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100-days-of-ml-code

100 Days of ML Coding

academy-course-dat31018

Exploratory Data Analysis using Pandas, Seaborn and Statsmodels

address_normalize

根据地址提取省、市、区/县、街道，并进行标准化

addressresolution

python地址解析/查区号/查邮编

advance-credit-risk-model

ai_risk

马上消费金融挑战者大赛-违约用户风险预测--第三名方案

akshare

AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库

auto-sklearn

Automated Machine Learning with scikit-learn

autobinning

分箱工具

autofeat

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

automatic-scorecard-model

评分卡建模自动化流程

automlwhitebox

Tool for whitebox (binning + logreg) model development

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-learn-datascience

:chart_with_upwards_trend: Curated list of resources to help you get started with Data Science

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-systematic-trading

A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.

banking-behaviour-scorecard

💳 The objective is to build a banking behavior scorecard model for HDFC's internal customers through a user's liability account and predict the credit risk.

binary_ensemble

binninganalysis.jl

Statistical standard error estimation tools for correlated data

binningpy

tool for binning continuous value

binsembler

Binsembler - A Binwise Ensembler. In general, Ensemble techniques combine the perspective of various models by aggregating the predictions output by each of these models thus tend to enhance the overall prediction accuracy. Simple techniques such as taking majority vote or simple averaging of the predicted probabilities or weighted averaging of predicted probabilities based on model’s F1 score or Accuracy or any other measure are the popular choices to ensemble the model predictions. Here we propose a novel approach based on aggregating the predicted probabilities as weighted averages where weights are the performance statistic based on bins the probabilities fall in. Idea is to divide the predicted probabilities of each model on a validation set into equal sized bins (preferably deciles) and calculate the metrics in each bin. Pick any one metric, and note down it for each bin in a mapping table. This will be the weight used in our weighted ensemble approach. When the prediction to be made on new data, first map the predicted probabilities for the new data to an appropriate bin and then pick the respective metric value for that bin from the mapping table and multiply with the predicted probability. Repeat the same for second model. Finally calculate a new predicted probability as the weighted average.

binst

Bin Stuff - a binning library for R/Python

boruta_py

Python implementations of the Boruta all-relevant feature selection method.

business-machine-learning

A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)

cards-campaigns-analysis

Analysis of credit card campaigns.

category_encoders

A library of sklearn compatible categorical variable encoders

cfa

FRM & CFA study notes

chimerge

对数据框中的某个变量进行有监督的分箱操作

china-merchants-bank-credit-card-cente-user-purchase-forecast

招商银行信用卡中心 消费金融场景下的用户购买预测 rank1

chinese_province_city_area_mapper

一个用于提取简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块