There are 1 repository under titanic topic.
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
Exploratory Dataset Analysis (EDA) will be uploaded to this repository. Libraries such as Pandas, Matplotlib, Seaborn and Plotly will be used for data analysis.
Predict survival on the Titanic on a Quantum Computer
Some useful examples of Deep Learning (.ipynb)
This repository is on different types of data, types of missing values and how to handle missing value
Data Analysis Solution for Titanic passenger data.
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.
Essential machine learning algorithms, concepts, examples and visualizations. Popular machine learning algorithms from scratch. Applications of machine learning.
🚢 Association and Pattern Recognition Algorithms on data from Titanic survivors.
Neural Network ConsoleでKaggleのタイタニックを学習するサンプルです。前処理(Jupyter Notebook)、学習・モデル構造自動探索(Neural Network Console)、ONNX推論(Jupyter Notebook)を含みます
Entry for the Titanic: Machine Learning from Disaster competition on Kaggle.
Titanic: Machine Learning from Disaster with Keras
Titanic dataset is used to perform Pandas operations
This repository contains my work during the Himmah data science Bootcamp.
In this challenge, I 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).
My workup of the Kaggle Titanic tutorial using R.
Deploying Titanic with a Custom BYOC Container to Multiple Platforms!
Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
model build, training, inference in SageMaker pipeline
In this repository you will get a complete guide to Titanic Spaceship Kaggle Competition. The main aim of this project is to predict whether the passengers will be transported to alternate dimensions or not.
Machine Learning / Python with Titanic Dataset
Titanic Survival Prediction Project (93% Accuracy)🛳️ In this notebook, The goal is to correctly predict if someone survived the Titanic shipwreck using different Machine Learning Model & Hyperparameter tunning.
Data preprocessing and feature engineering using Titanic Dataset.