1 00:00:01,080 --> 00:00:07,600 Now let's learn how to create a logistic regression with multiple independent variables and by. 2 00:00:09,030 --> 00:00:14,850 We will use the same template and the same code which we use for creating logistic regression with a 3 00:00:14,850 --> 00:00:19,140 single variable will just change our X and Y variables. 4 00:00:19,980 --> 00:00:22,920 And we will use this same port. 5 00:00:23,670 --> 00:00:29,880 Remember, if you are reopening your python, you need to import all the libraries that we imported 6 00:00:29,940 --> 00:00:30,480 earlier. 7 00:00:32,850 --> 00:00:41,670 And you just have to give values of your X and Y to create this model similar to last time. 8 00:00:42,020 --> 00:00:46,610 We'll use two libraries for space, a skillern and then start smaller. 9 00:00:49,380 --> 00:00:56,940 So this thing, our X, which is independent variables, should contain all the variables except sold 10 00:00:56,940 --> 00:00:59,340 variable from our data frame. 11 00:01:02,340 --> 00:01:08,760 Since we do not want this sorry variable, we can remove this from our data frame. 12 00:01:08,790 --> 00:01:11,070 So we'll be at door lock. 13 00:01:16,470 --> 00:01:18,540 And then we will put the colon. 14 00:01:18,990 --> 00:01:22,570 Since we want all rules, we are just put in colon. 15 00:01:22,620 --> 00:01:23,730 This means all rows. 16 00:01:24,630 --> 00:01:30,040 Then we'll put comma the argument before this comma. 17 00:01:30,150 --> 00:01:31,560 As four rolls. 18 00:01:31,860 --> 00:01:35,970 So if you mention any specific rule, it will only fit those rules. 19 00:01:36,060 --> 00:01:39,420 But since we want all the rules, we have put colon. 20 00:01:40,080 --> 00:01:47,130 And after this comma, we need to mention our column names since we need all the columns except the 21 00:01:47,130 --> 00:01:49,830 sorted volume will write B.F. dot columns. 22 00:01:56,060 --> 00:02:00,090 Not according to solid. 23 00:02:07,210 --> 00:02:09,890 We'll run this over X really believes that, Eddie. 24 00:02:12,920 --> 00:02:20,110 We need on these solid variable from our data frame as dependent variable rate like we to be. 25 00:02:20,750 --> 00:02:21,910 And then a squared record. 26 00:02:22,070 --> 00:02:26,080 I will mention the column name that is sort. 27 00:02:32,660 --> 00:02:40,310 If you want to look at the data, you can take head of X and Y, we will follow the same step as we 28 00:02:40,310 --> 00:02:48,080 did for logistic regression model with single variable, with first clear classifier object of logistic 29 00:02:48,080 --> 00:02:52,610 regression and then fade X and Y into that object. 30 00:02:53,300 --> 00:02:56,570 So does the same code as last time. 31 00:02:56,810 --> 00:02:58,080 So we will get a clear trend. 32 00:02:58,220 --> 00:02:58,610 This. 33 00:03:01,800 --> 00:03:06,870 We have fitted our model to look at the coefficient value of this model. 34 00:03:08,220 --> 00:03:12,330 We have to call attribute of this object, which is quiff underscored 35 00:03:15,220 --> 00:03:17,280 since our model is allowed. 36 00:03:17,400 --> 00:03:21,850 We will write C11, score a la dot quiff, underscore this. 37 00:03:23,070 --> 00:03:24,140 These are the coefficient. 38 00:03:25,320 --> 00:03:29,400 So the first one is between, the second one is Bitauto and so on. 39 00:03:31,770 --> 00:03:37,440 So the coefficient of your prize variable is minus zero point two five. 40 00:03:38,880 --> 00:03:42,120 And then our next variable was that I said a.D.A. 41 00:03:42,240 --> 00:03:53,160 So the coefficient of, let's say the area is minus zero point zero zero eight six and so on to get 42 00:03:53,160 --> 00:03:56,010 the value off and be done or which is intercept. 43 00:03:56,240 --> 00:04:00,480 We'll write CLV, underscore a la dot intercept. 44 00:04:03,130 --> 00:04:05,170 You can see this is the value of Bedazzler. 45 00:04:06,070 --> 00:04:11,070 Now we have values of all the B does, but does it all be done up to be happy? 46 00:04:14,230 --> 00:04:19,630 We can use these values to get the equation of what logic function. 47 00:04:23,200 --> 00:04:29,380 Using this bit of values, you can predict the probabilities and we will use those probabilities to 48 00:04:29,380 --> 00:04:32,440 predict why variable and coming radios. 49 00:04:36,280 --> 00:04:45,680 This is all you for logistic regression model and a Skillern on let's move on to a sex model. 50 00:04:47,050 --> 00:04:53,830 As I mentioned earlier, for sex model, we need to add consent and deliver X by default. 51 00:04:54,040 --> 00:04:56,980 The intercept value is zero and it sets model. 52 00:04:57,130 --> 00:04:59,650 That is B to zero, equal to zero. 53 00:05:01,840 --> 00:05:07,840 But to add consent, we will create X, underscore Korn's variable. 54 00:05:10,690 --> 00:05:15,850 Remember to import the sex model liabilities before executing this. 55 00:05:15,850 --> 00:05:16,180 Come on. 56 00:05:18,790 --> 00:05:20,380 Now let's forget this model. 57 00:05:24,030 --> 00:05:30,660 We will use the same code that we use for logistic regression with single word even 58 00:05:33,650 --> 00:05:35,760 and to get the summary of this model. 59 00:05:35,900 --> 00:05:36,360 Well, right. 60 00:05:36,540 --> 00:05:37,890 Logic, not somebody. 61 00:05:42,210 --> 00:05:49,100 You can see in the bottom table we have all the variables as the rules and we have their coefficient 62 00:05:49,520 --> 00:05:50,610 in the first column. 63 00:05:51,000 --> 00:05:58,500 And then we have other values, such as the standard error, the value and p value for each of these 64 00:05:58,500 --> 00:05:59,130 variables. 65 00:06:00,090 --> 00:06:01,440 This is how we run the model. 66 00:06:01,600 --> 00:06:04,260 We discuss the result and upcoming weirdo's.