https://github.com/chinmayrane16/titanic-survival-in-depth-analysis

Used Pandas , Matplotlib , Seaborn libraries to Analyze , Visualize and Explore the data of people travelling on Titanic, and Used Scikit-learn Modelling Algorithms to predict their probability of Survival.
https://github.com/chinmayrane16/titanic-survival-in-depth-analysis

classification-model data-cleaning data-visualization feature-engineering matplotlib numpy pandas seaborn

Last synced: 3 months ago
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Used Pandas , Matplotlib , Seaborn libraries to Analyze , Visualize and Explore the data of people travelling on Titanic, and Used Scikit-learn Modelling Algorithms to predict their probability of Survival.

• Host: GitHub
• URL: https://github.com/chinmayrane16/titanic-survival-in-depth-analysis
• Owner: Chinmayrane16
• Created: 2018-07-04T16:55:30.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-10-29T09:38:48.000Z (about 5 years ago)
• Last Synced: 2024-10-10T19:10:14.303Z (3 months ago)
• Topics: classification-model, data-cleaning, data-visualization, feature-engineering, matplotlib, numpy, pandas, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 510 KB
• Stars: 12
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Titanic-Survival-In-Depth-Analysis

**Problem Statement**

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 challenge, we ask you to 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).

**The Titanic: Machine Learning from Disaster competiton (Overview)**

The purpose of this project was to use DL and ML implementations on a dataset to help and analyise a titanic survival scenario.

* With data being provided of varoius passengers traveling on the ship I have used libraries like _Numpy , Pandas_ to manipulate , explore and analyze the data.

* Also used Libraries like _Matplotlib and Seaborn_ to Visualise the data. 

* Lastly I have used various machine learning models to make predictions on the formerly cleaned and preprocessed data. 

* Then I used _GridSearchCV_ to optimise the parameters of the various models.