Task description ---------------- The file task_data.csv contains an example data set that has been artificially generated. The set consists of 400 samples where for each sample there are 10 different sensor readings available. The samples have been divided into two classes where the class label is either 1 or -1. The class labels define to what particular class a particular sample belongs. Your task is to rank the sensors according to their importance/predictive power with respect to the class labels of the samples. Your solution should be a Python script or a Jupyter notebook file that generates a ranking of the sensors from the provided CSV file. The ranking should be in decreasing order where the first sensor is the most important one. Additionally, please include an analysis of your method and results, with possible topics including: * your process of thought, i.e., how did you come to your solution? * properties of the artificially generated data set * strengths of your method: why does it produce a reasonable result? * weaknesses of your method: when would the method produce inaccurate results? * scalability of your method with respect to number of features and/or samples * alternative methods and their respective strengths, weaknesses, scalability Hint: There are many reasonable solutions to our task. We are looking for good, insightful ones that are the least arbitrary. Please beware of the quality of the code as well.
