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# Linear Regression with Sklearn

Here we initialise and train a Linear Regression model before using the model to make predictions. Finally we find the intercept and cofficients of the model along with analysing the models performance on test data using mean squared error and mean absolute error metrics.

#Import Library from sklearn.linear_model import LinearRegression #Initialise & Fit Model model = LinearRegression() model.fit(X_train, y_train) #Use Model to Make Predictions y_pred = model.predict(X_test) #Get Intercept & Coefficients print(model.intercept_) coef = pd.DataFrame(model.coef_, X_train.columns, columns=['Coef']) #Get MSE & MAE from sklearn.metrics import mean_squared_error, mean_absolute_error print('MSE:',mean_squared_error(y_test,y_pred)) print('MAE:',mean_absolute_error(y_test,y_pred))

By analyseup - Last Updated Jan. 10, 2022, 11:27 p.m.

Gpu - Regression - Catboost

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