kaggle datasets

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 p

(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 are: numTree=55,maxDepth=30 , when I change

description, clarify the requirement;2. Build a model with any language or algorithm you are good at, train with training sets, and then use the trained model to speculate on the labels of the test set and generate a test set labels as the final submission file;3. The system will select 25% of the data from the submitted file for preliminary evaluation, according to the evaluation results are accurate and ranked. At the end of the game, use the remaining 75% of the data for final evaluation as

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

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

This blog is based on Kaggle handwritten numeral recognition in combat as the goal, with KNN algorithm learning as the driving guidance to explain. The reason for writing this blog What is KNN The analysis of KNN Kaggle Combat Advantages and disadvantages and optimization methods Summarize Reference documents The reason for writing this blogMachine learning is very hot

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 algorithms To get started.The data for this competition were taken from the MNIST dataset. The MNIST ("Modified National Institute of

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

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

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

The previous blog introduced the use of the logistic regression to achieve kaggle handwriting recognition, this blog continues to introduce the use of multilayer perceptron to achieve handwriting recognition, and improve the accuracy rate. After I finished my last blog, I went to see some reptiles (not yet finished), so I had this blog after 40 days. Here, pandas is used to read the CSV file, the function is as follows. We used the first 8 parts of Tr

"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

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

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

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

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

Finished Kaggle game has been nearly five months, today to summarize, for the autumn strokes to prepare.Title: The predictive model predicts whether the user will download the app after clicking on the mobile app ad based on the click Data provided by the organizer for more than 4 days and about 200 million times. Data set Features: The volume of data is large and there are 200 million of them. The data is unbalanced and th

Kaggle, get up.Kaggle games rely on machines for automatic processing, and machine learning is almost a must-have skill. Getting Started with Kaggle the machine learning skills required is not in-depth, just need to have a basic understanding of the common methods of machine learning, for example, for a problem, you can realize that it is a classification problem AH or regression problem ah, Why the machine