ML | sklearn.linear_model.LinearRegression() in Python
Last Updated :
21 Mar, 2024
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
import numpy as np
import pandas as pd
print ( "Dataset.head() \n " , dataset.head())
x = dataset.iloc[:, : - 1 ].values
print ( "\nFirst 10 Input Values : \n" , x[ 0 : 10 , :])
|
print ( "Dataset Info : \n" )
print (dataset.info())
|
x = dataset.iloc[:, : - 1 ].values
print ( "\nFirst 10 Input Values : \n" , x[ 0 : 10 , :])
y = dataset.iloc[:, 3 ].values
y1 = y
y1 = y1.reshape( - 1 , 1 )
print ( "\n\nFirst 10 Output true value : \n" , y1[ 0 : 10 , :])
|
from sklearn.cross_validation import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2 ,
random_state = 0 )
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(xtrain, ytrain)
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 , :])
|
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...