kaggle titanic

Kaggle Data Mining -- Take Titanic as an example to introduce the general steps of data processing, kaggletitanic Titanic is a just for fun question on kaggle, there is no bonus, but the data is neat, it is best to practice it. This article uses Titanic data and uses a simp

Titanic is a kaggle on the just for fun, no bonuses, but the data neat, practiced hand best to bring.Based on Titanic data, this paper uses a simple decision tree to introduce the process and procedure of processing data.Note that the purpose of this article is to help you get started with data mining, to be familiar with data steps, processesDecision tree model

Recently has the plan through the practice Classics Kaggle case to exercise own actual combat ability, today has recorded oneself to do titanic the whole process of the practice. Background information: The Python code is as follows: #-*-Coding:utf-8-*-"" "Created on Fri Mar 12:00:46 2017 @author: Zch" "" Import pandas as PD from Sklearn.featur E_extraction Import Dictvectorizer from sklearn.ensemble import

The previous three posts have been a fairly complete feature engineering, analyzing string-type variables to get new variables, normalize numeric variables, get derived properties and make dimensional specifications. Now that we have a feature set,

Make text ripple-third-party Open Source-Titanic, ripple-titanic : Https://github.com/RomainPiel/Titanic You can directly copy the code when using it (Note that there must be a wave chart under the res file) Xml code: JAVA code: Titanic = new Titanic ();

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to install ANACONDA3.The best is to https://www.kaggle.com registered account, run the fourth

New Smart Dollar recommendations　　Source: LinkedIn　　Abhishek Thakur　　Translator: Ferguson　　"New wisdom meta-reading" This is a popular Kaggle article published by data scientist Abhishek Thakur. The author summed up his experience in more than 100 machine learning competitions, mainly from the model framework to explain the machine learning process may encounter difficulties, and give their own solutions, he also listed his usual research database, al

Recently has been intermittent to do this titanic survival prediction model of the practice, this kaggle contest, many people on the internet have shared, and are very mature, and some write very detailed, I am mainly on the basis of cattle, according to the data mining process to comb ideas, Then practice each step to familiarize yourself with how Python is used for data mining.The general process of data

The sinking of the RMS Titanic is one of the very infamous shipwrecks in history. On April, 1912, during she maiden voyage, the Titanic sank after colliding with a iceberg, killing 1502 out of 2224 PA Ssengers and crew. This sensational tragedy shocked the international community and LEDs to better safety regulations for ships.One of the reasons, the shipwreck led to such loss of life is that there were not

Big Data Competition Platform--kaggle Introductory articleThis article is suitable for those who just contact Kaggle, want to become familiar with Kaggle and finish a contest project independently, for the Netizen who has already competed on the Kaggle, can not spend time reading this article. This article is divided i

In this paper, based on the spark decision tree Model algorithm, we train the Titanic's training data set containing the characteristics of passengers and crew, obtain the survival model of decision tree, and test the model with test data set (Knime).1. Download training data set and test data set from Kaggle website2, in Knime to create a new workflow, named: Titanicknimespark3. Read the training data setKnime supports reading data from a Hadoop clus

Hello, long time no article, there is wood to think I ah ~The formal work has been over the past one months, found in Qingdao internship and work in Beijing, feel completely different ~Now every night back to live in the place, are tired to sleep ... So also not in the mood to write too many articles and everyone to share, but I will adjust the status as soon as possible, to revitalize the! (where it's weird ...) Project Introduction My though

Titanic is a simple illusion obtained by applying an animated translation on the TextView textpaint Shader ' s matrix.Use of TitanicTitanic, the project structure is as follows:First, download the Titanic and deploy to the project,Titanic's project address: Https://github.com/RomainPiel/Titanic.In the project we need three files to use Titanic: Titanic.java, Tita

1 Data explorationA holistic understanding of the data1.1 Viewing data What are some of the characteristicsImport Pandas as PD Import Seaborn as SNS%= pd.read_csv ('g:\\titanic\\train.csv')Titanic.sample (10)Get the data 10 rows of records to observe, preliminary understanding of the composition of the data, you can see that the age, cabin inside is missing values, after further understanding the statistics of the data and then data processing, observ

One of the hottest films of the moment is Titanic. Even if the plot is already in the heart, but so beautiful and moving love, I believe it will attract a large number of viewers to pay for it, between lovers also just through this opportunity to increase each other's hormones. Of course, just watching a movie, that's too small pediatrics. If you can save your intimate photos on your mobile phone, iPad, and computer, you can always witness your love a

Getting started with Kaggle-using Scikit-learn to solve digitrecognition problems@author: Wepon@blog: http://blog.csdn.net/u0121626131, Scikit-learn simple introductionScikit-learn is an open-source machine learning toolkit based on NumPy, SciPy, and Matplotlib. Written in the Python language. Mainly covers classification,back and clustering algorithms such as KNN, SVM, logistic regression, Naive Bayes, random forest, K-means and many other algorithms

Commemorating the tenth anniversary of the film Titanic Author: delphiscnInformation Source: http://blog.csdn.net/Delphiscn Embracing her and him ten years later is our eternal memories-Inscription In 1997, she was a 22-Year-Old qingxiu girl from England.In 1997, he became a hot boy at the age of 22.A movie makes her and him the opposite.After gloryShe returned to England and became addicted to literature and artHe is confused, quiet, depressed, anxio

Get started with Kaggle -- use scikit-learn to solve DigitRecognition and scikitlearnGet started with Kaggle -- use scikit-learn to solve DigitRecognition Problems @ Author: wepon @ Blog: http://blog.csdn.net/u012162613 1. Introduction to scikit-learn Scikit-learn is an open-source machine learning toolkit based on NumPy, SciPy, and Matplotlib. It is written in Python and covers classification, Regression

Titanic will sink eventually. At the time of sinking, some people will be irrelevant. Some people will welcome new life. Only Kobe will hold the ship at the helm until the last moment. I am not a fan of Kobe and seldom admire the so-called emotions and loyalty in the professional sports world. But in this scene, I saw the purple and gold colors in Kobe's life. "The Lakers are a ship in the water. Everyone else can jump, they can leave in the summer,

matplotlib.pyplot as Plt %matplot Lib inline trainpath = str (' e:\\kaggle\invasive_species\\train\\ ') testpath = str (' E:\\kaggle\\invasive_ Species\\test\\ ') n_tr = Len (Os.listdir (trainpath)) print (' num of training files: ', n_tr) Num of training files:2295 You can see the specifics of the train_labels.csv, which is shown in the table below, where the data is already scrambled, and the samples l