This is Ordinary least squares Linear Regression from sklearn.linear_module.
sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1):
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.
To download dataset click here.
Code: Use of Linear Regression to predict the Companies Profit
- Reading Python File-Like Objects from C | Python
- Important differences between Python 2.x and Python 3.x with examples
- Python | Add Logging to Python Libraries
- Python | Sort Python Dictionaries by Key or Value
- Python | Add Logging to a Python Script
- Python | Set 4 (Dictionary, Keywords in Python)
- set add() in python
- Python Set | pop()
- SQL using Python | Set 1
- Any & All in Python
- Use of min() and max() in Python
- chr() in Python
- Python | a += b is not always a = a + b
- SHA in Python
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.