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

Let’s practice and become familiar with classification algorithms.
https://github.com/jesussantana/supervised-classification

automl automl-algorithms classification-algorithms classification-models classification-trees knn-classifier lazzyclassifier logistic-regression python support-vector-machine xgboost

Last synced: 2 months ago
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Let’s practice and become familiar with classification algorithms.

• Host: GitHub
• URL: https://github.com/jesussantana/supervised-classification
• Owner: jesussantana
• License: mit
• Created: 2021-05-24T15:54:34.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-29T14:34:22.000Z (almost 4 years ago)
• Last Synced: 2025-01-11T12:33:34.393Z (4 months ago)
• Topics: automl, automl-algorithms, classification-algorithms, classification-models, classification-trees, knn-classifier, lazzyclassifier, logistic-regression, python, support-vector-machine, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 31.1 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# [IT Academy - Data Science with Python](https://www.barcelonactiva.cat/es/itacademy)

## [S13 T01: Supervised Classification](https://github.com/jesussantana/Supervised-Classification/blob/main/notebooks/S13_T01_Supervised_Classification.ipynb)

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

### Description

Let’s practice and become familiar with classification algorithms.

### Level 1

- Exercise 1: 

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

- Exercise 2: 

  - Creates a new variable depending on whether the flight arrived late or not (ArrDelay> 0).

- Exercise 3: 

  - Compare classification models using accuracy, a confidence matrix, and other more advanced metrics.

- Exercise 4: 

  - Train them using the different parameters they support.  

- Exercise 5: 

  - 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

- Classification trees

- KNN - k-Nearest Neighbors

- Logistic Regression

- Support Vector Machine

- XGboost