Anilkumar Panda's repositories
kooptimize
Knock out rule optimisation using Non dominated Sorting Genetic Algorithm-II
anilkumarpanda
Config files for my GitHub profile.
anilkumarpanda.github.io
Personal blog
approachingalmost
Approaching (Almost) Any Machine Learning Problem
best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
blog_series
Code accompanying my blog
erroranalysis
Error analysis for ML model on Tabular data
fairness_cb
Fairness Code Breakfast
credit-risk-modelling
Credit Risk Modelling
credit_risk_model
A comprehensive credit risk model and scorecard using data from Lending Club
cs329s-ml-deployment-tutorial
Code and files to go along with CS329s machine learning model deployment tutorial.
gitignore
A collection of useful .gitignore templates
html-reports
Module to create static html reports
ing_connect_fairness
INGA Connect Fairness 2023 Workshop
interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
introduction_to_ml
Introduction to Machine Learning
Machine-Learning-for-High-Risk-Applications-Book
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
MLgenerator
MLgenerator is a web app which help you to generate machine learning starter code with ease.
pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
PiML-Toolbox
PiML (Python Interpretable Machine Learning) toolbox for model development and validation.
pydqc
python automatic data quality check toolkit
python-anti-patterns
An open collection of Python anti-patterns and worst practices.
python-training-2021
Introduction to Python Training.
skope-rules
machine learning with logical rules in Python
stability_selection
Compare Stability Selection with other Feature Selection techniques.
wells-fargo-competition
LGB+NN feature extraction