how to start with kaggle

') # elements in X is between 0 and 1 inclusively. When the data transformation is completed, it can be saved, so that in subsequent calls will be convenient, much faster, especially when the amount of data is large, more should be the case. # Save as h5 file f = h5py. File (' E:\\kaggle\invasive_species\\ndarray_train.h5 ', ' W ') f[' x ']=x f[' y ']=y f.close () When reading the data, note that the end of the [:] cannot be omitted. # read h5 fil

generous and the competition is relatively large; the competition shown for the study (yellow strips on the left) Less bonus; show as recruitment , although there is no bonus, but can be released to the project company internship/interview opportunities, which also gives the company to recruit talent another way. Shown as Playground for the practice race, Mainly used for beginner practiced hand, for beginners, it is recommended to start here . Getti

Https://mp.weixin.qq.com/s/JwRXBNmXBaQM2GK6BDRqMwSelected from GitHubArtur SuilinThe heart of the machine compilesParticipation: Shiyuan, Wall's, Huang Recently, Artur Suilin and other people released the Kaggle website Traffic Timing Prediction Contest first place detailed solution. They not only expose all the implementation code, but also explain the implementation model and experience in detail. The heart of the machine provides a brief o

the training set is clearly categorized.Don't say much nonsense, start writing code!Kaggle CombatIn Kaggle, there is a game of knowledge type. Well, it's your decision!First, download the training set and test set from Kaggle. To open the training set, you can see that the training set is made up of 42000 digital imag

Kaggle Competition official website: https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring Code: Https://github.com/pengpaiSH/Kaggle_NCFM Read reference: http://wh1te.me/index.php/2017/02/24/kaggle-ncfm-contest/ Related courses: http://course.fast.ai/index.html 1. Introduction to NCFM Image Classification task In order to protect and monitor the marine environment and ecological balance, The

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

Yesterday I downloaded a data set for handwritten numeral recognition in Kaggle, and wanted to train a model for handwritten digit recognition through some recent learning methods. These datasets are derived from 28x28 pixel-sized handwritten digital grayscale images, where the first element of the training data is a specific handwritten number, and the remaining 784 elements are grayscale values for each pixel of the handwritten digital grayscale ima

If the linear regression algorithm is like the Toyota Camry, then the gradient boost (GB) method is like the UH-60 Black Hawk helicopter. Xgboost algorithm as an implementation of GB is Kaggle machine learning competition victorious general. Unfortunately, many practitioners only use this algorithm as a black box (including the one I used to be). The purpose of this article is to introduce the principle of classical gradient lifting method intuitively

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

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

"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

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 also makes it easier and more efficient to build a solution based on this method, and many of

(0.826) of the last use of naive Bayesian training. Now we start to make predictions for the test data, using the numTree=29,maxDepth=30 following parameters:val predictions = randomForestModel.predict(features).map { p => p.toInt }The results of the training to upload to the kaggle, the accuracy rate is 0.95929 , after my four parameter adjustment, the highest accuracy rate is 0.96586 , set the parameters

Original address The previous article is for small data sets, the introduction is not recommended from the big data set start, can not consider machine memory, without out-of-core online learning, regardless of the distribution, can focus on the model itself. Next I made two ad CTR estimates related to the match, but the game was already closed, fortunately, we can also submit the results to see where close can be ranked. Actual game 6. Display Advert

):%0.4f"% (I+1,nfold, Aucscore) Meanauc+=aucsco Re #print "mean AUC:%0.4f"% (meanauc/nfold) return meanauc/nfolddef greedyfeatureadd (CLF, data, label, SCO Retype= "accuracy", goodfeatures=[], maxfeanum=100, eps=0.00005): scorehistorys=[] While Len (Scorehistorys) In fact, there are a lot of things to say, but this article on this side, after all, a 1000+ people's preaching will make people feel bored, in the future to participate in other competitions together to say it.http://blog.kaggle.com/2

Date:2016-07-11Today began to register the Kaggle, from digit recognizer began to learn,Since it is the first case for the entire process I am not yet aware of, first understand how the great God runs how to conceive and then imitate. Such a learning process may be more effective, and now see the top of the list with TensorFlow. Ps:tensorflow can be directly under the Linux environment, but it cannot be run in the Windows environment at this time (10,

training data contains a list of label and 784 column pixel values. The test data does not have a label column. Objective: To train the training data, to obtain the model and predict the label value of the test data.The following restores the picture from the pixel value to the actual picture, using Ipython notebook:In [1]:PwdC:\Users\zhaohf\DesktopIn [5]:CD .. / .. / .. / Workspace / Kaggle / Digitrecognizer / Data /C:\workspace\

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 simple decision tree to introduce the general process and steps of data processing. Note: The purpose of this article is to help you get st

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 is a simple and easy-to-use non-parametric classifier. It does not require any prior assum

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