https://github.com/mugambi645/titanic-survival-analysis
Titanic Survival Analysis
https://github.com/mugambi645/titanic-survival-analysis
binary-classification kaggle machine-learning titanic-survival-prediction
Last synced: about 2 months ago
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Titanic Survival Analysis
- Host: GitHub
- URL: https://github.com/mugambi645/titanic-survival-analysis
- Owner: Mugambi645
- Created: 2024-12-01T22:42:00.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T21:14:45.000Z (6 months ago)
- Last Synced: 2025-02-14T04:51:12.464Z (4 months ago)
- Topics: binary-classification, kaggle, machine-learning, titanic-survival-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 448 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# titanic-survival-analysis
The Challenge
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
In this challenge, i'll try to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
I have used accuracy_score to evaluate this binary classification problem
Accuracy = TP + TN / TP + TN + FP + FN
where TP = True Positives,
TN = True Negatives,
FP = False Positives,
FN = False Negatives
Consider checking the confusion matrix for further details
You can view predictions as binary outcomes i.e 1/0 in submission.csv file