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21 lines
745 B
21 lines
745 B
6 years ago
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#LinearRegression]
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#Import Library
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#Import other necessary libraries like pandas, numpy...
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from sklearn import linear_model
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#Load Train and Test datasets
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#Identify feature and response variable(s) and values must be numeric and numpy arrays
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x_train=input_variables_values_training_datasets
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y_train=target_variables_values_training_datasets
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x_test=input_variables_values_test_datasets
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# Create linear regression object
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linear = linear_model.LinearRegression()
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# Train the model using the training sets and check score
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linear.fit(x_train, y_train)
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linear.score(x_train, y_train)
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#Equation coefficient and Intercept
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print('Coefficient: \n', linear.coef_)
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print('Intercept: \n', linear.intercept_)
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#Predict Output
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predicted= linear.predict(x_test)
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