Today everyone, my name is Albert winner and today I'm going to demonstrate the SRT-5-1 lab tree. Advanced data analysis, it's about classification using decision trees. Objective as love is static rated lab 2. To create machine learning model using decision trees to classify binaries into malware and benign. So, yeah, we will have to write a report, process data and pattern program to classify and decide and show decision trees output. Right. So it's pretty much we have to explore the decision trees variance. So first off, and ask one, I need to install scikit learn. And so I already done a screenshot, but I show you how it's being done. So here's a visual studio code. Right. You can see I install the scikit learn over here and successfully installed. So the next step and ask one is to create a new column called target. But using this column will be zero. If the label is benign where and when if the label is smile where. Right. So we've been advised these asmas data set. And so this is how it looks like guys. Okay. The target is preserved. It's already been strip off the MD5 and the file name, all that. So pretty much what we have here is one for our own information that is actually those are model version. Right. And from here, 395 downwards, there are benign. Right. So in total, we will have one top. I can see there's a reason why camera but total of 1,360 pounds record with many many features. So there are 3,94 malware and 966 benign. So I did a computation there for our own information. Okay. Let me analyze the data set. So this is as much a regional file. Right. So the target is this side. As we know, this one says malware and when it turns to something green or something, it becomes a benign. Right. So it goes then is the right pipe. This is a regional asmas file. So I modify this one. So I actually move the target at the end. Okay. So if you look at it here, target becomes at the end. So that the asmas code will also work with this kind of. So like a raise and right. So we are using that one. As we know, we have over 247. 247 is the target. So we will split the data set into two. Right. So zero to 246 leaving the target. Right. 246. So we will only split after 246 instead of 247. And this is the decision tree classifier. Right. And this is how we train a model. Right. And this is the program output. This is task one. Now guys. Right. So this is the. Like the plot that will plot the graphics. Right. So it will come up also. We're saying to become a PDF file. It will say a PDF file. Right. Okay. And this is the part where it says random three forest. Right. And we have the computer matrix classification report and occurs is score. Right. So when we actually run this program, right, it creates this one here that is set. Right. So let's open it. Okay. Can see the close. And this one is actually the first run. Right. Now modifying that and. Now I make I made actually a tree plus one over plus one over here. So that is when it will not be confusing. Right. So when I run this one, run them state max of five. It's going to create this file also. This one. Right. I should see their five levels. Right. And that's the PDF that you got created over here. Right. Okay. And so if I run this file, this program is going to create the entropy. Right. So yeah, let me change. These zoom. Yeah. Yeah. So you can see entropy samples and so on. This is the true. Let me show you. And I decided spals. Right. And as you can see, I just write again, the entropy and is this here precision. Right. The bar is threshold of four. Right. Precision for zero. This is nine is nine percent. For one is zero point nine seven. So thank you very much. I hope you enjoy watching my video. And this is the lab tree. For advanced data analysis and is classification using decision trees. And also with this lab, I'll be submitting this report. Right. And then this tree files. And then this tree PDF file that get created for your reference. And also this one. That's one that for. Okay. So those are my lab demonstration. Have a good day.