kaggle competition

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

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,

Then the previous article Training 1) Validation We use the method of stratified sampling (stratified sampling) to separate the annotated datasets by 10% as a validation set (validation). Because the dataset is too small, our assessment on the

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 verification3.1.3.2 Cross-validation3.1.4 Super Pa

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

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, enterprises will issue, description, expectatio

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 ma

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

Classify handwritten digits using the famous MNIST dataThis competition was the first in a series of tutorial competitions designed to introduce people to machine learning.The goal-competition is-to-take an image of a handwritten a-digit, and determine what's digit is. As the competition progresses, we'll release tutorials which explain different machine learning

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

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

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

: Network Disk DownloadContent Profile ...This book is intended for all readers interested in the practice and competition of machine learning and data mining, starting from scratch, based on the Python programming language, and gradually leading the reader to familiarize themselves with the most popular machine learning, data mining and natural language processing tools without involving a large number of mathematical models and complex programming k

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

computational speed and good model performance, which is the goal of this project for two points. The performance is fast because it has this design: parallelization:You can use all of the CPU cores to parallelize your achievements during training. Distributed Computing:Use distributed computing to train very large models. Out-of-core Computing:Out-of-core Computing can also be performed for very large datasets. Cache optimization of data structures and algorithms:better use of hardware. The fi

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

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

, the use of the Out-of-core way, but really slow ah. Similar to the game 6,price numerical features or three-bit mapping into the category features and other categories of features together One-hot, the final features about 6 million, of course, the sparse matrix is stored, train file size 40G. Libliear seemingly do not support mini-batch, in order to save trouble have to find a large memory server dedicated to run lasso LR. As a result of the above filtering a lot of valuable information, ther