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Assignment #4Download the data from the following link. The data was obtained from Kaggle.https://www.dropbox.com/sh/33f7zxx8ve6u0qk/AAC2RPmMy_iCiV_KTrW4D205a?dl=0NOTE: This is not a team project. Do it by yourself. Submit your answers and the R-code used for youranalysis by Dec 18, 23:59PM. You can freely update your answer before the due. No late submission will beaccepted.NOTE 2: Make your document formatted as follows: Times New Roman, 12-point font, double-spaced only(not 1.5), 1-inch margins all around 8.5 x 11-inch paper (or A4), and the pages must be numbered. Thedocument should not exceed 10 pages including tables and figures. You can use a 10-point font and singlespacefor tables. The score will be determined not only by the accuracy and completeness of answers but alsoby the presentation quality of the document (Do not simply screenshot the results of R output!)Why are our best and most experienced employees leaving prematurely? Try to predict which valuableemployees will leave next. Fields in the dataset include:Satisfaction LevelLast evaluationNumber of projectsAverage monthly working hoursTime spent at the company (years)Whether they have had a work accidentWhether they have had a promotion in the last 5 yearsDepartments (column sales)SalaryWhether the employee has leftQ1. Load the data to your R system. How many variables and observations are in the data?Q2. Generate the descriptive statistics for each variable.Q3. Explore the relationships between variables. Can you find any interesting relationship?Q4. Compare the employees who has left and who has remained. Visualize the comparison with a histogramby factors.Q5. Develop the best regression model to explain the employees leaving. Estimate the parameters of yourmodel. Can you find any meaningful result?Q6. Develop the best classification model to predict the employees leaving. What is the sensitivity and thespecificity of your model?Q7. What would you suggest the HR managers do to increase Satisfaction Level of employees? Conduct theappropriate analysis to derive your suggestions.Q8. Find other managerial insights from the data.Q9. (Bonus question) Describe your efforts to be a Susan-like student for this course.本团队核心人员组成主要包括硅谷工程师、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