NegaJoules Project - The Machine Learning part

This document describes the methodology for importing data from a SQL database to Python environment, to conduct analysis, deliver Machine Learning predictive models and perform optimization on that model to improve the energy consumption of the systems under study.

%Further documentation on the method can be found in this blog %post.

Generating the solution

Install the dependencies

The models are written in Python 2.7 and makes use of the NumPy, scikit-learn, and pandas packages. These can be installed individually via pip or all together in a free Python distribution such as Anaconda.

Theano can be installed and configured to use any available NVIDIA GPUs by following the instructions here and here. The Lasagne package often requires the latest version of Theano; a simple pip install Theano may give a version that is out-of-date (see Lasagne documentation for details).

Lasagne can be installed by following the instructions here.

Download the code

To download the code run:

git clone git://github.com/simaaron/kaggle-Rain.git

Create an empty data folder

cd kaggle-Rain
mkdir data

Download the training and test data

The training and test data can be downloaded from the Kaggle competition webpage at this link. The two extracted files train.csv and test.csv should be placed in the data folder.

Note: the benchmark sample solution and code provided by Kaggle are not required.

Preprocess the data