Pdf files for the project will be uploaded later. Final Project Read the reference paper to know the background information about the collection of the 800 Columbia images. First use the attached sample code to perform the basic logistic regression analysis to predict the outcome. Here the outcome is whether or not the image is an outdoor picture. (note: change the file path name to your own, do not use your teacher's computer path name.) The predictors in the sample code are the 3 medians of the pixel intensities of the images. For your project, you need to use another method to perform the image classification. You are encouraged to use all the pixel information, partition the image into grids (say 10 by 10 partition) and obtain the mean or median intensities of the pixels in each grid area. In this way, you will have more predictors in your model. You need to use other methods such as neural network, random forest, support vector machine or linear discriminant analysis and KNN which we learned in this course. (Note: LASSO is not included as the main method here. Even if you wish to use LASSO, you still need to use one of the methods in the list above.) You need compare the method you proposed with the basic logistic regression method I provided here. You can compare them using sensitivity, specificity, and misclassification errors. Write a formal report of 10 pages with the following sections: title, abstract, introduction, data, methodology, analysis and model, result, conclusion, references. This report is individual work and no plagiarism is allowed. When you submit it via Turnitin, you will see the similarity score of your report. The score has to be less than 20%. Reports with similarity score above 20% will not be accepted.