 students, in this module, now we are doing as we are doing, now we will exercise that you can practically implement the random forest and how you can get benefit of this particular option available within your data science courses as I said, I have made some variations, I have not done all the exercises in one way or the other so that you may be whichever you like more or in different situations you can use it what is the status of your environment, what is the level of maturity accordingly so in this we will do that, we will install another library, scikit and then we will use this as an ensemble concept and then we will import it into the random forest regressor this is the model which you will implement in this exercise so first of all we have to train our data, you have x and y y is equal to random and this is your random forest, your prediction of that will come in the x test when that result comes to you then you tune it, I have given the whole code of tuning that how you have to tune it this is the different script, see when we said that we will import random forest regressor so now we have to mention this in our code that we have to use regressor here then this is your different criteria and grid search because you had this data in the form of a grid, so now you are searching it in grid you have more than 2 variables here, see when we talk about random forest, we have a very big data set so now it is in the form of a grid, so now we have to search it in the form of a grid and then we will have the trained values of x and y and we will have the best parameters of it so in this way we will tune it, we have done 2 steps, first we have trained and then we have tuned it after that we have to test it, so the whole code of this testing is available in it that how you have to test it, the line of each code again random forest is equal to, this is the same regressor that you have done this is true and false, whatever your values are, you will do it and just relax because when you will read the entire course of python or the full course of machine learning, then you will have a lot of time that you are a direct supervision of your teacher, instructor, you will do these things here because our objective is to give you an introduction, the overall subject of your data science so we are just touching base and giving you an introduction of all the things because if I do one exercise, it might take as much time as we have to cover 3, 4, 5 modules so the design of this course is like this, we share the content of the course we do the introduction, where the coding is needed, we are sharing the screenshots but this is in working condition, you can implement it in any training environment after that when you have trained it, then you have visualisation so you can see here, this is a random forest, the different component of the data this is the price of the other component, this is your y axis and this is your x axis so these are all the different dots that represent the values so if you have a regression line, that might be somewhere like this so this is one way that you will train the model as much as you increase the size of the data so these things will work for you again as I said this is a code, so I am not going to talk about this you can study this code as well, you can implement it I just repeat, the code that I have shared with you earlier and this code is in both the working or the full condition and when you do your practice at home, you can use this code as a starting point then the sky is the limit, you have a lot of codes available on the internet so you can implement all these things there