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You have a folder with 10x data that you want to try out different machine learning models on and see what is best. The contents of the folder look like this: * `dataset/`: This is the root folder with all the data you will be working with. * `model1/`: This is where you will save all the code and data you produce from the experiment. * `model2/`: This is where you will save all the code and data you produce from the experiment. * `model3/`: This is where you will save all the code and data you produce from the experiment. You are required to follow the rules below when preparing the data: * The data is already split into 10 files. Each file is labelled `file_X.csv`. * All files are in the same folder. The folder is called `dataset` and the file names are: `file_1.csv, file_2.csv, and so on until file_10.csv`. * All files are of equal size. The files are all of equal length. The length of each file will be the same as all the other files in the folder. You need to complete the following tasks: * Export the data into the same folder using the command line command: **python dataset/convert_data.py --prefix="dataset/" --folder="dataset" --outdir="dataset/"** The command-line options are as follows: * `--prefix`: This is where the data will be stored. * `--folder`: This is the path where the data will be stored. * `--outdir`: This is the path where the exported files will be saved. Make sure you specify a valid path to the data, this should be one that exists on your computer. If you are unsure, use the `ls` command in the command-line to check.

 

 


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