This is a final project submission for the following course:
Python for Data Analysis and Scientific Computing
COMPSCI X433.3 - 005
David Lim Cheng (X017427)
All credits to the following articles, which were used as outlines, and from which most of the code is based off of:
- Understanding Matrix Factorization for Recommendation
- Recommender Systems in Python 101
- Matrix Factorization for Movie Recommendations in Python
This project was built using the Conda package management system. Included is an environment.yml, which can be used to create a conda environment:
// navigate to the project directory
cd ~/path/to/dir
conda env create -f environment.ymlThis will create a conda environment called ucbx-python. Activate the environment and start a Jupyter Notebook server with the following commands:
source activate ucbx-python
jupyter notebookThis should automatically open a new tab at localhost:8888. Once the notebook is open, the cells can be worked through linearly, with comments in the code as well.
blas 1.0
ca-certificates 2018.03.07
certifi 2018.4.16
cycler 0.10.0
freetype 2.8
intel-openmp 2018.0.0
kiwisolver 1.0.1
libcxx 4.0.1
libcxxabi 4.0.1
libedit 3.1.20170329
libffi 3.2.1
libgfortran 3.0.1
libpng 1.6.34
matplotlib 2.2.2
mkl 2018.0.2
mkl_fft 1.0.1
mkl_random 1.0.1
ncurses 6.1
nltk 3.3.0
numpy 1.14.3
numpy-base 1.14.3
openssl 1.0.2o
pip 10.0.1
pyparsing 2.2.0
python 3.6.5
python-dateutil 2.7.3
pytz 2018.4
readline 7.0
scikit-learn 0.19.1
scipy 1.1.0
setuptools 39.1.0
six 1.11.0
sqlite 3.23.1
tk 8.6.7
tornado 5.0.2
wheel 0.31.1
xz 5.2.4
zlib 1.2.11