Help me build a model using random forest. the criteria is listed below

As a Data Scientist, one of the key tasks is to build models most appropriat/close to the truth; thus, modelling will not be limited to the aforementioned steps in this assignment.

the **rMSE** performance of your model, the better your model, the higher your score. Additionally, you need to describe/document your thought process in this model building process, this is akin to showing your working properly for the mathematic sections. If you don't clearly document the reasonings behind the model you use, we will have to make some deductions on your scores.

When you optimize your model's performance, you can use any supervised model that you know and feature selection might be a big help as well. [Check the non-exhaustive set of R functions relevant to this unit](https://learning.monash.edu/mod/resource/view.php?id=2017193) for ideas for different models to try.

$\textbf{Note}$ Please make sure that we can install the libraries that you use in this part, the code structure can be:

```install.packages("some package", repos='http://cran.us.r-project.org')```

```library("some package")```

Remember that if we cannot run your code, we will have to give you a deduction. Our suggestion is for you to use the standard ```R version 3.6.1```

You also need to name your final model ``fin.mod`` so we can run a check to find out your performance. A good test for your understanding would be to set the previous $\textbf{BIC model}$ to be the final model to check if your code works perfectly.

the data set can be found on this link below:

https://drive.google.com/drive/folders/1r_i7n4iUgOTaaLAPGfVNXj15VlpVLWb4?usp=drive_link

# Build your final model here, use additional coding blocks if you need to fin.mod <-

# Load in the test data. test <- read.csv("regression_test.csv") # If you are using any packages that perform the prediction differently, please change this line of code accordingly. pred.label <- predict(fin.mod, x_test) # put these predicted labels in a csv file that you can use to commit to the Kaggle Leaderboard write.csv( data.frame("RowIndex" = seq(1, length(pred.label)), "Prediction" = pred.label), "RegressionPredictLabel.csv", row.names = F )