https://github.com/drawcodeboy/kaggle_titanic_clone

튜토리얼 클론 코딩을 통해 Kaggle에 대해 배워보는 리포지토리입니다.
https://github.com/drawcodeboy/kaggle_titanic_clone

Last synced: about 1 year ago
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튜토리얼 클론 코딩을 통해 Kaggle에 대해 배워보는 리포지토리입니다.

• Host: GitHub
• URL: https://github.com/drawcodeboy/kaggle_titanic_clone
• Owner: drawcodeboy
• Created: 2022-09-16T10:09:20.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-08T04:20:58.000Z (over 3 years ago)
• Last Synced: 2025-01-05T23:14:22.196Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 1.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Titanic - Machine Learning from Disaster

Kaggle을 배우며 Kaggle에 필요한 공부가 무엇인지 클론 코딩을 통해 배웁니다.

 

### 각 사이트 링크

>* [Titanic - Machine Learning from Disaster](https://www.kaggle.com/competitions/titanic)

>* [이유한님 블로그](https://kaggle-kr.tistory.com/17?category=868316)

>* [이유한님 유튜브](https://www.youtube.com/watch?v=_iqz7tFhox0)

### File List

* titanic-clone-eda

    + 데이터 셋을 확인하고, 데이터 간의 관계를 파악하는 EDA를 수행합니다.

* titanic-clone-fe-n-modeling

    + EDA를 통한 인사이트를 가지고서 Feature Engineering을 수행하고, 적합한 모델을 찾아 만들어냅니다.

### Process

* Dataset Check

    + Null Data Check

    + Target Label 확인

* EDA

    + Pclass

    + Sex

    + Both Sex and Pclass

    + Age

    + Pclass, Sex, Age

    + Embarked

    + Family (SibSp + Parch)

    + Cabin

* Feature Engineering

    + Fill Null data

        + Fill Null in Age using title

        + Fill Null in Embarked

    + Change Age (Continous to categorical)

    + Change Initial, Embarked and Sex (string to numerical)

    + One-hot Encoding on Initial and Embarked

    + Drop Columns

* Building machine learning model and perdiction using the trained model

    + Preparation - Split dataset into train, vaild, and test set

    + Model generation and prediction

    + Feature importance

* Conclusion