Data Science 🐍 Features

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Features are input values to regression or classification models. The features are inputs and labels are the measured outcomes. Classification predicts discrete labels (outcomes) such as yes/no, True/False, or any number of discrete levels such as a letter from text recognition. An example of classification is to suggest a movie you will want to watch next (label) based on your prior viewing history (feature). Regression is different than classification with continuous outcomes such as any floating point number in a range. An example of regression is to build a correlation of the temperature of a pan of water (label) based on the time it has been heating (feature). The temperature values are continuous while the next movie is one of many discrete options. Features are used in classification and regression to predict an outcome.

Course Modules
1. Data Science 🐍 Course Overview
2. Data Science 🐍 Import / Export
3. Data Science 🐍 Analyze
4. Data Science 🐍 Visualize
5. Data Science 🐍 Prepare Data
6. Data Science 🐍 Regression
7. Data Science 🐍 Features
8. Data Science 🐍 Classification
9. Data Science 🐍 Interpolation
10. Data Science 🐍 Solve Equations
11. Data Science 🐍 Differential Equations
12. Data Science 🐍 Time Series
Data Science 🐍 Final Project

  1. APMonitor.com
  2. Feature engineering
  3. Feature select
  4. SelectKBest
  5. Heat map
  6. data science
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