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

Abstract

The purpose of the project was to select the most important features and investigate possible machine learning algorithm on the 2018 data collected through the MoTUS setup.

Directory tree

├───data_extractor_scripts
│   ├───.ipynb_checkpoints
│   ├───average_data_year
│   └───data_zip
├───exploratory_analysis
├───final_submission_scripts
│   ├───.ipynb_checkpoints
│   ├───images
│   ├───py_scripts
│   │   └───.ipynb_checkpoints
│   └───__pycache__
├───intermediate_work_scripts
├───report
└───submission_folder
    ├───data
    │   ├───average_data
    │   └───zip_files
    ├───results
    │   ├───baseline
    │   │   └───images
    │   ├───neural_network
    │   ├───random_forest
    │   ├───results_analysis
    │   └───ridge_regression
    │       └───images
    ├───scripts_data_extractor
    ├───scripts_features_selection
    └───scripts_regression
        ├───.ipynb_checkpoints
        └───__pycache__

Overview

This repository, other than the definitive results and scripts, contains all the intermediate goals for possible future investigation of the project development. The general content of the files (mainly .ipynb) and folders of this directory is shown below. Subfolder readmes will be present.

• data_extractor_scripts: contains all the different versions of the code used to convert the raw data into the regression matrix used in the machine learning algorithms. A final regression matrix is also included.

• exploratory_analysis: includes all the scripts used firstly to investigate the raw data. No notable operations are performed with these scripts.

• final_submission_scripts: contains the scripts able to efficiently run the 3 selected regression methods. It includes all the accessory files and .py conversion of the notebooks used run the scripts on the laboratory server.

• intermediate_work_scripts: includes the intermediate file used to run the explorative version of the regression.

• report: contains the final report for the CS-433 course, together with its appendix and the guidelines for the project.

• submission_folder: includes the final version of all the script necessary to produce the conclusion present in the report starting from the raw data.

• *SCRIPTS_RULES.txt:* general rules and procedures used throughout the project.

IMPORTANT: For working scripts and clean results always refer to /submission_folder. All codes have appropriate readmes and are adequately commented.