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]

Test project for yes ship it https://github.com/cornelius/yes_ship_it

A portfolio that proves I can actually ship things. Built from scratch and deployed on Vercel. Yes, it works. I tested it. Mostly.

As developers, sometimes our job is in most part refactor our own or even other people’s code. And if you are a developer for some time you probably already have been in situations where you refactor something and break everything else, the code has a comment like “When I wrote this, only God and I understood what I was doing. Now, God only knows” or “Magic. Do not touch.” Some of those comments are really funny until you have to touch and have to refactor to fix a bug or even ship a new feature. How to ensure that your changes will not make the whole code base explode? Or you will fix the bug and introduce five new others? I think the title already gave you the answer :)) Yes, Testing is the answer. All of then: Unit Testing, Integration Testing … and so on. That is one of the reasons why automated tests were created and we should care about it. As a very experienced developer said to me once "I don't trust my code, I trust my tests. I've been in this coding world long enough to know that I should rely in my test rather than my code". So how tests will help us? Testing our code will allow we to change things with confidence. Personally speaking, I don’t have enough courage to make big changes on a function or class that not have unit tests. Especially if this code is already shipped for production or for a client test. Testing you make sure that we don't publish changes that break something, especially when you have a Continuous Integration service that runs it for us. If you are working on a team or an open source project, the tests will help to ensure that you are not breaking other people code. A very important thing is that you have to rely on your tests, so we have to know if you or the person who wrote the tests for the code you will refactor, actually wrote good tests. Because the tests could pass, but if they test nothing at all, it's probably not safe to refactor.Conclusion Testing is one of the best things to guarantee the quality of our work and make we confident in improving the code base without fear of breaking what was already working. I think those are concepts that most people already have, especially if they are in this Development world for some time, but the main objective was just to reinforce how testing is important and can really help us. There are actually development methodologies such as TDD(Test Driven Development) that enforces you to implement tests. I’m not saying that tests will make your code 100% bug-free, but it will provide you more confidence and make your code better and more reliable.That’s all for this article, hope you like it If you got some comment or question, please let me know. I will be really happy in receiving your feedback. Thanks for reading :)