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Refresh. Predict survival on the Titanic and get familiar with ML basics.
Start here! Predict survival on the Titanic and get familiar with ML basics.
Nov 24, 2021 · Tackling the Titanic Dataset with Machine Learning (Kaggle Challenge!) The Titanic dataset is a classic machine learning problem. It provides information about the passengers aboard the...
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'.
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,...
Analysis of Titanic Survival Data. Introduction ¶. The Titanic sank on April 15, 1912 during her maiden voyage. After colliding with an iceberg, 1502 of its 2224 passengers died. The data set investigated in the following sections contains detailed information about 891 passengers. The data can be found on Kaggle and be downloaded from there.
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.
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.
titanic dataset. The titanic dataset gives the values of four categorical attributes for each of the 2201 people on board the Titanic when it struck an iceberg and sank. The attributes are social class (first class, second class, third class, crewmember), age (adult or child), sex, and whether or not the person survived.
