In this notebook, you will find the road map that I followed to build a machine learning model for a Kaggle competition. The notebook walks us through the workflow to solve the competition.
In this Project, I have used seven different stages in order to apply the Machine Learning algorithm and get valuzable insights.
- Acquire training and testing data.
- Wrangle, prepare, cleanse the data.
- Analyze, identify patterns, and explore the data.
- Model, predict and solve the problem.
- Visualize, report, and present the problem solving steps and final solution.
- Supply or submit the results.
The Problem statement have been taken from a competition site Kaggle and the final submission/result file has been uploaded/submitted against the test dataset. The question or problem definition for Titanic Survival competition is described here at Kaggle.
We may also want to develop some early understanding about the domain of our problem which is described on the Kaggle competition description page.
Here are the highlights to note.
- Out of 2224 passengers and crew, 1502 died when the Titanic sank on April 15, 1912, during her maiden voyage after striking an iceberg.
- The lack of lifeboats for the passengers and crew contributed to the shipwreck's high death toll.
- Some groups of people had a higher chance of surviving the sinking than others, such as women, children, and the upper class.
More information rekated to the Titanic Ship can be found here.