Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Overview

Kaggle-titanic

This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this repository is to provide an example of a competitive analysis for those interested in getting into the field of data analytics or using python for Kaggle's Data Science competitions .

Quick Start: View a static version of the notebook in the comfort of your own web browser.

Installation:

To run this notebook interactively:

  1. Download this repository in a zip file by clicking on this link or execute this from the terminal: git clone https://github.com/agconti/kaggle-titanic.git
  2. Install virtualenv.
  3. Navigate to the directory where you unzipped or cloned the repo and create a virtual environment with virtualenv env.
  4. Activate the environment with source env/bin/activate
  5. Install the required dependencies with pip install -r requirements.txt.
  6. Execute ipython notebook from the command line or terminal.
  7. Click on Titanic.ipynb on the IPython Notebook dasboard and enjoy!
  8. When you're done deactivate the virtual environment with deactivate.

Dependencies:

Kaggle Competition | Titanic Machine Learning from Disaster

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.

One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.

In this contest, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.

This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning."

From the competition homepage.

Goal for this Notebook:

Show a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.

This Notebook will show basic examples of:

Data Handling

  • Importing Data with Pandas
  • Cleaning Data
  • Exploring Data through Visualizations with Matplotlib

Data Analysis

  • Supervised Machine learning Techniques: + Logit Regression Model + Plotting results + Support Vector Machine (SVM) using 3 kernels + Basic Random Forest + Plotting results

Valuation of the Analysis

  • K-folds cross validation to valuate results locally
  • Output the results from the IPython Notebook to Kaggle

Benchmark Scripts

To find the basic scripts for the competition benchmarks look in the "Python Examples" folder. These scripts are based on the originals provided by Astro Dave but have been reworked so that they are easier to understand for new comers.

Competition Website: http://www.kaggle.com/c/titanic-gettingStarted

Comments
  • output file

    output file "data/output/logitregres.csv" contains the survived values other than {0,1}

    Thanks for the great article and code. I see that direct submission of output file to kaggle results in error and it says Survived column values must be either 0 or 1.

    Am I missing something? Should I have a cutoff and turn them in to 0 or 1?

    opened by srini09 2
  • Fixed issue with bar chart

    Fixed issue with bar chart

    If auto-sorting is on (as per default), the returned series object is sorted by values, i.e. for „male“ the not-survived category is reported first and for „female“ the survived. When summing over male and female, the categories get mixed up.

    opened by metatier 2
  • Adds the updated csv files with capitalied column names. Fixed the iPyth...

    Adds the updated csv files with capitalied column names. Fixed the iPyth...

    ...on Notebook so it works with capitalized column headers. Updated the data folder with the two new csv files (train and test) as well as output/logitregres.csv.

    opened by thearpitgupta 2
  • Column headers are now capitalized

    Column headers are now capitalized

    Looks like column headers in the training data set are now capitalized. See here http://www.kaggle.com/c/titanic-gettingStarted/download/train.csv It's not capitalized in the data set used used in the repo https://github.com/agconti/kaggle-titanic/blob/master/data/train.csv Wonder if Kaggle changed the data set and intentionally made this change. Anyways, if you want I am happy to submit a PR that works with capitalized column names. Let me know. Thanks.

    PS - Great work.

    opened by thearpitgupta 2
  • sharey for subplots

    sharey for subplots

    Not sure if you're original intention was to show the Y axis for all your subplots in input 14 but if it wasn't you can pass in sharey=True into df.plot() function to eliminate the redundant axes.

    example

    Awesome work on the notebook btw!

    opened by zunayed 2
  • Install KaggleAux through pip

    Install KaggleAux through pip

    Currently, a subsection of KaggleAux is included in this repository as a temporary connivence. It would be cleaner to have KaggleAux as a 3rd party dependency installed through pip. This would be less confusing to users, and would allow updates in KaggleAux to be better incorporated.

    enhancement 
    opened by agconti 1
  • Categorization of algorithms

    Categorization of algorithms

    The README and several places in notebook categorize SVM and Random Forest into "Unsupervised Learning". They actually belong to "Supervised Learning".

    e.g. http://cs229.stanford.edu/notes/cs229-notes3.pdf

    opened by hupili 1
  • Suggestion -- update requirements.txt

    Suggestion -- update requirements.txt

    Hi, I don't know if this repo is still maintained, but would be nice to update the requirements.txt with supported versions.

    :+1: Thanks for putting this repo together.

    opened by DaveOkpare 0
  • Update agc_simp_gendermodel.py

    Update agc_simp_gendermodel.py

    data indexing was inappropriate for the operation taking care Lines #18,#19 index 3 , we have Name of passenger but not gender , so all the time we'll get false
    Lines #26,#27,#28 , proportions should be calculated on Survived column , not on PassengerId

    opened by praveenbommali 0
  • why use barh and ylim

    why use barh and ylim

    I don't understand the need of using barh and ylim functions in plotting.Simple vertical graphs are easier to understand then what is the purpose of using barh.And Thank you for sharing this notebook it's really informative.

    opened by barotdhrumil21 0
Releases(v0.2.0)
Supercharging Imbalanced Data Learning WithCausal Representation Transfer

ECRT: Energy-based Causal Representation Transfer Code for Supercharging Imbalanced Data Learning With Energy-basedContrastive Representation Transfer

Zidi Xiu 11 May 02, 2022
Implementation of the paper "Shapley Explanation Networks"

Shapley Explanation Networks Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimenta

68 Dec 27, 2022
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Salesforce 1.3k Dec 31, 2022
Cancer metastasis detection with neural conditional random field (NCRF)

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

Baidu Research 731 Jan 01, 2023
Source code for paper "Deep Diffusion Models for Robust Channel Estimation", TBA.

diffusion-channels Source code for paper "Deep Diffusion Models for Robust Channel Estimation". Generic flow: Use 'matlab/main.mat' to generate traini

The University of Texas Computational Sensing and Imaging Lab 15 Dec 22, 2022
Event-forecasting - Event Forecasting Algorithms With Python

event-forecasting Event Forecasting Algorithms Theory Correlating events in comp

Intellia ICT 4 Feb 15, 2022
HyperCube: Implicit Field Representations of Voxelized 3D Models

HyperCube: Implicit Field Representations of Voxelized 3D Models Authors: Magdalena Proszewska, Marcin Mazur, Tomasz Trzcinski, Przemysław Spurek [Pap

Magdalena Proszewska 3 Mar 09, 2022
MoveNet Single Pose on OpenVINO

MoveNet Single Pose tracking on OpenVINO Running Google MoveNet Single Pose models on OpenVINO. A convolutional neural network model that runs on RGB

35 Nov 11, 2022
Contains a bunch of different python programm tasks

py_tasks Contains a bunch of different python programm tasks Armstrong.py - calculate Armsrong numbers in range from 0 to n with / without cache and c

Dmitry Chmerenko 1 Dec 17, 2021
A python/pytorch utility library

A python/pytorch utility library

Jiaqi Gu 5 Dec 02, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv This repository contains python scripts for tr

12 Dec 12, 2022
Efficient 3D Backbone Network for Temporal Modeling

VoV3D is an efficient and effective 3D backbone network for temporal modeling implemented on top of PySlowFast. Diverse Temporal Aggregation and

102 Dec 06, 2022
JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

JAX bindings to FINUFFT This package provides a JAX interface to (a subset of) the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) lib

Dan Foreman-Mackey 32 Oct 15, 2022
Unsupervised Pre-training for Person Re-identification (LUPerson)

LUPerson Unsupervised Pre-training for Person Re-identification (LUPerson). The repository is for our CVPR2021 paper Unsupervised Pre-training for Per

143 Dec 24, 2022
High-Resolution 3D Human Digitization from A Single Image.

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020) News: [2020/06/15] Demo with Google Colab (i

Meta Research 8.4k Dec 29, 2022
Face Recognition and Emotion Detector Device

Face Recognition and Emotion Detector Device Orange PI 1 Python 3.10.0 + Django 3.2.9 Project's file explanation Django manage.py Django commands hand

BootyAss 2 Dec 21, 2021
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

InferPy: Deep Probabilistic Modeling Made Easy InferPy is a high-level API for probabilistic modeling written in Python and capable of running on top

PGM-Lab 141 Oct 13, 2022
WSDM2022 Challenge - Large scale temporal graph link prediction

WSDM 2022 Large-scale Temporal Graph Link Prediction - Baseline and Initial Test Set WSDM Cup Website link Link to this challenge This branch offers A

Deep Graph Library 34 Dec 29, 2022
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real Time Video Interpolation arXiv | YouTube | Colab | Tutorial | Demo Table of Contents Introduction Collection Usage Evaluation Training and

hzwer 3k Jan 04, 2023