https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

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 on board, 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).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

Analysis of ‘Titanic Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/titanic-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

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 on board, 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).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Johar M. Ashfaque

Contents

Analysis of ‘Titanic-Dataset (train.csv)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hesh97/titanicdataset-traincsv on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Abhikalp Srivastava 15

Contents

Analysis of ‘Titanic dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ibrahimelsayed182/titanic-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Overview

This is Titanic dataset

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Titanic: Machine Learning from Disaster’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shuofxz/titanic-machine-learning-from-disaster on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Sachin Calicut

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Suresh Bhusare

Released under CC0: Public Domain

Contents

Analysis of ‘Titanic: all ones csv file’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brendan45774/gender-submisson on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The score of the csv file is 0.37799. This is the number to beat, so make sure you don't have a number below this.

Content

This is the titanic csv file, but everyone survives.

I also have another csv file: https://www.kaggle.com/brendan45774/test-file This may help you on your mission to get a perfect score.

--- Original source retains full ownership of the source dataset ---

The Titanic dataset is a popular dataset used for data analysis and machine learning tasks. It contains various information about passengers aboard the Titanic, including whether they survived or not. Here's a brief description of each of the columns:

PassengerId: A unique identifier for each passenger. Survived: Indicates whether the passenger survived or not. (0 = No, 1 = Yes) Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) Name: Name of the passenger. Sex: Gender of the passenger. Age: Age of the passenger in years. (Fractional if less than 1) SibSp: Number of siblings or spouses aboard the Titanic. Parch: Number of parents or children aboard the Titanic. Ticket: Ticket number. Fare: Fare paid for the ticket. Cabin: Cabin number. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) This dataset is often used for tasks such as predicting survival based on various factors or analyzing demographics of passengers aboard the Titanic.

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