Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics

Using Machine learning algorithm on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.

Titanic Survival prediction: Titanic dataset- how many people survive and how many were Male and Female

Machine Learning from Titanic Disaster

This repository contains all the projects/case studies done using Machine Learning methods. This is in conjunction with another repository. Difference being that R would be the main software used here

My approach in one of the most classic data analysis projects ever

Kaggle Competition Question

This project aims to predict the survival of passengers aboard the Titanic using the Naive Bayes classifier algorithm. The dataset used in this project contains information about Titanic passengers, such as their age, gender, passenger class, and other relevant features.

This project focuses on Exploratory Data Analysis (EDA) to identify the key determinants that influenced survival during the infamous Titanic accident.

Exploratory Data Analysis on Titanic Survivor Dataset provided by Kaggle.

A multidimensional ML project inspired by M-theory. Explores the Titanic dataset through structured preprocessing and visualization. Trains and optimizes multiple models to predict passenger survival. Includes an interactive Streamlit app for individual and batch predictions. Designed for users & theory enthusiasts exploring hidden data patterns.

Data analysis and Machine learning on titanic data

Detailed Exploratory Data Analysis (EDA) of the Titanic dataset.

A data-driven analysis and machine learning model to predict passenger survival in the Titanic disaster using Python, Pandas, and Scikit-learn.

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Titanic Survival Data EDA and Simple Predictions Based on EDA

Analyze the Titanic dataset to extract meaningful results.

How did passenger class affect survival on the Titanic?

Data Analysis on the RMS Titanic data set using Python

This repository provides a complete solution to Udacity's Machine Learning Project "Titanic_Survival_Exploration".

In this code we will predict survived for the tragic accident Titanic. It's a Kaggle competition.

titanic dataset

So I decide to work through the insight of Titanic tragedy with a glance at the Data (famously) provided on Kaggle.

Data Science internship at Asterisc Technocrat Pvt. Ltd. Task - Billioniaire Analysis, Covid - 19 Data Analysis, Titanic Survival Prediction

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Through an analysis of a dataset on the titanic, I seek to dig deeper into this tragedy by answering the broad question: What factors helped someone survive the sinking of the titanic?

Explore Titanic survival data by implementing a decision tree in sci-kit-learn

Using Deep Learning to create a model that predicts which passengers survived the Titanic shipwreck.

This is a project which predict the possibility of a passenger in the Titanic ship surviving or not. Information can be found on the website listed