https://github.com/jesussantana/feature-engineering

Learn to manage parameters with Python
https://github.com/jesussantana/feature-engineering

correlation feature-engineering normalize python scaling

Last synced: about 1 month ago
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Learn to manage parameters with Python

• Host: GitHub
• URL: https://github.com/jesussantana/feature-engineering
• Owner: jesussantana
• License: mit
• Created: 2021-04-29T12:44:47.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-06-02T14:24:31.000Z (about 5 years ago)
• Last Synced: 2025-06-08T16:04:38.794Z (about 1 year ago)
• Topics: correlation, feature-engineering, normalize, python, scaling
• Language: Jupyter Notebook
• Homepage:
• Size: 2.79 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# IT Academy - Data Science with Python

## Sprint 9: Correlation, Feature Scaling & Feature Engineering

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  

[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge&logo=Jupyter)](https://jupyter.org/try)  

[![wakatime](https://wakatime.com/badge/github/jesussantana/Feature-Engineering.svg)](https://wakatime.com/badge/github/jesussantana/Feature-Engineering)  

### Description

Learn to manage parameters with Python.

### Level 1

- Exercise 1: 

  - Grab a sports-themed dataset that you like and normalize categorical attributes in dummy. Normalize numeric attributes with StandardScaler.

  

### Level 2

- Exercise 2: 

  - Continue with the sports theme data set you like and apply the main component analysis.

### Level 3

- Exercise 3: 

  - Continue with the sports theme data set you like and normalize the data taking into account the outliers.

### Targets

- Pre-process the data by performing feature engineering

- Interpret the different concepts of feature engineering