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aix360-introduction

Introduction to explaining data and machine learning models with aif360

bicycle-rentals-predictor

Regression model that predicts demand for bicycle rentals

catboost

Catboost model implementation(SHAP)

codes_for_articles

college-admission-prediction-model

Model that can predict chances of college admission, using a pipeline for the different feature types, compared Ridge and LGBMRegressor pipeline with hyper-parameter tuning and train/test scores.

coverage-checker

This application is based on a CatBoost machine learning model. This basically takes four queries from the user (Upazila/Thana name, Network availability (3G/4G), District, and Zip code) and outputs the best operator for that location. This model was trained on the data I collected from Opensingnal application. I collected 22,360 data for 559 locations of Bangladesh. Currently this model is based on a static dataset but in the future, I have a plan to upgrade it to a real-time data collection-based model.

eli5

A library for debugging/inspecting machine learning classifiers and explaining their predictions

feature_engineering_and_xgboost_parameter_tuning

Examples of how to do feature engineering and Xgboost parameter tuning

fintech-lendingclub-model-by-catboost-algorithm

python风控建模实战lendingClub

interpret-ml

Explainable Machine Learning using SHAP for the CAC Consortium

interpretable-machine-learning

Machine Learning Interpretability - LIME e SHAP

interpretable-ml-shear-squat-wall

Developing an interpretable machine learning model for predicting the shear strength of RC squat walls using XGBoost and SHAP.

lgbmregressor

A simple implementation to regression problems using Python 2.7 and LightGBM

machinelearningwithinterpretability

Using Lime and Shap modules to understand how black box models works.

marvel-predicting-appearances

A simple data science project that involves web scraping, data cleaning and visualization, model building, and model explanation using character data from Marvel Fandom.

ml_poj_lgbmclassifier

CCCF-数据挖掘-企业非法集资风险预测

numerai_kfold_ensemble

A GBDT NumerAI ensemble model using LightGBM + CatBoost with KFold CV.

perovskite-ml

Comparative Analysis of Machine Learning Approaches on the Prediction of the Electronic Properties of Perovskite: A Case Study of the ABX3 and A2BB’X6

project-4-feature-selection_model-selection-and-tuning

Goal is to predict the concrete compressive strength using collected data

results_interpretation_by_shap

Interpretation results of machine learning models by SHAP

shap

A game theoretic approach to explain the output of any machine learning model.

treeexplainer-study

Code and documentation for experiments in the TreeExplainer paper

vasp_script

Useful scripts for VASP(very useful)