Happiness inside a job: A social netowork analysis
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bokeh
clean_data
data
features
src
.gitignore
1 - Introduction and database cleaning.ipynb
2 - Feature extraction.ipynb
3 - Visualizing graphs.ipynb
4 - Data augmentation.ipynb
5 - Machine learning.ipynb
GRAPH PLOTABLE.pck
G_comp.pck
Happiness inside a job.odp
README.md
inverse_alias.pck
plots+explained.html
shaolin_experimental.py
shaolin_graphs.py

README.md

#Happiness inside a job: A social netowork analysis

PyData-Bcn-2017

This is the repository for the talk materials. It contains several example notebooks and the dataset used to perform the analysis explained in the talk. The slides are available in google presentations. An .odt file is also available in this repository

This is still under construction, and we will be uploading aditional material during the next days.

Index

1. Introduction and database cleaning

2. Feature extraction

3. Visualizing graphs

4. Data augmentation

5. Machine Learning

6. R implementation

TODO

  • Upload R implementation
  • Add explanation to data augmentation
  • Add missing features to feature extraction notebook
  • Clean messy cells in ML notebook.
  • Comment machine learning notebook.
  • Discusion on feature selection