https://github.com/amatofrancesco99/titanic-classifier

A binary classifier, using logistic regression, to predict whether a person, given its information, would have survived to the Titanic’s disaster.
https://github.com/amatofrancesco99/titanic-classifier

classification logistic-regression machine-learning titanic-survival-prediction

Last synced: 3 months ago
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A binary classifier, using logistic regression, to predict whether a person, given its information, would have survived to the Titanic’s disaster.

• Host: GitHub
• URL: https://github.com/amatofrancesco99/titanic-classifier
• Owner: Amatofrancesco99
• License: mit
• Created: 2022-03-27T12:12:14.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-27T13:05:54.000Z (about 3 years ago)
• Last Synced: 2025-01-23T03:44:36.333Z (5 months ago)
• Topics: classification, logistic-regression, machine-learning, titanic-survival-prediction
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Titanic-Classifier

![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)

![ForTheBadge built-with-love](http://ForTheBadge.com/images/badges/built-with-love.svg)


![](https://komarev.com/ghpvc/?username=TitanicClassifier&color=green&style=for-the-badge&label=PAGE+VIEWS)

## Main Goal

A binary classifier to predict whether a person would have survived, or not, to the Titanic’s disaster.

## Dataset Description

Training set (710 samples), and testing set (177 samples).




Each dataset row represents a specific passenger’s information (predictors/features), such as:

ticket class; gender; age; number of siblings and spouses aboard; number of parents and children

aboard; passenger fare.




Finally, is also known whether the person survived or not (target variable).

## Prediction

Are you interested in knowing which would have had your probability of surviving? 




Change the `my_info` values into the [analysis.py](./scripts/analysis.py) file, then run the script.

## Overall Performances: 

* *Training*: 80.14% accuracy

* *Testing*: 78.53% accuracy

*** 

### Scatterplot showing the distribution of the two classes in the plane defined by the two most influential features



As shown in the upper scatterplot, the females are more likely to survive than males, while if the ticket class is low (1st class) the probability of surviving increases.




The feature which discriminates more the probability of surviving is the gender.