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