https://github.com/husna-poyraz/titanic-machine-learning
Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
https://github.com/husna-poyraz/titanic-machine-learning
data data-analysis data-science data-visualization deep-learning machine-learning missing-data outlier-detection python titanic
Last synced: 11 days ago
JSON representation
Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
- Host: GitHub
- URL: https://github.com/husna-poyraz/titanic-machine-learning
- Owner: Husna-POYRAZ
- Created: 2021-07-17T08:49:41.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-06T14:27:12.000Z (about 3 years ago)
- Last Synced: 2025-01-09T04:21:52.203Z (9 months ago)
- Topics: data, data-analysis, data-science, data-visualization, deep-learning, machine-learning, missing-data, outlier-detection, python, titanic
- Language: Jupyter Notebook
- Homepage:
- Size: 81.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Titanic
Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
# Introduction
The sinking of Titanic is one of the most notorious shipwrecks in the history. In 1912, during her voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.
# Content:
1. Load and Check Data
2. Variable Description
* Univariate Variable Analysis
. Categorical Variable
. Numerical Variable
3. Basic Data Analysis
4. Outlier Detection
5. Missing Value
* Find Missing Value
* Fill Missing Value
6. Visualization
* Correlation Between Sibsp -- Parch -- Age -- Fare -- Survived
* SibSp -- Survived
* Parch -- Survived
* Pclass -- Survived
* Age -- Survived
* Pclass -- Survived -- Age
* Embarked -- Sex -- Pclass -- Survived
* Embarked -- Sex -- Fare -- Survived
* Fill Missing: Age Feature
7. Feature Engineering
* Name -- Title
* Family Size
* Embarked
* Ticket
* Pclass
* Sex
* Drop Passenger ID and Cabin
8. Modeling
* Train - Test Split
* Simple Logistic Regression
* Hyperparameter Tuning -- Grid Search -- Cross Validation
* Ensemble Modeling
Prediction and Submission