Demonstrates basic data munging, analysis, and visualization techniques. RMS Titanic was the largest ship afloat at the time it entered service and was the second of three Olympic-class ocean liners operated by the White Star Line. Then do the predictions on test data and submit to Kaggle.Then for all the records with missing age, based on their Sex,Title and Pclass we assign the age. Currently hosted here, (currently inactive) it can run and save some Machine Learning models on the cloud. X-axis = Age ,Y-axis = Ticket_Fare ,Green dots = Survived, Red dots= DiedSmall green dots between x=0 & x=10 : Children who were survivedSmall red dots between x=10 & x=45: Adults who died (from a lower classes)Large green dots between x=20 & x=45 : Adults with larger ticket fares who are survived.I have combined the train and test data to apply the transformations on both. Use Git or checkout with SVN using the web URL.

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.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. And finally train the model on complete train data. We will cover an easy solution of Kaggle Titanic Solution in python for beginners. Kaggle Titanic Machine Learning from Disaster is considered as the first step into the realm of Data Science. ramansah/kaggle-titanic. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Feature engineering is an art and one of the most exciting things in the broad field of machine learning. I really enjoy to study the Kaggle subforums to explore all the great ideas and creative approaches. Not sure how passenger Id is contributing to the prediction. Kaggle Titanic problem is the most popular data science problem. 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. 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 - yangvnks/titanic-classification 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. github.com. This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. There were an estimated 2,224 passengers and crew aboard, and more than 1,500 died, making it one of the deadliest commercial peacetime maritime disasters in modern history. A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Below is the graphical representation of the same.Finally to train the model again with one last time on entire training data I have only included the features whose importance is more than 0.01. 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 .The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. Kaggle-titanic. 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. Demonstrates basic data munging, analysis, and visualization techniques. Below are the features provided in the Test dataset.From the below table we can see that out of 891 observations in the test dataset only 714 records have the Age populated .i.e around 177 values are missing. Women on the other hand survived more than men, comparatively well on all the age groups.When we plot the ticket fare of passengers who are survived/dead, we can see that the passengers with cheaper ticket fares are more likely to die.

We need to impute this with some values, which we can see later.Chart below says that more male passengers are died compared to females (Gender discrimination :-)))And also we can see men who are between 20 to 40 are survived more compared to older aged men as depicted by the green histogram. Shows examples of supervised machine learning techniques. And after training i could see a slight improvement in the score, this time it is Then I ran the model on the test data, extracted the predictions and submitted to the Kaggle. When examining the event that led to the sinking of the Titanic, it’s a tragedy with so many lives lost. Contribute to kaggle-titanic development by creating an account on GitHub. Thanks a lot for reading and please let me know your comments, thoughts & suggestions if any.Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. This is a binary classification problem for the titanic dataset.

I have tried other algorithms like Logistic … This is the most recommend challenge for data science beginners. The problem statement for this challenge is to predict passenger survival or not survival. 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… Exploration.

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