PREREQUISITES:
-> Working Internet Connection

-> Python 3.6 or above

-> Pip (pip is inbuilt if python 3.6 and above is installed)

-> Beautifulsoup 4-
To install Beautiful soup library , open command prompt and type in "pip install beautifulsoup4"
For further reference check the documentation for beautiful soup here
https://www.crummy.com/software/BeautifulSoup/bs4/doc/#

-> Requests
To install Requests library , open command prompt and type in " pip install requests"
For further reference check the documentation for Requests here
http://docs.python-requests.org/en/v2.7.0/

-> Pandas
To install Pandas , open command prompt and type in " pip install pandas"
For further reference , check the documentations for Pandas here
https://pandas.pydata.org/pandas-docs/stable/whatsnew.html

-> Scikit Learn
To install Scikit Learn , open command prompt and type in " pip install scikit-learn"
For further reference , check the documentations for Scikit-learn here
https://pypi.org/project/scikit-learn/

-> Vader Sentiment Analyzer
To install Vader Sentiment Analyzer, open command prompt and type in " pip install vaderSentiment"
For further reference, check the documentations for Vader Sentiment Analyzer here
https://github.com/cjhutto/vaderSentiment#installation

RUNNING THE CODE:

-> Before running the code the dataset containing the companys previous stock values has to be fed to the code

-> The user can give in any csv file(should contain open and close prices) and name it as company.csv
   and place it in the directory that contains the code

-> One csv file is already loaded into the folder for testing purposes

-> open "Stock Prediction using Linear Regression and Sentiment Analysis.py" and run it using python IDLE

-> Ignore warning messages if displayed

-> The code asks for the user to input the company name which is the search term, enter a search term and the code starts to execute

-> wait for the code execution to complete to get the output