diff --git a/README.md b/README.md
index b153418573400c4c1a70036118f4638111ece67c..b9ecb3b7af6ae70cb6d6ac841fc1fc4549909e69 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ This project is develop using the following:-
 2. Change Powershell to Comand Prompt by pressing the plus(+) symbol on top right corner of the Terminal.
 3. Activate the Virtual Environment (Venv) in Comand Prompt:  `Venv\scripts\activate` (Windows)  or `source/Venv/bin/activate` (Linux / MacOS).
 4. Now you are in your Virtual environment, Next step is to start Rasa server by: `rasa run`.
-5. Now it will show in console that Rasa server is up and running, It indicates that till now your rasa is running perfectly without any errors (You are very Lucky).
+5. Now it will show in console that Rasa server is up and running, It indicates that till now your rasa is running perfectly without any errors (**You are very Lucky**).
 6. If rasa server is running, Then Again open a new comand prompt from Terminal without closing the previous prompt. 
 7. Now it's time to Run the chatbot on Streamlit. For that enter : `streamlit run app.py` in new Comand Prompt.
 8. This will direct you to a default Browser, where you will find our 'Airline Chatbot'.  
@@ -92,3 +92,13 @@ Here is the overview of the requests:
 - Chatbot Implementation with RASA: 
 
     1. Intent: `domain.yml` & `nlu.yml` file where it gives the intent and according to that it show the responses for the user & nlu gives the examples according to the intent. 
+    2. Actions: `actions.py` file contains the back-end data where it read the csv file and use class function to get passenger flight details from the csv file and generate the response to users.
+    3. Streamlit File: `app.py` file contains the front-end data, which communicate between the user and the chatbot. 
+    4. stories.yml: `stories.yml` where it shows the process of conversation for the chatbot. 
+    5. rules.yml: `rules.yml` where user says goodbye and conversation end.
+    6. endpoints.yml: `endpoints.yml` where things runs smoothly with the help of url.
+    7. train models.py: `train_models.py` where it contains two models for the prediciton of the data.
+
+Each component has it's own where it has created user- friendly chatbot and gives predictions and analysis according to the data.
+
+# Work Done