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Postdoctoral Researcher at Deutsches Elektronen-Synchrotron
Creators:
Christian Contreras-Campana, PhD
Dedicated area for the development data insight in the area of predictive analytics from churn prediction, fraud detection, and general maching learning modeling
This repository contains an example jupyter notebook. The DataInsight repository include the following topics:
- Data science visualization
- Model optimization
- Feature selection
- Machine learning model explanation and interpretation
- Deep learning
- Computer vision
- Reinforcement learning
- Topic modeling
- Sentiment analysis
- Time series analysis
- Cluster analysis
- A/B testing with Bayesian inference
- Survivial analysis
- Utilities toolkits
The DataInsight toolkit repository may be install using pip. Use the following command:
pip install -e git+https://github.com/chrisjcc/DataInsight#egg=DataInsight --process-dependency-links
For the purposes of code development the pip install version will need to be removed. Use the following command:
pip uninstall DataInsight
Then a local copy of the git repository will need to be checked out. These are the instructions on how to install and run local package:
- Inside repository:
python setup.py install
-
Start Jupyter notebook or restart Jupyter notebook depending on your circumstance.
-
Import the package using:
from neural_network import *
# Existing folder
cd existing_folder
git init
git remote add origin git+https://github.com/chrisjcc/DataInsight
git add .
git commit -m "Initial commit"
git push -u origin master
# Existing Git repository
cd existing_repo
git remote rename origin old-origin
git remote add origin git+https://github.com/chrisjcc/DataInsight
git push -u origin --all
git push -u origin --tags
Available hyper-parameter optimization techniques:
- GridSearchCV/RandomizedSearchCV: Exhaustive/Randomized parameter searches
- HyperOpt: Sequetial model-based optimization via Bayesian optimization with Gaussian Process or Tree-sturctured of Parzen Estimators (TPE)
- Bayes_Opt Bayesian optimization constructing a posterior distribution of functions for gaussian process,
- SkOpt: Bayesian optimization with acquisition function: Expected improvement (EI), Lower Confidence bound (LCB), and Probability Improvement (PI)
- Optunity: Primarily based on PSO