Plz provide the code and remove it 2 days after u have posted as I don't want to share it with others and copy what I have written. Don't remove it before 2 days.

Tutorial 9 and 10.docx X al-9-and-10docx/#question Tutorial: #9 and #10 Topic: Programming in R. Classification and Clustering Programming Language: You can choose between Python and R. For completing the challenge in R there is an extra bonus point! R Setup Instructions: Before starting this assignment you need to make sure the programming language R and development environment Studio are installed on your computer, and download a zipped folder containing the datasets used in the assignment. 1. R. If you don't already have installed on your computer, go to this page. find the link corresponding to your Os (Windows, Mac OS X or Linux), and follow the corresponding download and installation instructions 2. RStudio - To install Studio, go to this page download the correct installer for your OS. open it and follow the setup instructions Dataset: You can find schoolkids dataset on Blackboard KNN Classification Using Python or R (Schoolkids data) Write Python or R code that creates a classifier for the Schoolkids data using k- nearest-neighbors (knn), taking into account different features. We've created two new data files. Schoolkids Train.csv and Schoolkids Test.csv: Schoolkids Test contains 20 rows extracted from the original Schoolkids data, and Schoolkids Train contains the rest. In both of the data files, the School column has been removed, and Goal is shifted to the last (Sth) column. Initially use all eight fea- tures in the data to predict each student's Goal Once you have your code running experiment with different values of k l = 3..... 10) In addition try using different subsets of the features for prediction instead of all eight features and report the accuracy over the test dataset Normalize the data from 0 to 1 for numerical values. You can use Euclidean and hamming distance (for categorical data) to compute the distances How high can you get the accuracy in the program you submit make sure to use the setting fork and the set of features that give the highest accuracy you were able to find Include the source code in the final submission Do not use external library that contains KNN implementation for a full mark if you will use external library grade will be decreased by 50% Tutorial 9 and 10.docx X al-9-and-10docx/#question Tutorial: #9 and #10 Topic: Programming in R. Classification and Clustering Programming Language: You can choose between Python and R. For completing the challenge in R there is an extra bonus point! R Setup Instructions: Before starting this assignment you need to make sure the programming language R and development environment Studio are installed on your computer, and download a zipped folder containing the datasets used in the assignment. 1. R. If you don't already have installed on your computer, go to this page. find the link corresponding to your Os (Windows, Mac OS X or Linux), and follow the corresponding download and installation instructions 2. RStudio - To install Studio, go to this page download the correct installer for your OS. open it and follow the setup instructions Dataset: You can find schoolkids dataset on Blackboard KNN Classification Using Python or R (Schoolkids data) Write Python or R code that creates a classifier for the Schoolkids data using k- nearest-neighbors (knn), taking into account different features. We've created two new data files. Schoolkids Train.csv and Schoolkids Test.csv: Schoolkids Test contains 20 rows extracted from the original Schoolkids data, and Schoolkids Train contains the rest. In both of the data files, the School column has been removed, and Goal is shifted to the last (Sth) column. Initially use all eight fea- tures in the data to predict each student's Goal Once you have your code running experiment with different values of k l = 3..... 10) In addition try using different subsets of the features for prediction instead of all eight features and report the accuracy over the test dataset Normalize the data from 0 to 1 for numerical values. You can use Euclidean and hamming distance (for categorical data) to compute the distances How high can you get the accuracy in the program you submit make sure to use the setting fork and the set of features that give the highest accuracy you were able to find Include the source code in the final submission Do not use external library that contains KNN implementation for a full mark if you will use external library grade will be decreased by 50%