Titanic: Machine Learning

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Visualizations
5. Licensing, Authors, and Acknowledgements

Installation

All the librarires required to run the code are mentioned in requirements.txt.

To install Run: pip install -r requirements.txt

Project Motivation

To train a mahcine learning model for predicting whether a person survived or not using the information provided in the dataset. For this project, I was also interested in using the dataset to better understand the following aspects.

1. Were Females more like to survive?
2. Were people of higher socioeconomic class more likely to survive?
3. Were people with more siblings or spouses aboard less likely to survive?

File Descriptions

• Titanic.ipynb : The jupyter notebook Titanic.ipynb includes data exploration, code, machine learning model and visualizations. After comparing the accuracy of severla lgorithms I decided to use I decided to use the Gradient Boosting model with accuracy score 84.77.

• Titanic_Profile_Report.html : Profiling report of training data set.

• Data: Includes dataset provided by Kaggle for the competition

  • train.csv : Training dataset
  • test.csv : Test dataet

• Visualizations: Includes all plots generated from the training data for data exploration and analysis

  • AgeGroup-Survived.jpg : Plot of AgeGroup vs. Survived feature
  • CabinBool-Survived.jpg : Plot of CabinBool vs. Survived feature
  • Parch-Survived.jpg : Plot of Parch vs. Survived feature
  • Pclass-Survived.jpg : Plot of Pclass vs. Survived feature
  • Sex-Survived.jpg : Plot of Sex vs. Survived feature
  • SibSp-Survived.jpg : Plot of SibSp vs. Survived feature

Screenshots

Screenshot 1: Females were more likely to survive

Screenshot 2: People with more siblings or spouse aboard were less likely to survive

Screenshot 3: People with higher socioeconomic class were more likely to survive

Licensing, Authors, Acknowledgements

Author: Rahul Gupta Copyright 2020

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.