diff --git a/README.md b/README.md
index 4c4ad4d..afacbcd 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,22 @@
 ## ML-Model-Flask-Deployment
 This is a demo project to elaborate how Machine Learn Models are deployed on production using Flask API
 
+
+
 ### Prerequisites
-You must have Scikit Learn, Pandas (for Machine Leraning Model) and Flask (for API) installed.
+You must have Scikit Learn, Pandas (for Machine Learning Model) and Flask (for API) installed.
+
+
 
 ### Project Structure
 This project has four major parts :
-1. model.py - This contains code fot our Machine Learning model to predict employee salaries absed on trainign data in 'hiring.csv' file.
+1. model.py - This contains code for our Machine Learning model to predict employee salaries based on training data in 'hiring.csv' file.
 2. app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
-3. request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
+3. request.py - This uses requests module to call APIs already defined in app.py and displays the returned value.
 4. templates - This folder contains the HTML template to allow user to enter employee detail and displays the predicted employee salary.
 
+
+
 ### Running the project
 1. Ensure that you are in the project home directory. Create the machine learning model by running below command -
 ```
@@ -18,24 +24,36 @@ python model.py
 ```
 This would create a serialized version of our model into a file model.pkl
 
+
+
 2. Run app.py using below command to start Flask API
 ```
 python app.py
 ```
 By default, flask will run on port 5000.
 
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 3. Navigate to URL http://localhost:5000
 
+
+
 You should be able to view the homepage as below :
 ![alt text](http://www.thepythonblog.com/wp-content/uploads/2019/02/Homepage.png)
 
+
+
 Enter valid numerical values in all 3 input boxes and hit Predict.
 
-If everything goes well, you should  be able to see the predcited salary vaule on the HTML page!
+
+
+If everything goes well, you should  be able to see the predicted salary value on the HTML page!
 ![alt text](http://www.thepythonblog.com/wp-content/uploads/2019/02/Result.png)
 
+
+
 4. You can also send direct POST requests to FLask API using Python's inbuilt request module
-Run the beow command to send the request with some pre-popuated values -
+Run the below command to send the request with some pre-populated values -
 ```
 python request.py
 ```