Problem Description Given dataset contains data of tweets on various airline’s twitter handles. It contains a total of 12 columns, out of which one column specifies the sentiment of the tweet. All other columns provide various information related to what was the tweet, where was it posted from, when was it posted, it's retweeted; etc. My task was to build a machine learning / deep learning model to predict the sentiment of the tweet using all or some of the other given columns ## Data Description Description of columns of the dataset is given below - 1. tweet_id -- Id of the tweet 2. airline_sentiment -- Sentiment of the tweet (Target variable) 3. airline_sentiment_confidence -- Confidence with which the given sentiment was determined 4. negativereason_confidence -- Confidence with which the negative reason of tweet was predicted 5. name -- Name of the person who tweeted 6. retweet_count -- Number of retweets 7. text -- Text of the tweet whose sentiment has to be predicted 8. tweet_created -- Time at which the tweet was created 9. tweet_location -- Location from where the tweet was posted 10. user_timezone -- Time zone from where the tweet was posted 11. negativereason -- Reason for which user posted a negative tweet 12. airline -- Airline for which the tweet was posted Inspiration The data is a nice combination of Numeric and Non-numeric featutres. it can be used for sentiment analysis.