can any one help me start with this question , I need to do using Studio R language.

dataset: training

temperature,humidity,iight,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 23.1,27.2,419,701.6666666667,0.0047569929,1 23.1,27.2,419,699,0.0047569929,1 23.1,27.2,419,689.3333333333,0.0047569929,1 23.075,27.175,419,688,0.0047453507,1

thank you

Studio R language

2. Predicting room occupancy by using decision tree and random forests classification algo rithms. (20% 25%) Marking scheme: MSc: 5.0% each BSc: (a), (e) 5.0% each; (b), (d) 7.5% each. (a) Load the room occupancy training and testing datasets that are also used for the 1st coursework. 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. Te, 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. the mashina else ifration alvorithm. (20% 25%) 2. Predicting room occupancy by using decision tree and random forests classification algo rithms. (20% 25%) Marking scheme: MSc: 5.0% each BSc: (a), (e) 5.0% each; (b), (d) 7.5% each. (a) Load the room occupancy training and testing datasets that are also used for the 1st coursework. 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. Te, 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. the mashina else ifration alvorithm. (20% 25%)