Update README.md

Author: skotak2

README.md

@@ -1,8 +1,26 @@
 # Parsing Text Using Map-Reduce Programming Model
 
-The evolution of big data systems is based on the foundational programming paradigm of Map-Reduce, involving high scale computation of data processing on a network of comodity hardware.This project is to illustrate on implementation of map-reduce and parallelize the process.
+### TABLE OF CONTENTS
+* [Objective](#objective)
+* [Technologies](#technologies)
+* [Data](#data)
+* [Map-Reduce](#map-reduce)
+* [Implementation](#implementation)
+* [Results](#results)
 
-<ins>**Concept of Map-Reduce**</ins>:
+## OBJECTIVE 
+Perform processing of text and count the occurence of each word using map-reduce concept amd mimic Hadoop infrastructure with parallel processing. Multi-threading is used to execute two mapper and reducer functions.
+
+## TECHNOLOGIES
+Project is created with: 
+* Python - Multi-Threading
+
+## DATA
+The data is made available [here](https://github.com/skotak2/Pasrsing-Text-with-MapReduce-programming-Paradigm-with-multithreading/blob/master/Data/Data.txt)
+
+![GitHub Logo](https://github.com/skotak2/Pasrsing-Text-with-MapReduce-programming-Paradigm-with-multithreading/blob/master/Images/input.jpg)
+
+## MAP REDUCE
 
 Consider the following Text - "I am a human being. I am a Data Scientist"
 
@@ -29,16 +47,22 @@ Consider the following Text - "I am a human being. I am a Data Scientist"
 
 Here we implement the concept of multithreading, to parallelize the process. Map Reduce is divided into sub tasks in parallel & aggregate teh results of sub-totals to final output. The process of mapping key to value and further aggregating them through reducers is achieved by the theards.
 
-<ins>**Implementation:**</ins>
+
+## IMPLEMENTATION
 
 With the above concept in place, we implement the setup in the following steps:
 
-**Step1** : Map for key value pairs with multiple mappers
+*Step1* : Map for key value pairs with multiple mappers
 
-**Step2** : Sort the values and load in to the partition holder
+*Step2* : Sort the values and load in to the partition holder
 
-**Step3** : Multiple Reducers to pic from the partition and aggregate them
+*Step3* : Multiple Reducers to pic from the partition and aggregate them
 
 The above steps will yield a list of outputs from the reducer, which could be concatenated and loaded into a datafram or a spreasheet
 
-The code is available on  - "mapreduce.py"
+
+## RESULTS
+The deployed model can be accessed from the url from any system to translate kannada sentences to english. 
+
+![GitHub Logo](https://github.com/skotak2/Pasrsing-Text-with-MapReduce-programming-Paradigm-with-multithreading/blob/master/Images/output.jpg)
+