Update README.md

Author: saikumarkaleru

README.md

@@ -1,3 +1,5 @@
+#Goal
+Through this project, based on the characteristics, we will come up with another set of guided navigation, which can guide the user in case the user misses steps in a workflow on a consistent manner.
 # Tools And Technologies
 * html,css javascript
 * Google Analytics
@@ -12,18 +14,9 @@
 
 ## Description
 I have created an e-commerce website using Html,css,javascript,bootstrap then deployed into Netfly Platform . 
-It contains various webpages such as home, store, account, about,
-contact us. There are various features like product details, add to cart, search product .
-I have applied user guiding tool named WalkMe that will guide a new user through orderly
-steps on my website to use it in the most simple way to buy a product. I have integrated my
-website to Google Analytics Platform. This tool is able to track the users action
-and the time spent by the users on the various actions of the website. I have accumulated the
-analytics data from various users and stored it. I have collected data of around 75 users with
-various data points such as time spent by users on the different pages on website, filters being
-used by the users , time spent by users on particular product page, etc. Then, Fitting the Markov
-Chain model gives us the transition probabilities matrices and the lambda parameters of the chain
-for each one of the three lags along with the Start and End Probabilities.
+It contains various webpages such as home, store, account, about,contact us. There are various features like product details, add to cart, search product .
+I have applied user guiding tool named WalkMe that will guide a new user through orderly steps on my website to use it in the most simple way to buy a product. I have integrated my website to Google Analytics Platform. This tool is able to track the users action and the time spent by the users on the various actions of the website. I have accumulated the analytics data from various users and stored it. I have collected data of around 75 users with various data points such as time spent by users on the different pages on website, filters beingused by the users , time spent by users on particular product page, etc. Then, Fitting the Markov Chain model gives us the transition probabilities matrices and the lambda parameters of the chain for each one of the three lags along with the Start and End Probabilities.
 
-## Transaction Diagram
+## Transition probability
 
 ![alt text](https://github.com/saikumarkaleru/Analyse-User-Behaviour-Optimise-the-User-Workflow-Using-a-Machine-Learning-Algorithm/blob/master/download.png)