1 is .txt document where you will have all the instructions and 2nd you will have python code and you will have to write a logic #todohere..

I will provide you two documents. 1 is .txt document where you will have all the instructions and 2nd you will have python code and you will have to write a logic #todoHere.. Where you will need to write the code. It is related to Tic Tac Toe game and also we have to put X in random places in the board and make sure X wins. You can try referring youtube videos on this.

I’m working on a artificial intelligence exercise and need an explanation and answer to help me learn.

I’m working on a artificial intelligence exercise and need an explanation and answer to help me learn.
I will provide you two documents. 1 is .txt document where you will have all the instructions and 2nd you will have python code and you will have to write a logic #todoHere.. Where you will need to write the code. It is related to Tic Tac Toe game and also we have to put X in random places in the board and make sure X wins. You can try referring youtube videos on this.

Are the random test points consistently on the correct side of the line?

Problem 1 – Decision Trees (20 points)
Pull the data from https://archive.ics.uci.edu/ml/datasets/Credit+Approval
Create a decision tree to determine if credit should be extended based on a test case.
Grading criteria: Demonstrate that you evaluated the data set and applied aduquate preprocessing to the data
Make sure you comment you code and the cleaning process so we can follow your logic in grading
Provide a confusion matrix for your results. Text based is fine.
Provide a visualization with explanation that demonstrates logical evaluation of the model
Actual accuracy can depend on how you split the training and test data and other random variationsIf you get below 70% accuracy, there may be a problem with your model
Spoiler Alert: If you don’t start with some exploration to
determine how to approach data cleaning, this will be more difficult
than it should be.
Put the explanation of your model here:
Problem 2 – K-means (10 points)
Use the Ecoli dataset at https://archive.ics.uci.edu/ml/datasets/Ecoli
Ignore the label and create clusters using k values between 4 and 6.
Pick the best k value and explain why you picked it
Show any calculations you used to pick the best cluster
Create two visualizationOne colors the nodes with the cluster membership
The other colors the nodes based on the actual label
Grading criteria: Adequately describe how to pick the best cluster and successful create the required visualization
Provide an explanation of your model:
Problem 3 – Support Vector Machines (10 points)
Use the Iris trainging set
Explore the data to find the best two features to useWe are mostly doing this so we can visualize the results
Split the data set into 80% training and 20% testing
Create a SVM to model the data
Create a visualization that shows the line and the margins
Create anonther visualization that shows the decision surfaceDo not include the test data points
Randomly select 10 test points and add them to the visualization. Color them based on their label
Are the random test points consistently on the correct side of the line?
Predict the label for ALL of the test data Show a confusion matrix
Calculate the F1 measure
Grading criteria: SVM graphically appears to correctly to use a reasonable line
F1 measure is consistent with what we showed in class
Explanation of your model:

Explain how the technology is applied in these local or regional settings.

post your initial response to the discussion prompt (400-600 words). Your initial post should:
Briefly explain the emerging technology you selected and how it works;
Generate an example application of the technology you selected in the context of your local or regional environment. The example could be a real existing example or a hypothetical one;
Assess the impact of your example technology application on legal, social, ethical, and/or professional issues in your context;
Explain how the technology is applied in these local or regional settings.