Update README.md

README.md

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-# P7_Scoring
-
 # Introduction
-Our notebooks have been initialized thanks to several sources provided around the Home Credit default risk Kaggle 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.
+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.
 
 # Notebooks & dashboard
+
 There are 4 different notebooks:
+* __Current : P7_EDA__: focusing on Exploratory Data Analysis,
+* __P7_FE_FS__: focusing on Feature engineering and a first model-agnostic Feature Selection,
+* __P7_Model__: focusing on scoring with model evaluation,
+* __P7_Interpretation__ : focusing on model interpretation, <br/>
+* and a __Dashboard__ python file.
 
-P7_EDA: focusing on Exploratory Data Analysis,
-P7_FE_FS: focusing on Feature engineering and a first model-agnostic Feature Selection,
-P7_Model: focusing on scoring with model evaluation,
-P7_Interpretation : focusing on model interpretation,
-and a Dashboard python file.