https://github.com/jesussantana/supervised-regression

Practice and become familiar with regressions
https://github.com/jesussantana/supervised-regression

lazzyregressor neural-network python random-forest randomizedsearchcv regression-models regressions supervised-regression

Last synced: 7 months ago
JSON representation

Practice and become familiar with regressions

• Host: GitHub
• URL: https://github.com/jesussantana/supervised-regression
• Owner: jesussantana
• License: mit
• Created: 2021-05-05T08:22:39.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-07-11T07:46:49.000Z (about 4 years ago)
• Last Synced: 2025-01-11T12:33:55.463Z (9 months ago)
• Topics: lazzyregressor, neural-network, python, random-forest, randomizedsearchcv, regression-models, regressions, supervised-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 62.9 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# IT Academy - Data Science with Python

## [S12 T01: Supervised Regression](https://github.com/jesussantana/Supervised-Regression/blob/main/notebooks/S12_T01_Supevised_Regression.ipynb) 

### [Github Supervised Regression](https://github.com/jesussantana/Supervised-Regression) 

[![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)  

[![Linkedin: JesusSantana](https://img.shields.io/badge/-JesusSantana-blue?style=flat-square&logo=Linkedin&logoColor=white&link=https://www.linkedin.com/in/chus-santana/)](https://www.linkedin.com/in/chus-santana/)  

[![GitHub JesusSantana](https://img.shields.io/github/followers/jesussantana?label=follow&style=social)](https://github.com/jesussantana)  

### Description

Let’s practice and become familiar with regressions

### Level 1

- Exercise 1: 

  - Create at least three different regression models to try to best predict DelayedFlights.csv flight delay (ArrDelay).

- Exercise 2: 

  - Compare them based on MSE and R2.

- Exercise 3: 

  - Train them using the different parameters they support

- Exercise 4: 

  - Compare your performance using the traint / test approach or using all data (internal validation)

### Level 2

- Exercise 5: 

  - Perform some variable engineering process to improve prediction

### Level 3

- Exercise 6: 

  - Do not use the DepDelay variable when making predictions

### Targets

- Regression models

- Regression trees

- Random Forest

- Neural Networks

- Other models