Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description 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 challenge, 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. Practice Skills Binary classification Python and R basics

A classification approach to the machine learning Titanic survival challenge on Kaggle.Data visualisation, data preprocessing and different algorithms are tested and explained in form of Jupyter Notebooks

Applying Machine Learning Algorithms to the Kaggle "Titanic Survival Prediction Problem".

API-First approach to make Machine Learning solution usable

This program consists of clean and polished Graphical User Interface (GUI) that interacts with 8 Machine Learning models and data visualization tools through the use of different Python libraries. The user can interact with the GUI through selecting which model to run on the testing data on, which then takes them to a screen displaying the prediction results of the testing data as well as the general model accuracy. The screen also includes various buttons that, when selected, display complex and attractive data visualizations on the testing data.

Kaggle竞赛：预测泰坦尼克号中乘客的获救概率以及存活情况

My take on the Kaggle Titanic Challenge, Accuracy: 0.80681

Predicting the survival of passengers on RMS Titanic using information about the passengers.

Using Machine learning algorithm on the famous Titanic Disaster Dataset

Predicting the survival of the passenger

My implementation for Logistic Regression and applying it to different data sets.

This repo contains all of my submissions to Kaggle competitions or datasets

This repository has various machine learning projects. You can check out this repository to download machine learning projects

This repo makes use of different machine learning regression algorithms to predict the survivals of the titanic disaster.

a class assignment record

This notebook contains my submission of Titanic submission challenge on Kaggle. Feel free to suggest improvements.

Machine Learning from Titanic Disaster

Using Machine learning algorithm on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.

Ames Housing price Prediction, SAT_ACT statistical analysis,Reddit engagement using natural Language processing TF-IDF, Titanic survival predictions

Intro to machine learning using kaggle's titanic passenger list of survivors

The notebook walks us through a typical workflow for solving data science competitions at sites like Kaggle. There are several excellent notebooks to study data science competition entries. However many will skip some of the explanation on how the solution is developed as these notebooks are developed by experts for experts. The objective of this notebook is to follow a step-by-step workflow, explaining each step and rationale for every decision we take during solution development.

Some useful examples of Deep Learning (.ipynb)

Classification models on the famous Titanic Dataset along with Data Visualisation and EDA

Notebooks from my blog. meterdatascience.weebly.com

Simple Flask App with Logistic Regression model to predict Titanic survivor ⛰💥🚢

Notebook for Titanic Machine Learning from Disaster competition on Kaggle.

Jack help you to know if you would survive in the Titanic using PredictionIO