Machine Learning in Survey Research
October 25th, 2019
Please join instructor Adam Eck (assistant professor of computer science, Oberlin College), as he conducts a half-day workshop titled “Machine Learning in Survey Research”. This workshop is designed for population/survey researchers and analysts of all skill levels, and will present an introduction to machine learning concepts and their applications to survey research (such as sample frame creation, respondent modelling, and open-ended response coding).
- Introduction to machine learning and its applications to survey research
- Decision trees and random forests
- Deep learning and other neural network-based techniques
- ML techniques to model respondent behaviors, assist with coding of open-ended responses, and more
- Demonstration using R and Python
Slides & Lab Materials:
- A zip file containing slides, sample data, and all R and Python code is available here
- A sample dataset for use in the lab is available here
- Pre-compiled versions of the lab (R version and Python version)
The lab portion of this workshop will be mirrored in both R (using R Markdown) and Python (using Jupyter Notebook).
–R software (required)
## install packages install.packages(c("caret","rpart","rpart.plot","randomForest","mxnet"))
python -m pip install --upgrade pip python -m pip install -U pandas python -m pip install -U scipy python -m pip install -U scikit-learn python -m pip install -U IPython python -m pip install -U graphviz python -m pip install -U pydotplus