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Homework Programming AssignmentIn this assignment, you will be implementing a support vector machine to classify anAustralian weather dataset.Part 0: Data PreprocessingThe data set has features of related weather metrics per day and your goal is to predictwhether or not it will rain the next day. We have already processed the data for you(for part 1).Part 1: Support Vector MachineYour task will be to implement a support vector machine from scratch only in python.We have provided a file called svm.py that contains a minimal amount of stencil. Do notmodify the stencil; otherwise, your submission will be incompatible with the autograder.You are permitted to add additional class variables and functions, but do not edit theexisting class variables. You can check your implementation using keras, sklearn oranother python library. However, when we grade your implementation, we will removelibraries that provide pre-built implementations.Support Vector Machine (svm.py):You will see three functions and a two classes with a predict method: LinearSVM andSVM. Your SVM will take the kernel function (linear, radial basis function/gaussian, andpolynomial), and C as input. Similar to the logistic regression, we will be using thepredict method in your model to evaluate the accuracy. We have also provided you witha quadratic program solver that you can use inside the train function. Although youshould implement both SVM and LinearSVM, you will want to use LinearSVM on thisdata set, because forming the kernel matrix is very computationally expensive whenthere are tens of thousands of rows.Tips and Hints● Test your implementation against other implementations and see how itcompares (e.g., SVM from sklearn).● Split up functions in a logical way that makes it easy to write unit tests. Test thateach of your functions is outputting the correct information given knowninput/output.● Use small inputs to test and debug your models. Try creating your own dataset.● Try visualizing the output of your model; is it what you expected?Grading:● SVM:○ Linear Kernel Accuracy○ Radial Basis Kernel○ Polynomial Kernel LinearSVM accuracy > =80%● Code Review (PEP8 style and some comments)Handin:The files to turn in is: svm.py. The autograder for the SVM will show if your submissionis compatible with our system程序：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