Titanic Traveller Survivability Prediction

Overview

Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

The data is sourced from the Kaggle Titanic dataset. The data is split into two groups:

  1. Training Set
  2. Testing Set

Data Dictionary

Variable Description Key
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.

Requirements

  • Pandas
  • Scikit-Learn
  • Matplotlib
  • Numpy
Owner
John Phillip
Software developer, main tools are C++, Python, and SQL. Interested in Robotics, A.I, and Machine Learning
John Phillip
An AutoML survey focusing on practical systems.

This project is a community effort in constructing and maintaining an up-to-date beginner-friendly introduction to AutoML, focusing on practical systems. AutoML is a big field, and continues to grow

AutoGOAL 16 Aug 14, 2022
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

Igor Ivanov 671 Dec 25, 2022
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

2.3k Jan 05, 2023
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

Victor Umunna 7 Oct 13, 2022
This handbook accompanies the course: Machine Learning with Hung-Yi Lee

This handbook accompanies the course: Machine Learning with Hung-Yi Lee

RenChu Wang 472 Dec 31, 2022
Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.

formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations li

34 Dec 21, 2022
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
Made in collaboration with Chris George for Art + ML Spring 2019.

Deepdream Eyes Made in collaboration with Chris George for Art + ML Spring 2019.

Francisco Cabrera 1 Jan 12, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET Can a machine learning project be implemented to estimate the salaries of baseball players whos

Pinar Oner 7 Dec 18, 2021
Python package for causal inference using Bayesian structural time-series models.

Python Causal Impact Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalI

Thomas Cassou 219 Dec 11, 2022
Real-time stream processing for python

Streamz Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelin

Python Streamz 1.1k Dec 28, 2022
Lingtrain Alignment Studio is an ML based app for texts alignment on different languages.

Lingtrain Alignment Studio Intro Lingtrain Alignment Studio is the ML based app for accurate texts alignment on different languages. Extracts parallel

Sergei Averkiev 186 Jan 03, 2023
Basic Docker Compose for Machine Learning Purposes

Docker-compose for Machine Learning How to use: cd docker-ml-jupyterlab

Chris Chen 1 Oct 29, 2021
Conducted ANOVA and Logistic regression analysis using matplot library to visualize the result.

Intro-to-Data-Science Conducted ANOVA and Logistic regression analysis. Project ANOVA The main aim of this project is to perform One-Way ANOVA analysi

Chris Yuan 1 Feb 06, 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

23.3k Dec 31, 2022
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

Christoph Mark 129 Dec 24, 2022
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan

Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.

Abid Ali Awan 1 Dec 31, 2021
A Python implementation of GRAIL, a generic framework to learn compact time series representations.

GRAIL A Python implementation of GRAIL, a generic framework to learn compact time series representations. Requirements Python 3.6+ numpy scipy tslearn

3 Nov 24, 2021