机器学习项目实战

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.

Spark ML Tutorial and Examples for Beginners

This repository contains a gentle introduction to machine learning algorithms with hands on practical examples

Kick-off repository for starting with Kaggle!

I used expert mode of TensorFlow to solve some problems

This repository is on different types of data, types of missing values and how to handle missing value

Predict survival on the Titanic on a Quantum Computer

Survival prediction using Titanic dataset and Logistic Regression

Some useful examples of Deep Learning (.ipynb)

Kodluyoruz istatistik ve veri ön işleme çalışma grubunda Eğitmenimiz tarafından önerilen Titanic veri seti üzerindeki çalışmam yer almaktadır. Bu çalışmada veri setinin betimsel istatistikleri, veri görselleştirmesi, eksik (kayıp) veri analizi yöntemleri (missing value analysis methods) , aykırı değer analizi (outlier detection) yöntemleri ilgili veri setine uygulanmıştır.

Data Analysis Solution for Titanic passenger data.

find things in google maps

Task 2 of the Prodigy infotech Data science internship

東京大学グローバル消費インテリジェンス寄付講座

Exploratory data analysis (EDA) of the Titanic dataset using Python. Analyzed survival patterns by age, gender, and class with visualizations (seaborn/matplotlib). Non-ML focus—highlighting insights with statistics and plots.

Auriez vous survécu au naufrage du Titanic ?

Essential machine learning algorithms, concepts, examples and visualizations. Popular machine learning algorithms from scratch. Applications of machine learning.

Simple EDA for Titanic Dataset.

No description provided.

This repository contains my work during the Himmah data science Bootcamp.

Here are several machine learning projects.

Entry for the Titanic: Machine Learning from Disaster competition on Kaggle.

Titanic.nu - A website I've developed for friends of mine who are running an art collective and a gallery. It's based on this Netlify template: https://github.com/netlify-templates/one-click-hugo-cms

🚢 Kaggle Titanic ML solution achieving 0.80143 score using 100-model ensemble of Gradient Boosted Trees | TensorFlow Decision Forests

Titanic dataset is used to perform Pandas operations

🚢 Association and Pattern Recognition Algorithms on data from Titanic survivors.

Notebooks from my blog. meterdatascience.weebly.com

Sopravviverai alla sciagura del Titanic? Un Classificatore Binario basato su Neural Network