# Introduction

Our notebooks have been initialized thanks to several sources provided around the Home Credit default risk [Kaggle](https://www.kaggle.com/c/home-credit-default-risk) competition: data from Home Credit, Kernels-Notebooks from competitors, Discussions threads involving host and competitors.
While the objective of competitors is to get the best AUC score _Area under the ROC Curve_ by *__any means__*, we'll rather focuse on the ability to provide *__interpretable__* decisions, including the possibility given to customers to explore their case.

# Deliverables
methodological notice (French) : P7_ML_for_Credit_Scoring.pdf
slides : P7_Support.pdf

# Dashboard
The  __Dashboard__ python file, can be found in the public repo :  https://github.com/EtienneLardeur/Streamlit_App
it is hosted remotely here : https://share.streamlit.io/etiennelardeur/streamlit_app/main/local_app.py

or download and rune from your folder :
streamlit run local_app.py 
launched at localhost - working with remote inputs.

# Notebooks
There are 5 different notebooks:
* __P7_EDA__: focusing on Exploratory Data Analysis,
* __P7_FE__: focusing on Feature engineering,
* __P7_FS__: the feature selection,
* __P7_Model__: focusing on scoring with model evaluation,
* __P7_Interpretation__ : focusing on model interpretation ! also provide any attempt in order to properly design dashboard <br/>