* update README.md

README.md

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 # P7 project : OpenClassRooms subject
 Interactive dashboard for the "Prêt à dépenser" company, designed to provide account managers with information on the conditions for granting loans. 
 
-# Files description 
-api file : 
+## Files description 
+**api file :** 
 
-main.py created by FastAPI : one route returns the model's prediction scores for the selected loan ID and another returns the local importance values 
+main.py created by FastAPI,  one route returns the model's prediction scores for the selected loan ID and another returns the local importance values 
 
-requirements.txt : 
+requirements.txt : file with the list of packages used and their version numbers.
+
+sample_call.py : dictionary of an example (loan ID) of values for all the features required for an API response to the prediction request
+
+app.yaml et cloudbuild.yaml : API configuration and deployment files on GCP
 
 tests file : 
 test_api.py : contains unit test files that check the correct operation of the various API functions. 
 
-api dashboard :
+model.pkl : binary file containing the best trained model recorded using the MLflow library. File only used for local deployment of the API and Dashboard. 
+
+**api dashboard :**
+
+main.py created by streamlit library. dashboard source code, including the definition of the various visualisations, user interactions and data queries.
+
+scaler.pkl : serialization of standarscaler object used in the data pre-processing step before training the model 
+
+## Interactive Dashboard with python - Streamlit : Instructions
+### Description
+The credit approval risk assessment dashboard is an interactive application created with Streamlit. It enables users to view and analyse a customer's data and loans, providing relevant graphs. It also provides a score to predict the risk of granting a loan. 
+This dashboard can be accessed directly from the web, or it can be deployed locally by following the instructions below: 
+
+### Instructions 
+1. Prerequisites:
+
+* Make sure you have Python installed on your system.
+* Install the required dependencies by running the following command in your terminal:
+```sh
+pip install -r requirements.txt
+```
+2. Running the API and the Dashboard:
+* Clone the GitHub repository containing the API and Dashboard code using the following command:
+
+```sh
+git clone https://github.com/AuHoh/P7
+```
+* Navigate to the project directory using the command:
+```sh
+cd P7
+```
+* Using the console, from the `api/` directory, launch API with the following command:
+```shell
+python main.py
+```
+
+* Using the console, from the `dashboard/` directory, launch Streamlit with the following command:
+```shell
+streamlit run main.py
+```
+* Once the server is running, you should see a local link in your terminal (e.g., http://localhost:8000). Click on this link to access the Streamlit dashboard in your browser.
+
+3. Using the Dashboard:
+
+* Upon opening the dashboard, you will see a user-friendly interactive interface where you can choose the loan ID from the drop-down list or type it in directly. 
+* In the sidebar, you'll see different information about the customer and the corresponding loan. 
+* On the main page, you will be able to make a prediction of the loan approval risk assessment based on the information available in the database and/or, as an option, you can use the variation bars to try to modify some of the loan amounts before making a new prediction. 
+* Click on the "predict" button, and you will see the prediction with its metric, and a graph of the top 50 global features importance of the model. 
+* Click on the "Feature contribution to customer score" button to calculate the feature importance values specific to the loan ID case.  
+* Click on the "Download the description of the model features" button to obtain an Excel table with a description of all the columns in the dataset. 
+* Interactive charts display the ability to select features from a drop-down list to see either the distribution of the feature based on whether credit is granted or denied, or to view a bi-variate analysis chart and position the customer value .
+
+If you have any questions or feedback, use the contact details provided in the "Contact" section to get in touch with the team responsible for the dashboar
+
+
+
+
+### Screenshot
+
+![Dashboard example](Docs/Images/Dashboard1 2023-07-13 à 11.51.46.png)
+![Dashboard example](Docs/Images/Dashboard2 2023-07-13 à 11.54.40.png)
 
-# Interactive Dashboard with python - Streamlit : Instructions
 
-## Screenshot or demo
 
 ## Author
-Audrey Hohmann
+Audrey Hohmann, OpenclassRoom student : Data Scientist