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

Infamous Titanic survial prediction competition on Kaggle

Example code for solving the Titanic prediction problem found on Kaggle.

Titanic Survival Prediction Using Machine Learning is a tool that uses data to guess if passengers survived the Titanic sinking. It looks at factors like age, gender, and ticket class to make its guesses. The goal is to understand what influenced survival rates on the ship.

Titanic Survival Prediction Web App built using Machine Learning and Streamlit.

View the predictions of a neural network while it is being trained on the titanic dataset.

Titanic prediction workshop - ML studio with PowerBI/shiny on Azure via

The Titanic Survival Prediction project uses the Naïve Bayes algorithm to predict whether a passenger would survive the Titanic disaster based on features such as age, gender, passenger class, and fare.

Titanic Survival Prediction

Applying Machine Learning Algorithms to the Kaggle "Titanic Survival Prediction Problem".

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

Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN. The best accuracy score was from Random Forest: 84.35%

Meta-Model Approach for Chaos Prediction Using the Titanic Dataset.

This repository is generated in response of beginner level competition posted on Kaggle for survivors prediction from Titanic incident.

Survival prediction using Titanic dataset and Logistic Regression

Project for Titanic survival prediction, achieving 83.28% accuracy through advanced feature engineering, hyperparameter optimization, and ensemble methods.

The Titanic Prediction project uses Python and machine learning to predict passenger survival based on demographic and travel features.

The Titanic: Machine Learning from Disaster competiton. 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 and 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

Titanic-Machine-Learning-from-Disaster This repository contains a machine learning project for predicting survival of passengers who travelled on Titanic Ship in 1912. Problem Description- This project highlights my approach to the introductory machine learning competition on Kaggle website- Titanic: Machine Learning from Disaster [1]. 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. This project analyses which people were likely to survive. In particular, tools of machine learning have been used to predict which passengers survived the tragedy. Project Description This project has been made in Python v3.4. It uses various data processing, visualisation and machine learning packages such as numpy, pandas, matplotlib, scikit-learn etc. which should be installed if the code is run on a local machine. The project uses a 5 step process (general procedure) for it's predicting task which is as follows [2]: Perform a statistical analysis of the data and look over it's characteristics such as data type of columns, number of instances, correlation of each attribute with the output variable, finding mean and other information about data, correlation matrix etc. After performing statistical analysis, do a visual analysis by plotting the data. Do analyse the scatter_matrix, plot box plots etc. so as to know which attributes are relevant and which are not. Remove irrelevant attributes from the dataset for further analysis. Make a list of all machine learning algorithms that can give good prediction results and spot check each one of them (apply each one of them on the dataset) to find which one is better for prediction. Use k-fold cross validation to calculate performance characteristics of each of the learners (accuracy, precision, recall, area under ROC curve etc.). Take some of the good performing algorithms and perform a grid search/ randomised search over it's hyperparameters to find the optimal hyperparameters for the prediction task. Ensure that the optimal hyperparameters do not overfit the data, by performing k-fold cross validations on learners using these tuned hyperparametes as well. Use an ensemble or Voting Classifier on the above selected algorithms to achieve better performance or use any one of the above algorithm directly to perform predictions. Keep iterating over the above steps again and again and tune them according to the need so as to achieve better performance. File Description titanic_predictor - contains python code for predicting survival. my_solution.csv - contains sample output file generated from algorithm. train.csv- contains training data test.csv - contains testing data for making predictions readme.md - for guide to this project.

Data being provided of people travelling on titanic , analysis done using matplotlib and seaborn libraries along with pandas manipulation , finally a particular machine learning model after comparison is trained to obtain maximum accuracy on the data which is formerly cleaned and converted to be trained and at last the survival of a person is predicted based on the trained model .

Django Based Titanic Dataset ML Prediction

In this we will be analyzing the machine learning model predictions on the titanic dataset

Titanic survival prediction — Random Forest classifier with feature engineering (82.7% accuracy)

this is a titanic survived predictions with the help of machine learning deployed with streamlit as a webpage

The aim of the project was to predict which passengers survived the Titanic Disaster. The type of machine learning we will be doing is called classification, because when we make predictions we are classifying each passenger as ‘survived’ or not.

The aim of this project is to predict whether a passenger would survive the Titanic disaster based on various features such as their class, age, sex, and other relevant data. The project utilizes a Decision Tree Classifier, a machine learning model, to make these predictions.

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TITANIC SURVIVAL PREDICTION

PHP script for prediction titanic dataset (ml from disaster)

Titanic survival prediction using machine learning and google colab