shubhamchaudhari1996/starbucks-twitter-analysis

It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.