please write python code:

For this assignment, you are going to evaluate the sensitivity of decision trees and the $k-$nearest neighbour

algorithm to different data quality issues. Your sensitivity analysis needs to consider both classification and

regression problems. Sensitivity to the following data quality issues have to be explored:

Outliers

Noise

Missing values

Irrelevant features

For continuous$-$valued features, the effect of value ranges that differ in order of magnitude

For classification problems, the effect of skew class distributions

For each of the issues above, you have to carefully think about the process that you will follow. This includes

creating appropriate datasets and selecting sensible performance measures.

Your report should provide a detailed description of the algorithms used, and a discussion of your expectations

about sensitivity towards each of the above data quality issues. The approach followed towards each of the data

quality issues is described in the methodology section of your report. The empirical process provides information

about control parameters, performance measures, and data sets. All detail to reproduce your experiments have

to be provided. The results are provided in the results section, and are used to provide a conclusion about

sensitivity with respect to each data quality issue. Comment on whether the empirical observations correlate