In this notebook, you will work on the Titanic dataset and use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.

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 onboard, resulting in the death of 1502 out of 2224 passengers and crew.

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 notebook, we build a predictive 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).

Download the dataset titanic.csv and place in the same directory.

libraries used : numpy , pandas , seaborn , scipy and other ML libraries

After using suitable data processing and machine learning techniques, we achieve around 93 % accuracy.