Title: Linear Regression in Python – Real Python
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Description: Use Python to build a linear model for regression, fit data with scikit-learn, read R2, and make predictions in minutes.
Open Graph Description: Use Python to build a linear model for regression, fit data with scikit-learn, read R2, and make predictions in minutes.
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"text": "Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. The simplest form, simple linear regression, involves one independent variable. The method of ordinary least squares is used to determine the best-fitting line by minimizing the sum of squared residuals between the observed and predicted values.
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"text": "Simple linear regression involves a single independent variable and models a relationship between two variables. Multiple linear regression involves two or more independent variables and models a relationship between one dependent variable and several independent variables. The equations and complexity increase with the number of predictors in multiple linear regression.
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"text": "To use scikit-learn for linear regression, follow these steps:
\n\n- Import
LinearRegression from sklearn.linear_model. \n- Prepare your data as NumPy arrays.
\n- Create an instance of the
LinearRegression model and fit it using .fit() with your data. \n- Obtain the model parameters with attributes like
.coef_ and .intercept_. \n- Predict new values with
.predict(). \n
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"text": "The formula for linear regression is \ud835\udc66 = \ud835\udefd\u2080 + \ud835\udefd\u2081\ud835\udc65\u2081 + \u22ef + \ud835\udefd\u1d63\ud835\udc65\u1d63 + \ud835\udf00, where \ud835\udc66 is the dependent variable, \ud835\udc65i are the independent variables, \ud835\udefdi are the coefficients, and \ud835\udf00 is the error term. The goal is to find the coefficients that minimize the sum of squared differences between observed and predicted values.
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