Topic: titanic-survival Goto Github
Some thing interesting about titanic-survival
Some thing interesting about titanic-survival
titanic-survival,Detailed Exploratory Data Analysis (EDA) of the Titanic dataset.
User: aiza-d
titanic-survival,Using Machine learning algorithm on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.
User: amberkakkar01
titanic-survival,This short project take the Titanic Survival Data Set and Analyze it.
User: anavgupta
titanic-survival,Visualization of the Titanic DataSet
User: anavgupta
titanic-survival,Prediction of Survivors from titanic data set.
User: ankit152
Home Page: https://www.kaggle.com/c/titanic/data
titanic-survival,🚢 Association and Pattern Recognition Algorithms on data from Titanic survivors.
User: arhcoder
Home Page: https://www.kaggle.com/competitions/titanic
titanic-survival,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
User: ashishpatel26
Home Page: https://www.kaggle.com/ashishpatel26/titanic-passenger-survival-analysis
titanic-survival,Analysis of what sorts of people were likely to survive the titanic disaster.
User: ashishsardana
Home Page: https://www.kaggle.com/c/titanic
titanic-survival,Surviving the Titanic step-by-step with groups
User: davidtvs
titanic-survival,Prediction of survival onboard the Titanic (Titanic dataset) using Machine Learning (Python)
User: dwadh
titanic-survival,Analysing Titanic dataset using Apache SparkML on a Docker container
User: edlavr
titanic-survival,This repo guides you to to build predictive models of Titanic survival, including data-viz & pre-processing, feature analysis, building predictive models and performance evaluation.
User: faisal-irzal
titanic-survival,Udacity Project 2 - Titanic Data Analysis
User: fengqy04
titanic-survival,deployment of machine learning models
User: fernandezfran
Home Page: https://www.udemy.com/certificate/UC-e1621cc2-3b9d-459b-8c13-fcf9c9353582/
titanic-survival,In this code we will predict survived for the tragic accident Titanic. It's a Kaggle competition.
User: franklaercio
Home Page: https://www.kaggle.com/c/titanic
titanic-survival,LITG project
User: gambani-simran
titanic-survival,Contains project work for Udacity's Data Analyst Nanodegree from the September 2017 cohort
User: gouravaich
titanic-survival,Explore Titanic survival data by implementing a decision tree in sci-kit-learn
User: gouravaich
titanic-survival,Problem to solve: On the Titanic disaster predict who would survive based on 4 features: sex, age, fare and class.
User: guevara-erik
titanic-survival,Kaggle Competition - Titanic: Machine Learning from Disaster (Top 8%)
User: huspark
Home Page: https://www.kaggle.com/c/titanic
titanic-survival,Udacity - Machine Learning Engineer Nanodegree Project - Titanic Survival Exploration
User: joelselvaraj
Home Page: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009
titanic-survival,Predict the survival rate and Chances get Survived on Titanic using Machine learning.
User: kanishksh4rma
titanic-survival,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.
User: kshitizrohilla
titanic-survival,Titanic Survival Exploration & Prediction - 1st project for Udacity's Machine Learning Nanodegree
User: lmego
titanic-survival,TOP13% solution for the Titanic-Kaggle competition using a Gradient Boosting Classifier. Moreover, implementation of a Streamlit App to play with the models.
User: marcpaulo15
titanic-survival,Some useful examples of Deep Learning (.ipynb)
User: mickey0521
titanic-survival,Investigate Titanic Dataset Using iPython Notebook, Pandas, Matplotlib, and Seaborn
User: missmariss31
titanic-survival,Titanic Survival Prediction using Logistic Regression
User: nano-bot01
titanic-survival,Analyze the Titanic dataset to extract meaningful results.
User: nikhilmanglik
titanic-survival,Investigating Titanic Dataset as MLFND Project
User: piyush2896
Home Page: https://in.udacity.com/course/machine-learning-engineer-nanodegree--nd009-in-basic/
titanic-survival,Udacity Machine Learning Nano degree Program Project Predicting Passenger Survival
User: prateekiiest
titanic-survival,In this repository, I uploaded all the projects/tasks in Data science Internship at Bharat Intern.
User: sanketyedle
titanic-survival,This Repository contains the Titanic Survival Project created by using Logistic Regression in Machine Learning.
User: sidgolangade
titanic-survival,
User: soumyasingh05
titanic-survival,This repository shows the Passenger Survival Prediction of titanic dataset.
User: subhamyadav580
titanic-survival,Titanic Survival prediction: Titanic dataset- how many people survive and how many were Male and Female
User: tamanna18
titanic-survival,Maximum entropy (MaxEnt) classifier
User: tonyzeng2016
titanic-survival,Kaggle
User: yanyusong
titanic-survival,Solution to the titanic machine learning competition on kaggle
User: yomna521
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