Task: Build data set for a logistic regression model that predicts the state of a protein Data location: super_secret_md.tar.gz Requirements: - Data set must be in a CSV file format. - The CSV must not have any column headers and the last column(farthest right) must contain the label. - Within the parent data directory 'super_secret_md' are two directories named 'closers' and 'openers'. These will be the class labels you will be predicting. - Any data within the closers directory will have a label of 1 and anything within the openers directory a label of 0. - Within each of these 2 directories, there will be additional directories that that contain several .dat data files. These data files will have our feature data in them. - One .dat file == one sample - Each file will be formatted as 2 values per row. The first value can be ignored but the second will be the feature used in the data set. - If the contains NaN values, handle it as you see fit. - The max length of the feature vectors is 20. Fill with zeros to make shapes consistent. - Feel free to install Python modules. - To run the model script, python analysis.py path/to/data.csv