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coding-challenge

da_weather-energy-analysis

da_wordcloud_r_pgmg

diabetes-prediction

fs_task

Data Engineer_FS_Task

germanphysicianrating_sem3

This project was a part of the Course - Social Media and Business Analytics. In this analysis, Data Science techniques and Statistical Models are used to answer the question- "How patients rate physicians? "

ml_email_spam_filter_sem1

This is my first Machine Learning project, in the Data Science Course. A classification model is built using Machine Learning Algorithms for email spam identification.

ml_nlp_spam-ham-deployment

ml_project

ml_salary_prediction_deployment

An application to compute salary of a person given age, interview score and hosted on Heroku using Flask

nlp_movie_recomm_deployment

optimizationtechniques

pandas_exercises

Practice your pandas skills!

twitter_and_stockprice_sem2

Twitter is an online social networking service with over 300 million monthly active users. This enormous amount of data available on social media platforms can be extracted and analyzed for various purposes. In this paper, we aim to investigate the relationship between sentiment analysis of Twitter data and stock market prices for five companies (Walmart, ExxonMobil, Apple, Berkshire Hathaway Inc., and Amazon) by scraping the Tweets extracted from Twitter based on company hashtags and using the twitter intelligence tool – twint. Sentiment analysis is applied to the extracted tweets and a correlation is analyzed between stock market movements of a company and sentiments in tweets. Elaborately, news and tweets in social media about a company would encourage decision of people to invest or not in the stocks of that company and as a result, the stock price of that company would increase or fall. At the end of the paper, it is shown that a none or very weak correlation exists between the rise and fall in stock prices with the public sentiments in tweets