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

• Difficulty Level : Basic
• Last Updated : 26 Sep, 2018

This is Ordinary least squares Linear Regression from sklearn.linear_module.
Syntax :
sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1):

Parameters :

fit_intercept : [boolean, Default is True] Whether to calculate intercept for the model.
normalize : [boolean, Default is False] Normalisation 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.

In the given dataset, R&D Spend, Administration Cost and Marketing Spend of 50 Companies are given along with the profit earned. The target is to prepare ML model which can predict the profit value of a company if the value of its R&D Spend, Administration Cost and Marketing Spend are given. Code: Use of Linear Regression to predict the Companies Profit

 `# Importing the libraries``import` `numpy as np``import` `pandas as pd``  ` `# Importing the dataset``dataset ``=` `pd.read_csv(``'https://media.geeksforgeeks.org/wp-content/uploads/50_Startups.csv'``)``  ` `print` `(``"Dataset.head() \n "``, dataset.head())``  ` `# Input values``x ``=` `dataset.iloc[:, :``-``1``].values``print``(``"\nFirst 10 Input Values : \n"``, x[``0``:``10``, :])`` ` `print` `(``"Dataset Info : \n"``)``print` `(dataset.info())` `# Input values``x ``=` `dataset.iloc[:, :``-``1``].values``print``(``"\nFirst 10 Input Values : \n"``, x[``0``:``10``, :])``  ` `  ` `# Output values``y ``=` `dataset.iloc[:, ``3``].values ``y1 ``=` `y``y1 ``=` `y1.reshape(``-``1``, ``1``)``print``(``"\n\nFirst 10 Output true value : \n"``, y1[``0``:``10``, :])` `# Dividing input and output data to train and test data``# 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``)``  ` `# Feature Scaling``# Multilinear regression takes care of Feature Scaling``# So we need not do it manually ``  ` `  ` `# Fitting Multi Linear regression model to training model``from` `sklearn.linear_model ``import` `LinearRegression``regressor ``=` `LinearRegression()``regressor.fit(xtrain, ytrain)``  ` `# predicting the test set results``y_pred ``=` `regressor.predict(xtest)``  ` `y_pred1 ``=` `y_pred``y_pred1 ``=` `y_pred1.reshape(``-``1``,``1``)``  ` `print``(``"\n   RESULT OF LINEAR REGRESSION PREDICTION : "``)``print` `(``"\nFirst 10 Predicted value : \n"``, y_pred1[``0``:``10``, :])` My Personal Notes arrow_drop_up