Know Thy Neighbor: Scikit and the K-Nearest Neighbor Algorithm

*
Accepted Session
Short Form
Intermediate
Scheduled: Wednesday, June 25, 2014 from 4:45 – 5:30pm in B301

Excerpt

This presentation will give a brief overview of machine learning, the k-nearest neighbor algorithm and Scikit-learn. Sometimes developers need to make decisions, even when they don't have all of the required information. Machine learning attempts to solve this problem by using known data (a training data sample) to make predictions about the unknown. For example, usually a user doesn't tell Amazon explicitly what type of book they want to read, but based on the user's purchasing history, and the user's demographic, Amazon is able to induce what the user might like to read.

Description

Portia Burton will show how you can practically use Python’s machine learning package to conduct classifications, and predictive analysis. She will demonstrate how beginners can take advantage of IPython and Scikit-learn to create small but robust applications.

Tags

Scikit-learn, python, kNN

Speaking experience

Portia has spoken at Portland Python User Group, Hack the People, and the Portland Python User Group. She has also presented at PyCon 2014.

Speaker

  • Portia Burton

    PLB Analytics

    Biography

    Portia Burton is the founder of PLB Analytics, a company which uses data to solve practical business problems. She is also the organizer of the Portland Data Science Group, a ragtag club of data visualization and data mining nerds. Portia loves poking around with Pandas, Scikit-learn and building nifty one-off apps with Flask. When she is not in front of her computer she enjoys impromptu yoga in the park.

    Sessions

      • Title: Know Thy Neighbor: Scikit and the K-Nearest Neighbor Algorithm
      • Track: Cooking
      • Room: B301
      • Time: 4:455:30pm
      • Excerpt:

        This presentation will give a brief overview of machine learning, the k-nearest neighbor algorithm and Scikit-learn. Sometimes developers need to make decisions, even when they don’t have all of the required information. Machine learning attempts to solve this problem by using known data (a training data sample) to make predictions about the unknown. For example, usually a user doesn’t tell Amazon explicitly what type of book they want to read, but based on the user’s purchasing history, and the user’s demographic, Amazon is able to induce what the user might like to read.

      • Speakers: Portia Burton