Can anyone help me please solution this question ? thank you in advance Predicting room occupancy by using decision tree and random forests classification algorithms. Using Studio R

a) Load the room occupancy training and testing datasets. Train a decision tree classifier and evaluate the predictive performance by reporting the classification accuracy obtained on the testing dataset.

(b) Output and analyse the tree learned by the decision tree algorithm, i.e. plot the tree structure and make a discussion about it.

(c) Train a random forests classifier and evaluate the predictive performance by reporting the classification accuracy obtained on the testing dataset. Define set.seed(1).

(d) Output and analyse the feature importance obtained by the random forests classifier.

testing dataset

Temperature,Humidity,Light,CO2,HumidityRatio,Occupancy

21.89,31.55,436.5,1047,0.0051296601,0

21.89,31.36,434,1031,0.0050985151,0

21.89,31.125,432.75,977.5,0.005059998,0

21.7,28.5,279.3333333333,585,0.0045762473,1

20.6,21.865,454,652.5,0.003274764,0

20.6,22.2,442.75,681.75,0.0033252058,0

20.6,22.26,444,702.3333333333,0.003334241,0

training dataset

Temperature,Humidity,Light,CO2,HumidityRatio,Occupancy

23.18,27.272,426,721.25,0.0047929882,1

23.15,27.2675,429.5,714,0.0047834409,1

23.15,27.245,426,713.5,0.0047794635,1

23.15,27.2,426,708.25,0.0047715088,1

23.1,27.2,426,704.5,0.0047569929,1

23.1,27.2,419,701,0.0047569929,1