Displaying 1 to 7 from 7 results

pixiedust-facebook-analysis - A Jupyter notebook that uses the Watson Visual Recognition, Natural Language Understanding and Tone Analyzer services to enrich Facebook Analytics and uses PixieDust to explore and visualize the results

  •    HTML

In this Code Pattern, we will use a Jupyter notebook to glean insights from a vast body of unstructured data. Credit goes to Anna Quincy and Tyler Andersen for providing the initial notebook design. We'll start with data exported from Facebook Analytics. We'll enrich the data with Watson’s Natural Language Understanding (NLU), Tone Analyzer and Visual Recognition.

watson-banking-chatbot - A chatbot for banking that uses the Watson Conversation, Discovery, Natural Language Understanding and Tone Analyzer services

  •    Javascript

Read this in other languages: 中国. Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work.

nlc-email-phishing - Detect email phishing with Watson Natural Language Classifier

  •    CSS

In this Code Pattern, we will build an app that classifies email, either labeling it as "Phishing", "Spam", or "Ham" if it does not appear suspicious. We'll be using IBM Watson Natural Language Classifier (NLC) to train a model using email examples from an EDRM Enron email dataset. Please note that this data is free to use for non-commercial use, and explicit permission must be obtained otherwise. The custom NLC model can be quickly and easily built in the Web UI, deployed into our nodejs app using the Watson Developer Cloud Nodejs SDK, and then run from a browser. Give the NLC service a name. This name will be used later if you Deploy to IBM Cloud when you add the service under Connections.

generate-insights-from-data-formats-with-watson - How do we process data in different formats like docx, pdf etc and generate insights to be linked with structured data in database?This pattern helps in establishing relations between structured & unstructured data to generate recommendations using Watson NLU & Watson Studio

  •    Jupyter

In this code pattern, we will demonstrate a methodology to integrate structured data & unstructured data to generate recommendations. Processing unstructured data coming in different data formats has many challenges with respect to data extract & derive meaning to help us take informed decisions, however the related data would be in the structured format. It would be time consuming process to check different data sources manually for inference and that is where this pattern will be handy. We will showcase a configurable yet scalable process which will help in merging the different data sources and expedite the process of decision making. We have taken the example of HR recruitment process where we use the candidate's resume to be compared with job description & candidate database to identify the best suited candidate for a given job profile. This will help the HR to develop an efficient recruitment plan. Our motto is to select the right candidate which helps in risk mitigation for the organization thereby enhancing the ROI and increases the credibility for the recruitment process. We will be using Watson Studio & Watson NLU to solve this use-case. The intended audience for this code pattern is developers who want to learn a new method for scanning the text across different document format and establish a relation with the data stored in the structured format in a database. The distinguishing factor of this code pattern is that it allows a configurable mechanism of search optimization which allows the recruiter to select the best fit candidate for the role.

analyze-insights-on-startup-using-watson-studio - This code pattern will demonstrate a methodology to show how we can get analytical insights and visualisations from raw data on the Web

  •    Jupyter

The World Wide Web or the "Web" is the universe of network-accessible information. All this information present in a raw format on the Web. What if you want a way to ingest raw information on the web for any given topic and provide insights and visualiations for the same. This code pattern does, just that taking an example of performing analytics on Startups. Being in the age of start-ups. There is a rapid increase in a number of companies providing skilled services. We can scrape information about such companies and evaluate their success stories based on the number of articles or live use cases appeared in news portals.

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