1 00:00:00,600 --> 00:00:00,900 All right. 2 00:00:00,900 --> 00:00:02,600 So that's a really bad prediction. 3 00:00:02,600 --> 00:00:06,900 So now we're going to get the good prediction which we can clearly see here. 4 00:00:06,933 --> 00:00:08,400 You know that's 6.5. 5 00:00:08,400 --> 00:00:10,600 And we'll get a prediction around here 6 00:00:10,600 --> 00:00:14,166 which will correspond indeed to the salary that this person mentioned. 7 00:00:14,533 --> 00:00:15,600 So let's do this. 8 00:00:15,600 --> 00:00:18,366 Let's create a new code cell here. 9 00:00:18,366 --> 00:00:22,100 And to get that prediction we're going to do it efficiently 10 00:00:22,100 --> 00:00:26,166 I'm going to copy this and paste here. 11 00:00:26,400 --> 00:00:29,633 And now once again I would like you to press pause on this video 12 00:00:29,766 --> 00:00:32,633 and figure out what we have to replace here to indeed 13 00:00:32,633 --> 00:00:38,366 get the predicted salary resulting off our polynomial regression model. 14 00:00:38,400 --> 00:00:41,400 So please press pause and then I'll tell you the solution right away. 15 00:00:42,766 --> 00:00:44,433 Okay. Let's do this. 16 00:00:44,433 --> 00:00:44,700 All right. 17 00:00:44,700 --> 00:00:45,100 So first 18 00:00:45,100 --> 00:00:48,200 the obvious thing you had to change is of course the name of your regressor. 19 00:00:48,200 --> 00:00:52,800 Because for polynomial regression we named our regressor linear two. 20 00:00:53,200 --> 00:00:55,733 And then executives same thing as before. 21 00:00:55,733 --> 00:00:59,433 Here we can't input that single position level. 22 00:00:59,433 --> 00:01:02,433 You know coming from the matrix of single feature. 23 00:01:02,633 --> 00:01:07,800 But instead well we have to exactly input, you know, the features of this equation 24 00:01:08,000 --> 00:01:13,766 where x1 is equal to 6.5 and therefore what we need to input as features 25 00:01:13,966 --> 00:01:17,733 is this array composed of the following features values 26 00:01:17,833 --> 00:01:21,900 6.5 for x1, then 6.5 squared for x1 squared, 27 00:01:22,133 --> 00:01:26,400 then 6.5 at the power of three and 6.5 at the power of four. 28 00:01:26,400 --> 00:01:30,333 Because now we have built a polynomial regression model with degree four. 29 00:01:30,600 --> 00:01:31,133 All right. 30 00:01:31,133 --> 00:01:33,833 So here the exact thing that we have to predict. 31 00:01:33,833 --> 00:01:37,900 And we can actually input that efficiently by, you know, 32 00:01:38,100 --> 00:01:42,300 taking exactly what we input here in the predict method, 33 00:01:42,633 --> 00:01:46,366 that fully read object from which we call the fit transform method 34 00:01:46,500 --> 00:01:51,033 to transform this matrix of single feature X into this 35 00:01:51,033 --> 00:01:54,500 matrix composed of the several features at the different powers. 36 00:01:54,500 --> 00:01:58,200 And here, well, of course, the feature value 37 00:01:58,200 --> 00:02:03,100 that we will input inside this fit transfer method will not be x, 38 00:02:03,100 --> 00:02:07,633 but not 6.5 directly like you know before. 39 00:02:07,800 --> 00:02:13,800 But once again, a 2D array containing the value of 6.5 to cell of 6.5. 40 00:02:14,033 --> 00:02:15,900 And so that's exactly the same as before. 41 00:02:15,900 --> 00:02:19,866 We need to enter this 6.5 value inside an array. 42 00:02:20,166 --> 00:02:22,166 You know just before we had x. 43 00:02:22,166 --> 00:02:23,500 Here x is an array. 44 00:02:23,500 --> 00:02:27,000 And now we have to input 6.5 in a 2D array just like that. 45 00:02:27,266 --> 00:02:28,600 And now there you go. 46 00:02:28,600 --> 00:02:30,400 We are ready to get r 47 00:02:30,400 --> 00:02:33,533 predicted salary resulting from the polynomial regression model. 48 00:02:34,000 --> 00:02:39,866 So let's find out right away what is that predicted salary and wonderful. 49 00:02:39,866 --> 00:02:43,333 So the predicted salary is 158. 50 00:02:43,333 --> 00:02:48,300 Well actually $159,000 which is super close 51 00:02:48,566 --> 00:02:51,400 to the salary mentioned by this person, 52 00:02:51,400 --> 00:02:54,466 you know, as the previous salary earners in the previous company. 53 00:02:54,733 --> 00:02:59,766 So now we can be 100% confident to hire this person because not only 54 00:02:59,766 --> 00:03:03,766 it is a good fit for the job, but also it is a very honest person. 55 00:03:04,133 --> 00:03:07,200 And we figured that out with polynomial regression. 56 00:03:07,666 --> 00:03:09,000 So congratulations. 57 00:03:09,000 --> 00:03:10,433 We also have this case study. 58 00:03:10,433 --> 00:03:11,833 Not only we solved this case, 59 00:03:11,833 --> 00:03:15,433 but you now know how to build a polynomial regression model. 60 00:03:15,433 --> 00:03:17,500 And you added that in your toolkit. 61 00:03:17,500 --> 00:03:20,266 This is your first nonlinear regression model. 62 00:03:20,266 --> 00:03:21,700 Congratulations for that. 63 00:03:21,700 --> 00:03:25,933 Now we're going to build three other ones starting with the SVR support 64 00:03:25,933 --> 00:03:27,233 vector for regression. 65 00:03:27,233 --> 00:03:29,966 And that's what we'll do in the next section. 66 00:03:29,966 --> 00:03:30,766 Now for those of you 67 00:03:30,766 --> 00:03:35,100 who also want to study R well you'll find that same implementation. 68 00:03:35,100 --> 00:03:37,266 But in R of course in the next section. 69 00:03:37,266 --> 00:03:38,933 And for those of you who only want to learn 70 00:03:38,933 --> 00:03:41,133 Python will join me in this section after that 71 00:03:41,133 --> 00:03:45,500 to first learn about SVR with Kirill and then implement the SVR model 72 00:03:45,500 --> 00:03:48,233 with me, which will be, by the way, on the same data 73 00:03:48,233 --> 00:03:51,233 set with the same case study, so that we can actually compare 74 00:03:51,300 --> 00:03:54,300 the different results of our non-linear regression models. 75 00:03:54,533 --> 00:03:56,933 So I can't wait to see you in these next sections. 76 00:03:56,933 --> 00:03:58,800 And until then, enjoy machine learning.