Data Science Project -Predicting survival on the Titanic

Data Science Project -Predicting survival on the Titanic

In this data science project with Python, we will complete the analysis of what sorts of people were likely to survive.You will learn to use various machine learning tools to predict which passengers survived the tragedy.


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Shailesh Kurdekar

Solutions Architect at Capital One

I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More


Lead Consultant, ITC Infotech

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

What will you learn

Understanding the problem statement
Importing the dataset and importing libraries
Performing basic EDA and checking for null values
Imputing the null values filling them using appropriate method
Statistics summaries using describe function
Encoding categorical variables
Using graphs to visualize data visually
Using groupby() function method to visualize the relationship between different data
How to use pivot tables
Understanding Overfitting and Cross Validation
Applying Logistic Regression for modeling
Using Cross Folds Validation to prevent overfitting
Making predictions for the test dataset

Project 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.This data science project will give you introdcution on how to use Python to apply various machine learning techniques  to the RMS Titanic dataset and predict which passenger would have survived the tragedy.

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Curriculum For This Mini Project

04h 12m