace X and y with your actual data sets.

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