Drupal-Jedi/stepdaughter

Stepdaughter

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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.

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jdukes/cursed_pytron

A curses based tron clone I wrote for my stepdaughter and I to play when I was first learning about python classes

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Deli8t/-Optimizing-Neural-Networks-Titanic--Project

Background 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 on board, resulting in the death of 1502 out of 2224 passengers and crew. Problem Statement 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, you have been tasked to build a neural network 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). You will be required to perform feature engineering then tune your model. You can use the following guiding notebook. [Link] Hints (Feature Engineering): 1. Create a new feature title by extracting from passenger names i.e. Mrs, Miss, Mr and Master. 2. Create a new cabin number feature deck. ○ You can extract the letters before the Cabin no, which represent the deck. 3. Create a new family_size feature i.e. Parch + SibSp. 4. Create a new feature fare_per_person i.e. Fare / Family Size. Dataset Information You have been given two similar datasets that include passenger information like name, age, gender, socio-economic class, etc. One dataset is titled `train.csv` and the other is titled `test.csv`. ● 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”. ● The `test.csv` dataset contains similar information but does not disclose the “ground truth” for each passenger. The data contains features and labels: ● Features ○ Passenger Id, Passenger Class, Passenger Name, Passenger Gender, Passenger Age, No. of Siblings, spouse, parent and children related to passenger on-board, Passenger Ticket, Ticket's Fare, Passenger Cabin, Embarked (location on the ship). ● Label ○ Survived Train Dataset = https://bit.ly/31azYjb Test Dataset = https://bit.ly/2XmmAYe Dataset Glossary ● 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. Acknowledgements The dataset was sourced from Kaggle [Link]

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