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Predict survival on the Titanic and get familiar with ML basics Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more
Feb 12, 2023 · Dataset describing the survival status of individual passengers on the Titanic. Missing values in the original dataset are represented using ?. Float and int missing values are replaced with -1, string missing values are replaced with 'Unknown'.
Jul 31, 2024 · Raw. titanic_dataset.csv. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters. Show hidden characters. PassengerId.
Analyze the Titanic dataset to uncover insights about passenger survival using Python. Key features include data cleaning, exploratory data analysis, and visualizations with Pandas, NumPy, Matplotlib, and Seaborn. The project provides a comprehensive look into factors affecting survival rates.
last,first,gender,age,class,fare,embarked,survived Braund,Mr. Owen Harris,M,22,3,7.25,Southampton,no Cumings,Mrs. John Bradley (Florence Briggs Thayer),F,38,1,71.2833 ...
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Titanic Dataset - Train.csv will contain the details of a subset of the passengers on board (891 to be exact) and importantly, will reveal whether they survived or not, also known as the “ground truth”.
This repository contains the analysis and visualization of the Titanic dataset. The project aims to explore various factors that affected the survival rates of passengers aboard the Titanic and to build a predictive model to determine the likelihood of survival.
