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Refresh. Predict survival on the Titanic and get familiar with ML basics.
Apr 9, 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.
Start here! Predict survival on the Titanic and get familiar with ML basics.
Nov 25, 2021 · The legendary Kaggle problem, Titanic, based on the tragedic sinking of the RMS Titanic records data about 891 passengers of RMS Titanic, and we are required to predict if they have...
An in-depth analysis of the Titanic dataset, exploring passenger demographics, survival rates, and other key metrics using Python. This repository contains code for data acquisition, preprocessing,...
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'.
Welcome to the captivating world of Titanic dataset analysis! This repository serves as your gateway to exploring the rich insights hidden within the Titanic dataset using Python and Kaggle. Delve deep into the realm of classification techniques and machine learning algorithms - sanksarraval/Titanic-Dataset-Analysis-with-Python-and-Kaggle
The Titanic Data Science Project seeks to predict passenger survival outcomes from the infamous 1912 disaster using machine learning. Our goal is to identify key survival determinants by analyzing a dataset that includes age, gender, class, and fare, among other variables. Problems. Survival Analysis.
Titanic Passenger Data - Exploring Survival Patterns and Demographic Information.
Jan 2, 2018 · This data set contains the survival status of 1309 passengers aboard the maiden voyage of the RMS Titanic in 1912 (the ships crew are not included), along with the passengers age, sex and class (which serves as a proxy for economic status). 70% of the data was selected (using stratified sampling) for the training set.
