MoTUS machine learning approach - Machine Learning Project 2

Machine learning Project 2 Option A – CS-433 – EPFL<br> Fall 2018<br> *Authors:* Gianluca Mancini, Tullio Nutta and Tianchu Zhang<br> *Laboratory:* LESO-PB – Solar Energy and Building Physics Laboratory<br> *Supervisors:* Roberto Castello and Dasaraden Mauree

Scripts outline

• data_extractor_v1 to v3.ipynb: averages the raw data starting from non compressed, .zip and .7z respectively. v3 is unstable (probable unexpected error at some point) due to high compresion ratio of .7z files. Input file should be places in /data_zip. v2 version used.

• regression_mat_builder_v1.ipynb: takes file in /average_data_year and creates regression_mat_year.csv (~200MB) cleaning the data.

• data_extractor_7zip and _zip.ipynb: .py files of the datat extractor v3 and v2 respectively. Ready to run on the server.

• 1. Folders and files outline
• *average_data_year:* contains already a preprocessed files for data of almost a year (January to mid November).

• *data_ext_original:* all the scripts already provided to extract the data

• *data_zip:* empty folder. Set input data path for data extractor files.

• *regression_mat_year.csv:* averaged (on a 5 minutes time interval) and condensed data. Consider data from January to mid November.