No description, website, or topics provided.
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
assets
data
practice
CHANGELOG.md
README.md
knn_with_sklearn.ipynb

README.md

KNN & Classification

Unit 3: Required


Materials We Provide

Topic Description Link
Lesson K-Nearest Neighbors with Scikit-Learn Here
Solution Solution code for lesson prompts Here
Data 2015 Season Statistics for ~500 NBA Players Here
The Iris Dataset (Flowers) Here
Practice Two sample activities to practice KNN Here

Learning Objectives

After this lesson, students should be able to:

  • Utilize the KNN model on the iris data set.
  • Implement scikit-learn's KNN model.
  • Assess the fit of a KNN Model using scikit-learn.

Student Requirements

Before this lesson(s), students should already be able to:

  • Load, explore, and manipulate data using Pandas
  • Create simple visualizations with Matplotlib
  • Interpret statistical information from box and scatter plots
  • Describe the statistical meaning of an "error"

Lesson Outline

TOTAL (170 min)

  • Learning Objectives (5 min)
  • Overview of the Iris Data Set (10 min)
    • Terminology
  • Exercise: "Human Learning" With Iris Data (60 min)
  • Human Learning on the Iris Data Set (10 min)
  • K-Nearest Neighbors (KNN) Classification (30 min)
    • Using the Train/Test Split Procedure (K=1)
  • Tuning a KNN Model (30 min)
    • What Happens If We View the Accuracy of our Training Data?
    • Training Error Versus Testing Error
  • Standardizing Features (15 min)
    • Use StandardScaler to Standardize our Data.
  • Comparing KNN With Other Models (10 min)

Additional Resources

For more information on this topic, check out the following resources: