data kaggle

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 re

Kaggle Big Data Contest Platform IntroductionBig Data Competition platform, domestic is mainly Tianchi Big Data competition and datacastle, foreign main is kaggle.kaggle is a data mining competition platform, The website is: https://www.kaggle.com/. A lot of institutions, en

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

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 w

Kaggle is currently the best place for stragglers to use real data for machine learning practices, with real data and a large number of experienced contestants, as well as a good discussion sharing atmosphere. Tree-based boosting/ensemble method has achieved good results in actual combat, and Chen Tianchi provides high-quality algorithm implementation Xgboost als

(' relative importance ') Plt.draw () plt.show () The code is a bit long, but mainly divided into two, one is model training, the other is based on the importance of training to screen important features and drawing. The attributes that are more important than 18 are obtained as shown in the following illustration: It is important to see the three properties of TILTLE_MR title_id gender. and the title related to the attributes are our analysis of the name, can be seen in some string propertie

(test) # print Classification_report (test_target,res1) return kclass train_init = np.genfromtxt (open (' t Rain.csv ', ' RB '), delimiter= ', ') Target_init = np.genfromtxt (open (' trainlabels.csv ', ' RB '), delimiter= ', ') Test_init = Np.genfromtxt (Open (' test.csv ', ' RB '), delimiter= ', ') #train, test,train_target,test_target = Dsplit (Train_init, Target_init) #train, test,test_init = DOPCA (train,train_target,test,test_init) train,test_init = DOPCA (Train_init, Target_init,test_init

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

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

Unsupervised Learning2.2.1 Data Clustering2.2.1.1 K mean value algorithm (K-means)2.2.2 Features reduced dimension2.2.2.1 principal component Analysis (Principal Component ANALYSIS:PCA)3.1 Model Usage Tips3.1.1 Feature Enhancement3.1.1.1 Feature Extraction3.1.1.2 Feature ScreeningRegularization of the 3.1.2 model3.1.2.1 Under-fitting and over-fitting3.1.2.2 L1 Norm regularization3.1.2.3 L2 Norm regularization3.1.3 Model Test3.1.3.1 Leave a verif

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