https://github.com/sevdanurgenc/data-science-reprocessing-workshop

This repo contains a workshop on data science topics.
https://github.com/sevdanurgenc/data-science-reprocessing-workshop

data-science workshop

Last synced: 8 months ago
JSON representation

This repo contains a workshop on data science topics.

• Host: GitHub
• URL: https://github.com/sevdanurgenc/data-science-reprocessing-workshop
• Owner: SevdanurGENC
• License: mit
• Created: 2022-06-29T21:48:57.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-06-29T21:55:45.000Z (about 4 years ago)
• Last Synced: 2025-03-23T20:13:44.690Z (over 1 year ago)
• Topics: data-science, workshop
• Language: Jupyter Notebook
• Homepage:
• Size: 281 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Data-Science-Reprocessing-Workshop

# End-to-End Prediction and Classification Tutorial on Tips Dataset

![A Waiter's Tips](https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/05df8cc2-4413-4a7c-93c7-dbf7991b18a7/ddxyf0d-44c7e112-9fa3-46f2-9f70-a9af10326667.png/v1/fill/w_1079,h_537,q_80,strp/a_waiters__tips_by_markdownimgmn_ddxyf0d-fullview.jpg?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOiIsImlzcyI6InVybjphcHA6Iiwib2JqIjpbW3siaGVpZ2h0IjoiPD01MzciLCJwYXRoIjoiXC9mXC8wNWRmOGNjMi00NDEzLTRhN2MtOTNjNy1kYmY3OTkxYjE4YTdcL2RkeHlmMGQtNDRjN2UxMTItOWZhMy00NmYyLTlmNzAtYTlhZjEwMzI2NjY3LnBuZyIsIndpZHRoIjoiPD0xMDc5In1dXSwiYXVkIjpbInVybjpzZXJ2aWNlOmltYWdlLm9wZXJhdGlvbnMiXX0.EXIAv4nm9B2xi2mbcZ52n58rfPGsiQeldOQ0Bj7fahY)

This tutorial includes the entire flow from raw dataset to predictions and classifications with tuning, applied on "A Waiter's Tips" dataset on Kaggle. I've conducted EDA, visualized the data, preprocessed it and applied regression and classification, evaluated the models and tuned the classification with GridSearchCV.

## Models and Data Used

-   Data: Tips and total bill paid by customers, the days and the times they come, their genders, if they smoke or not, and the size of their tables, given in tips.csv

-   Classification Methods: Logistic Regression, Decision Tree Classifier and KNN

- Regression Methods: Linear Regression

- Feature Selection: Select K-Best and Pearson Correlation

# Files

- *tips.csv* including data

- *tips.ipynb* Interactive Python Notebook that includes the code itself

## Libraries Used

    sklearn

    pandas

    seaborn

    matplotlib

## Author

-   **Dr. Nano**  - [sevdanurgenc](https://github.com/sevdanurgenc)