stepdaughter - Moozonian

Overview The data has been split into two groups: training set (train.csv) test set (test.csv) The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features. The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic. We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like. Data Dictionary VariableDefinitionKey survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton Variable Notes pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5 sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored) parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

GitHub Repo https://github.com/Suraj-Tupe/Titanic-Survival-Prediction-Using-Machine-Learning

Suraj-Tupe/Titanic-Survival-Prediction-Using-Machine-Learning

The RMS Titanic was known as the unsinkable ship and was the largest, most luxurious passenger ship of its time. Sadly, the British ocean liner sank on April 15, 1912, killing over 1500 people while just 705 survived. In this article, we will analyze the Titanic data set and make two predictions. One prediction to see which passengers on board the ship would survive and then another prediction to see if we would’ve survived. Data Set Column Descriptions pclass: Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) survived: Survival (0 = No; 1 = Yes) name: Name sex: Sex age: Age sibsp: Number of siblings/spouses aboard parch: Number of parents/children aboard fare: Passenger fare (British pound) embarked: Port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) adult_male: A male 18 or older (0 = No, 1=Yes) deck: Deck of the ship who: man (18+), woman (18+), child (<18) alive: Yes, no embarked_town: Port of embarkation ( Cherbourg, Queenstown, Southampton) class: Passenger class (1st; 2nd; 3rd) alone: 1= alone, 0= not alone ( you have at least 1 sibling, spouse, parent or child on board) age Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5 sibsp The dataset defines family relations in this way: Sibling= brother, sister, stepbrother, stepsister Spouse= husband, wife (mistresses and fiancés were ignored) parch The dataset defines family relations in this way: Parent= mother, father Child= daughter, son, stepdaughter, stepson Some children traveled only with a nanny, therefore parch=0 for them.

GitHub Repo https://github.com/Aeschautomator/Kinsley-and-Elise--Art-Supplies

Aeschautomator/Kinsley-and-Elise--Art-Supplies

a Project for my 7 year old stepdaughter & friend