Using IBM Watson Studio and Watson Machine Learning, this code pattern provides an example of data science workflow which attempts to predict the end-of-day value of S&P 500 stocks based on historical data. This pattern includes the data mining process that uses the Quandl API – a marketplace for financial, economic, and alternative data delivered in modern formats for today’s analysts.
🎓 Learning outcomes
- Use Jupyter Notebooks in Watson Studio to mine financial data using public APIs.
- Use specialized Watson Studio tools like Data Refinery to prepare data for model training.
- Build, train, and save a time series model from extracted data, using open-source Python libraries or the built-in graphical Modeler Flow in Watson Studio.
- Interact with IBM Cloud Object Storage to store and access mined and modeled data.
- Store a model created with Modeler Flow and interact with the Watson Machine Learning service using the Python API.
- Generate graphical visualizations of time series data using Pandas and Bokeh.
- Mridul Bhandari, IBM Developer Advocate, https://developer.ibm.com/profiles/mridul.bhandari/
- Anchal Bhalla, Data and AI Technical Specialist, https://developer.ibm.com/profiles/anchal.bhalla/
- IBM Cloud Sign-up link – http://ibm.biz/StockMarketWatsonStudio
- Hands-on – https://ibm.biz/StockMarketWatsonStudioLab
- GitHub Repository – https://github.com/IBM/watson-stock-market-predictor
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Dubai, United Arab Emirates