The dataset on the Canvas (ML_HW_Data_CellDNA.csv) contains various numericmeasurements (i.e. size, center, etc) from thousands of bacterium under microscope. Thenon-zero values in the last column...

Just basic coding for a graduate student. the data are attached below


The dataset on the Canvas (ML_HW_Data_CellDNA.csv) contains various numeric measurements (i.e. size, center, etc) from thousands of bacterium under microscope. The non-zero values in the last column are the target responses that indicate the bacterium (rows) that are interesting enough for further study. The Os in the last column indicate the bacterium (rows) are NOT interesting candidates for further study. Convert this target dependent variable to binary values of either Os or 1s for your two-class classification. Write a program using either Python, MatLab, or any programming language of your choice to perform the two-class classification analysis on this dataset using the Support Vector Machine method with the “RBF” kernel. You do NOT need to split data into training and testing sets. Answer the following questions: 1. Experiment your SVM RBF model with different “box constraints” and “kernel scales”. 2. What is the accuracy, Precision, and Recall for each class prediction under each of your above experiments? 3. Is there any trend that you observed in your experiments? 4. Optional: plotting a graph with clear legends and tick labels to illustrate the trend will be very helpful. 5. Create an ROC curve plot for **EACH** class in Just ONE of your experiments. 1. Please include the WORD document to include your answers (and clearly readable figures/screenshots) to the above questions. Please include your name on the top of your WORD document. 2. Please print your program (matlab or python) as PDF and include the PDF in your submission. Please name your program as “a8.m/.mlx/.py/.inpyb”, depending on the programming language / environment you used. > Please also include your program in the formats like .m/.mlx/.py/.inpyb in your submission. The dataset on the Canvas (ML_HW_Data_CelIDNA.csv) contains various numeric measurements (i.e. size, center, etc) from thousands of bacterium under microscope. The non-zero values in the last column are the target responses that indicate the bacterium (rows) that are interesting enough for further study. The Os in the last column indicate the bacterium (rows) are NOT interesting candidates for further study. Convert this target dependent variable (last column) to inary valugs of either Os or Is for your two-class classification. Write a program using either Python, MatLab, or any programming language of your choice to perform the two-class classification analysis on this dataset using the Support Vector Machine method. You do NOT need to split data into training and testing sets. Answer the following questions: 1. How many support vectors did you find? 2. List top 3 records that have the smallest =xabsolute™* values decision values (i.e. from w” « X + b calculation). 3. What are the decision values “w” + X +b” for the following records: 131, 165, 892, 1057 (in Matlab), or 130, 164, 891, 1056 (in Python)? Anything special about those values of these few records? Also, what are the probabilities of belonging to the class 1 (i.e. the 2" class) for those records? 4. What is the precision, recall, F-measure of **EACH** of the two classes. 5. Create a ROC Curve for **EACH** of the two classes. 1. Please include the WORD document to include your answers (and clearly readable figures/screenshots) to the above questions. Please include your name on the top of your WORD document. 2. Please print your program (matlab or python) as PDF and include the PDE in your submission. Please name your program as “a7.m/.mlx/.py/.inpyb”, depending on the programming language / environment you used. 3. Please also include your program in the formats like .m/.mlx/.py/.inpyb in your submission.