 # ML | sklearn.linear_model.LinearRegression () in Python

Parameters:

fit_intercept: [boolean, Default is True] Whether to calculate intercept for the model.
normalize: [boolean, Default is False] Normalization before regression.
copy_X: [boolean, Default is True] If true, make a copy of X else overwritten .
n_jobs: [int, Default is 1] If -1 all CPU`s are used. This will speedup the working for large datasets to process.

This dataset shows R&D costs, administration and marketing costs of 50 companies, and profits. The goal is to prepare an ML model that can predict the value of a company`s profit when given values ​​for R&D costs, administration costs, and marketing costs.

Code: using linear regression to predict company profit

 ` # Importing Libraries ` ` import ` ` numpy as np ` ` import ` ` pandas as pd `   ` # Import dataset < / code> ```` dataset = pd.read_csv ( ` https://media.python.engineering/wp-content/uploads/50_Startups.csv ` )   print ( "Dataset.head ()" , dataset.head ())   # Input Values ​​ x = dataset.iloc [:,: - 1 ]. values ​​ print ( " First 10 Input Values: " , x [ 0 : 10 ,:])   ``` ` print ` ` (` ` "Dataset Info: "` `) ` ` print ` ` (dataset .info ()) ` ` # Input Values ​​` ` x ` ` = dataset.iloc [:,: - 1 ]. values ​​```` print ( " First 10 Input Values: " , x [ 0 : 10 ,:])     # Output values ​​ y = dataset.iloc [:, 3 ]. Values  y1 = y y1 = y1.reshape ( - 1 < code class = "plain">, 1 ) print ( " First 10 Output true value: " , y1 [ 0 : 10 ,:]) ``` ` # Separate input and output for training and data validation ` ` # Training: testing = 80:20 ` ` from ` ` sklearn.cross_validation ` ` import ` ` train_test_split ` ` xtrain , xtest, ytrain, ytest ` = ` train_test_split (x, y, test_size ` ` = ` ` 0.2 ` `, ` ` ` ` random_state ` ` = ` ` 0 ` `) `   ` # Function scaling ` ` # Polylinear regression takes care of object scaling ` ` # So we don`t have to do it manually `     ` # Fitting the multilinear regression model to the learning model ` ` from ` ` sklearn.linear_model ` ` import ` ` LinearRegression `   ` regressor ` ` = ` ` LinearRegression () ` ` regressor.fit (xtrain, ytrain) `   ` # predicting test case results ` ` y_pred ` ` = ` ` regressor.predict (xtest) ` ` `  ` y_pred1 ` ` = ` ` y_pred ` ` y_pred1 ` ` = ` ` y_pred1.reshape (` ` - ` ` 1 ` `, ` ` 1 ` `) `   ` print ` ` (` ` " RESULT OF LIN EAR REGRESSION PREDICTION: "` `) ` ` print ` ` (` ` "First 10 Predicted value:" ` `, y_pred1 [` ` 0 ` `: ` ` 10 ` `,:]) ` 