Kaggle在9月30日开启的一个新的比赛，举办者是巴西最大的汽车与住房保险公司之一：PortoSeguro。
该比赛要求参赛者根据汽车保单持有人的数据建立机器学习模型，分析该持有人是否会在次年提出索赔。
这里的文档是比赛所用到的数据，数据均已经处理

在使用jiba分词的情况下，使用这个词典有助于提高你的分词准确度，因为这个分词词典包含了众多领域词汇，这些词汇出自某dog的几十个细胞词库。
已使用转换器转换成txt，欢迎下载。

丹麦科技大学教授写的插值工具，Fortran和.exe都有，快捷好用，英文说明如下：programforinterpolatingvaluesfromagridusingbilinearorsplineinterpolation.thegridorthepredictionpointsmaybecineithergeographicalorutmcoordinates.thesplinepredictionisperformedinawindowofsize'nsp'x'nsp'pointsaroundthecwantedpoints,withtypicalvalueofnspbeing8foragoodinterpolation.

著名的Netflix智能推荐百万美金大奖赛使用是数据集.因为竞赛关闭,Netflix官网上已无法下载.Netflixprovidedatrainingdatasetof100,480,507ratingsthat480,189usersgaveto17,770movies.Eachtrainingratingisaquadrupletoftheform.TheuserandmoviefieldsareintegerIDs,whilegradesarefrom1to5(integral)stars.[3]Thequalifyingdatasetcontainsover2,817,131tripletsoftheform,withgradesknownonlytothejury.Aparticipatingteam'salgorithmmustpredictgradesontheentirequalifyingset,buttheyareonlyinformedofthescoreforhalfofthedata,thequizsetof1,408,342ratings.Theotherhalfisthetestsetof1,408,789,andperformanceonthisisusedbythejurytodeterminepotentialprizewinners.Onlythejudgesknowwhichratingsareinthequizset,andwhichareinthetestset—thisarrangementisintendedtomakeitdifficulttohillclimbonthetestset.Submittedpredictionsarescoredagainstthetruegradesintermsofrootmeansquarederror(RMSE),andthegoalistoreducethiserrorasmuchaspossible.Notethatwhiletheactualgradesareintegersintherange1to5,submittedpredictionsneednotbe.Netflixalsoidentifiedaprobesubsetof1,408,395ratingswithinthetrainingdataset.Theprobe,quiz,andtestdatasetswerechosentohavesimilarstatisticalproperties.Insummary,thedatausedintheNetflixPrizelooksasfollows:Trainingset(99,072,112ratingsnotincludingtheprobeset,100,480,507includingtheprobeset)Probeset(1,408,395ratings)Qualifyingset(2,817,131ratings)consistingof:Testset(1,408,789ratings),usedtodeterminewinnersQuizset(1,408,342ratings),usedtocalculateleaderboardscoresForeachmovie,titleandyearofreleaseareprovidedinaseparatedataset.Noinformationatallisprovidedaboutusers.Inordertoprotecttheprivacyofcustomers,"someoftheratingdataforsomecustomersinthetrainingandqualifyin

HandbookofImageQuality,CharacterizationandPrediction.BrianWKeelan.MarcelDekker,2002好不容易找到的

BuildingPredictiveModelsinRUsingthecaretPackage

控制理论和最优滤波器设计的经典教材，有很强的专业性，适合控制理论和信号处理方面的进阶。

Badclimatechangemaygreatlyincreasethefragilityofthecountry.Howtoevaluatetheimpactofclimatechangeandmitigatetheimpactofclimatechangehasbecomeanurgentproblem.Withregardtotaskone,adataenvelopmentanalysis(DEA)modelisestablishedtogetthecountry'sfragility.Firstofall,weselected4climatefactorsasinputindicatorsand5outputindicators.Then,weusetheentropymethodtodeterminetheweightandthenthenationalvulnerabilityisdivided.Atthesametime,wegettheconclusionthattemperatureaffectsGDPandthetimesofarmedconflictdirectlyandaffectsthefragilityindirectly.Inviewoftasktwo,wechooseSomaliaasanobjectofstudy.First,alltheindexesaredividedinto5levelsbythemethodofclusteranalysis.Second,weselect10countriesincludingSomalia,tosolvethedecisionunitmatrix.Then,usingthemodeloftheproblemone,itisfoundthattheincreaseintemperatureandrainfallwillcausethenationalvulnerabilitytoriseanddecrease,respectively.Finally,weassign4climateindicatorsto0ofthedecisionunits,anddrawtheconclusionthatnationalvulnerabilitywillbereducedwithouttheimpactofclimatefactors.Whenitcomestotaskthree,weusetheroughsettheorytoreducetheoutputindextothenumberofarmedconflicts.Then,weusetheBPneuralnetworkmodeltopredicttheconclusion:Thereisasignificantincreaseinfragilityincasesofmuchmorearmedconflictandabnormaltemperature.Whentheaverageannualarmedconflictiscertain,thenationalvulnerabilityindexwillfaceanincreasingturningpointatthetemperatureof10.01andtherainfallof1823mm.Astotaskfour,threepoliciesonenergyreductionandemissionreductionissuedbythegovernmenthavebeenselected,andamodelofcarboncycleisestablished.TakingChinaasanexample,wecalculatetheextentofthechangeoftheaveragetemperaturebyreducingthecarbondioxideemiss

利用PSO训练BP神经网络的matlab代码。
粒子群算法优化BP神经网络，可用于指标预测(BPneuralnetworkoptimizedbyParticleswarmoptimization(PSO)thatcanbeusedforindexprediction)

codeforcesrating预测插件，拖动至Chrome即可安装

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。