• Dataset:

The attached dataset is about PM2.5.

Training Data: Two alternate years can be taken as train data.​

Testing Data: Two years of data from the remaining three can be taken as test data.​

• Problem Statement:

You are supposed to perform two tasks for this dataset: Classification​ and ​ Regression​

1. Classification Task: Target Column: “Month”

Evaluation Metric: Accuracy

2. Regression Task: Target Column: “PM2.5”

Evaluation Metrics: MSE

Also, report the mean and standard deviation of the error.

Implement the above problem statement(both for Classification and Regression) from scratch using the following:

1. Decision Trees (DT) – (​ You have to analyze yourself as told in class for different depths, width and other parameters of the tree and draw your inferences.)
2. Bagged Decision Trees

• Random Forest

Implement these as taught in the class.

• Gaussian Processes:

In the data provided to you, you will find signal strength in dB vs distance. Assume the “Distance​ ”​ to be an independent variable and “Signal​ Strength”​ as a target. Compute the mean and variance prediction for signal strength at the following 5 points {Sr. No.: <2,4,6,8,10>}. Use GPR to train using the remaining data points {Sr. No.: <1,3,5,7,9,11,12>} from the table provided.