Learn about Instance-based learning with k-Nearest Neighbor.
- [Narrator] K-nearest neighbor classification is…a supervised machine learning method that you can use…to classify instances based on the arithmetic…difference between features in a labeled data set.…In the coding demonstration for this segment,…you're going to see how to predict whether a car…has an automatic or manual transmission…based on its number of gears and carborators.…K-nearest neighbor works by memorizing observations…within a labeled test set to predict classification labels…for new, incoming, unlabeled observations.…The algorithm makes predictions based on how similar…training observations are to the new, incoming observations.…
The more similar the observation's value,…the more likely they will be classified with the same label.…Popular use cases for the k-nearest neighbor algorithm…are stock price prediction, recommendation systems,…predictive trip planning, and credit risk analysis.…The k-nearest neighbor model has a few assumptions.…Those are that the data set has little noise,…that it's labeled, that it contains only relevant features,…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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