P1: Titanic Disaster

Who are the survivors? Why they survived?

Mahesha Godekere AI, TITANIC

1. Overview

The objective of this project is to follow a step-by-step walk-through, explaining each step and rationale for every decision made while executing this project. This project aims to explore the different steps while applying the tools of machine learning to predict survivors and reasoning out why they survived.

Learn organically. What does this mean? Try less depending on the black box explanation/analysis. Try understanding the concept behind the analysis or the steps. Always try manual methods! Once done, generalize and automate it. Automation results in reusable modules or libraries for next reuse. This will speed up your analysis in your future projects.

1. Exploratory Data Analysis (EDA)

Analyzing the raw data sets to summarize their main characteristics,

2. Feature Engineering

Feature engineering attempts to increase the predictive power of learning algorithms.

……..in progress

#delete onece done http://jonathansoma.com/lede/data-studio/classes/small-multiples/long-explanation-of-using-plt-subplots-to-create-small-multiples/

Reference

https://www.analyticsvidhya.com/blog/2016/10/17-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/ https://www.kaggle.com/c/titanic
https://www.kaggle.com/startupsci/titanic-data-science-solutions/notebook
https://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html https://towardsdatascience.com/play-with-data-2a5db35b279c https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features https://www.kaggle.com/jeffd23/scikit-learn-ml-from-start-to-finish
https://www.kaggle.com/helgejo/an-interactive-data-science-tutorial
https://www.kaggle.com/omarelgabry/a-journey-through-titanic
https://www.kaggle.com/sinakhorami/titanic-best-working-classifier