Stats 101C作业代做、代做R语言作业、代写R编程设计作业、代写Kaggle Competition作业代做留学生Prolog|代做留学生Process

Stats 101C Kaggle Competition Final ProjectThere are two competitions:- A classification competition- And a regression competitionEach competition accounts for 16% of your final course grade. Each competition is scored separately.Grading of each competition:- 4% Competition performance and R Script verification- 12% ReportCompetition performance grading:First place and ties for first place: 4 pointsLast place and ties for last place: 0 pointsEveryone else in between first and last place earns points that are scaled.For example, let’s say that at the end of the competition there are 12 unique positions on the leaderboard. There might be more than 12 teams, but with ties, let’s say there are only 12 unique positions.Having 12 unique positions means there will be 11 gaps. Each gap will be 4/11 = 0.3636 points. And thescoring will be as follows:- 1st place (and any ties): 4 points- 2nd place (and any ties): 3.636 points- 3rd place (and any ties): 3.273 points- Etc.- 10th place (and any ties): 0.727 points- 11th place (and any ties): 0.364 points- 12th place (and any ties): 0 pointsEach competition is scored separately. It is possible for a team/individual to get first place in onecompetition and earn 4 points in that competition while ending up in last place for the othercompetition and getting 0 points for that one.R Script verificationYou will submit an R script that shows how your predictions were made. I have provided a startingtemplate that imports the data and produces the necessary output file to submit to Kaggle.Your R script will be run to verify that it does indeed produce the predictions you submitted to Kaggle.If the predictions you submitted to Kaggle do not match the output produced by your R script, you willget a 0 for the competition performance portion of your project grade. This rule is to prevent studentsfrom making a model in R and then manually changing the predictions in the submission file to get ahigher score in the competition.Similarly, your R script should not make predictions manually. It must use the trained model for makingpredictions.Report guidelines:You will submit a PDF report explaining the model you fit. The report is worth 12 points.The report will describe anything that is done to the data before the model is fit. This includes any datacleaning, data manipulation, or data transformation that was performed. It includes any variableselection or dimension reduction process or any new variables that were created. You don’t need to doany of the above things in your script to get full credit, but if you do any of the above steps, they mustbe explained in the report.The report will describe what kind of model was chosen for the final prediction and submission.The report will explain why you think your model is a good choice and/or any shortcomings of the modeland areas of improvement. This section should include how you evaluated your model performance.(Your evaluation of model performance should not be, “I submitted the predictions to Kaggle and got ascore.”)Report should be about 2 pages long.Grading Rubric for the report.Good: Basic: Needs Improvement:Overall writing Explanations are correct,complete, and convincing.Assumptions are made explicitand given justification.[minus ~0 pts]Explanations are partiallycorrect but incomplete orunconvincing.Assumptions are made explicitbut not justified.[minus ~1pts]Explanations are illogical,incorrect, or incoherent.Assumptions are not madeexplicit.[minus ~3 pts]Description ofthings done tothe data beforefitting the modelExplanation of any datamanipulation is completewithout mistakes.Any and all steps that areperformed in the script areexplained. Reasons for each stepis provided and are justifiable.[minus ~0pts]Any and all steps that areperformed in the script areexplained.Reasons for each step isunconvincing or questionable.[minus ~1pts]Explanation of any datamanipulation is not complete.There are steps performed inthe script that are notexplained.Reasons for each step is notprovided or are not justified.[minus ~3 pts]Description offinal modelExplanation of model iscomplete and without mistakes.Report describes how many /what variables are used. Reportdescribes properties of themodel (e.g. parametric vs nonparametric).Report providesreasons for using this particularmodel.[minus ~0pts]Explanation of model iscomplete but has minormistakes.Report describes how many /what variables are used. Reportdescribes properties of themodel. Report provides reasonsfor using this particular model.[minus ~1pts]Explanation of model containsserious mistakes. The modelused is not adequatelydescribed.Report does not provide reasonsfor using this particular model.[minus ~3 pts]Discussion ofmodel strengthsand weaknessesand modelperformanceEvaluation of modelperformance is complete andreasonable.Report discusses modelstrengths and weaknesses /possible improvement.Discussion is correct andjustifiable.[minus ~ 0pts]Evaluation of modelperformance is provided butcontains minor mistakes.Report discusses modelstrengths and weaknesses /possible improvement.[minus ~1pts]Evaluation of modelperformance is missing orcontains serious mistakes.Discussion of model strengthsand weaknesses is missing orcontains serious mistakes.[minus ~3 pts]本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp QQ：99515681 或邮箱：[email protected] 微信：codehelp