oegedijk/sagemaker-creditscore-explainer oegedijk/sagemaker-creditscore-explainer

  • Stars
    star
    6
  • Rank 2,539,965 (Top 51 %)
  • Language
    Jupyter Notebook
  • Created over 4 years ago
  • Updated about 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
oegedijk/sagemaker-creditscore-explainer
github

oegedijk

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Example of how to deploy an ML algorithm together with SHAP explanations to AWS Sagemaker, including a front end dashboard.

More Repositories

1

explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Python
2,287
star
2

dash_oop_components

OOP components for plotly dash that make dashboard components composable, reusable and configurable
Jupyter Notebook
52
star
3

rule_estimator

A scikit-learn compatible estimator based on business-rules with interactive dashboard included
Jupyter Notebook
28
star
4

dash_querystrings

Example of how to include querystrings to save state url in plotly's Dash
Python
17
star
5

explainingtitanic

A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc
Python
17
star
6

keras-embeddings

Example of how to use entity embeddings (similar to word embeddings such as word2vec, but then generalized for any categorical feature) in a Keras model.
Python
11
star
7

dash_oop_demo

Deployment example of dash_oop_components library
Python
6
star
8

dash-tabs

Example of how to use tabs in a plotly's Dash framework across multiple files
Python
4
star
9

blog

Repository that hosts my blog (created using fastpages)
Jupyter Notebook
3
star
10

deploy-xgboost-to-aws-lambda

short guide on how to deploy xgboost machine learning models to production on AWS lambda
Python
2
star
11

python-datascience-course

notebooks for internal Python for Datascience course that I teach at UWV.
Jupyter Notebook
2
star
12

sklearn-transformers

A collection of sklearn compatible transformer classes. Sklearn's standard transformers have some annoying lacunes (especially when fitting on new data with labels missing in training data for example), so these transformers fix that. Also some other more adventurous transformer classes. Shows how to quickly make transformer pipelines for e.g. making dataframe ready for neural net with embeddings.
Python
2
star
13

oegedijk

1
star