{"id":1938,"date":"2019-07-22T12:00:00","date_gmt":"2019-07-22T10:00:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/how-to-perform-linear-regression-in-python-and-r-similar-results\/"},"modified":"2026-07-05T03:24:07","modified_gmt":"2026-07-05T01:24:07","slug":"how-to-perform-linear-regression-in-python-and-r-similar-results","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/how-to-perform-linear-regression-in-python-and-r-similar-results\/","title":{"rendered":"How to Perform Linear Regression in Python and R( Similar Results)"},"content":{"rendered":"

In this short lesson, I would teach you how to perform linear regression in Python and R. It’s quite easy. But the interesting thing is that we get similar results.<\/p>\n

So let’s start with Python.<\/p>\n

 <\/p>\n

Linear Regression in Python<\/strong><\/h5>\n

Now the data is given in an excel spreadsheet. This is shown below:<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
x<\/td>\ny<\/td>\n<\/tr>\n
1<\/td>\n3<\/td>\n<\/tr>\n
10<\/td>\n20<\/td>\n<\/tr>\n
20<\/td>\n90<\/td>\n<\/tr>\n
40<\/td>\n110<\/td>\n<\/tr>\n
60<\/td>\n130<\/td>\n<\/tr>\n
71<\/td>\n170<\/td>\n<\/tr>\n
80<\/td>\n150<\/td>\n<\/tr>\n
95<\/td>\n220<\/td>\n<\/tr>\n
120<\/td>\n260<\/td>\n<\/tr>\n
125<\/td>\n300<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

You can watch the video<\/a> to see how to easily transfer this data to Jupyter Notebook.<\/p>\n

The Python code is shown below. I have also included comments in the code to make it easily readable.<\/p>\n

#Import the necessary modules<\/span>\r\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\r\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\r\nfrom<\/span> sklearn.linear_model<\/span> import<\/span> LinearRegression\r\n\r\n#Create a numpy array using the given dataset<\/span>\r\nx =<\/span> np.<\/span>array([1<\/span>,10<\/span>,20<\/span>,40<\/span>,60<\/span>,71<\/span>,80<\/span>,95<\/span>,120<\/span>,125<\/span>])\r\ny =<\/span> np.<\/span>array([3<\/span>,20<\/span>,90<\/span>,110<\/span>,130<\/span>,170<\/span>,150<\/span>,220<\/span>,260<\/span>,300<\/span>])\r\n\r\n\r\n#Create a LinearRegression variable<\/span>\r\nlinmod =<\/span> LinearRegression()\r\nx =<\/span> x.<\/span>reshape(-<\/span>1<\/span>, 1<\/span>)\r\nlinmod.<\/span>fit(x,y)\r\ny_pred =<\/span> linmod.<\/span>predict(x)\r\n\r\n\r\n#First do a scatterplot, then fit a regression line<\/span>\r\nplt.<\/span>scatter(x,y)\r\nplt.<\/span>plot(x, y_pred, color=<\/span>'red'<\/span>)\r\nplt.<\/span>show()\r\n<\/pre>\n
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<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
<\/p>\n
print<\/span>(linmod.<\/span>intercept_)\r\n12.054491647432457<\/span>\r\n\r\nprint<\/span>(linmod.<\/span>coef_)\r\n[2.14221075<\/span>]\r\n<\/pre>\n<\/div>\n<\/div>\n
 <\/p>\n
Just in case you miss out anything, the Jupyter\u00a0 Notebook window is shown below.<\/p>\n
\"Linear
Linear Regression in Python<\/figcaption><\/figure>\n
 <\/p>\n
Linear Regression in R<\/strong><\/h5>\n
Let’s now examine how to perform Linear Regression in R. I’m sure you know you need to have RStudio installed.<\/p>\n
The whole code is given below.<\/p>\n
<\/p>\n
#First copy the data to the clipboard<\/span>\r\n\r\nmydata = read.table(file=\"clipboard\", sep=\"\\t\", header = TRUE)<\/span>\r\nmydata<\/span>\r\nplot(mydata$y ~ mydata$x)<\/span>\r\n\r\nlinmod = lm(mydata$y ~ mydata$x)<\/span>\r\nabline(linmod, col=\"red\")<\/span>\r\n\r\nlinmod<\/span>\r\n<\/pre>\n
 <\/p>\n
The graph generated by R is shown below<\/p>\n
\"Linear_Regression\"
Linear_Regression in R<\/figcaption><\/figure>\n
 <\/p>\n
The RStudio window is shown below:<\/p>\n
\"RStudio<\/p>\n
I would recommend you watch the video lesson below:<\/p>\n