Examples showcasing some of the topics that will be discussed at the webinars.
Create an infographic that shows the analytics from tweets containing a certain keyword. In particular, visualize the following:
Tweet Timeline: A timeline showing the dates and times the tweets were posted.
Tweet Sentiment Analysis: Classify the sentiment of the tweets (in English) as being positive, negative or neutral.
Favorite Counts: Number of times the tweets have been liked.
Retweet Counts: Number of times the tweets have been retweeted.
Get the Data
Connect to Twitter:
Note: To edit or evaluate the code in this notebook, click Open In at the bottom of the browser window to select a Wolfram Cloud product. Place your cursor within the code and press the Shift and Enter keys together.
Set the keyword to search for in tweets:
Search for the tweets (in English) containing the keyword and download the data:
Use automated machine learning to predict the median value of owner-occupied homes in 506 Boston suburbs, based on potential influential factors (such as the crime rate, number of rooms, distance to employment centers, etc.).
Use the trained model to predict the price of this home (in thousands of dollars):
Test the Model
Various evaluation metrics can be used to see how well the model is performing on the test data.
Create a PredictorMeasurements object and extract various metrics of evaluation and other information from it:
Standard deviation that represents the root mean square of residuals:
Plot of predicted values versus test values:
Where to Go Next
Visit http://mpdatascience.com to learn more about the integrated multiparadigm approach to data science offered by Wolfram technology and explore further examples.
Get started with incorporating statistical analysis into your workflow with this free videocourse at Wolfram U.
Learn how to use the Wolfram neural network framework to employ machine learning in your data science project. Visit the Wolfram U Machine Learning page to watch video courses and also to register for our Machine Learning Webinar Series.