Follow the harvard style https you must cite all the datasets

to use with the following tasks, include these in your R working directory.

Total Marks 100, Weighting 20%

my.data <- the.data[sample(1:517,330), c(1:13)]
This would give you a new dataset with 330 rows and 13 columns.

Assign your transformed data along with your transformed variable of interest to an array (it should be 330 rows and 5 columns). Save it to a txt file titled "name-transformed.txt".

write.table(your.data,"name-transformed.txt")
The following tasks are based on the saved transformed data.

T4. Use your model for prediction.

Using your best fitting model from T3, predict Y (the area) for the following input
X5=96.1; X6=181.1; X7=671.2; X8=14.3; X9=20.7; X10=69; X11=4.9; X12=0.4 You should use the same pre-processing as in Task 2.

- Comment on the implications and the limitations of the fitting model you used for prediction.

The slides should contain all necessary information to prove your findings.

replaced with your surname or first name).

Your assignment will not be assessed if the code is missing, or the outputs of the code are inconsistent with the content of the slides.

"UCI Machine Learning Repository: Forest Fires Data Set". Archive.ics.uci.edu. N.p., 2017, http://archive.ics.uci.edu/ml/datasets/forest+fires.