Description:
You will use Python to process the German Credit DataSetLinks to an external site. (https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)) to make predictions on users' credit ratings. Review the German Credit DataSetLinks to an external site. (https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data). It has 1,000 observations. Take a look at the data and remove a few attributes that you think do not help to determine the creditworthiness of a customer. The last column is whether a customer is actually "good" or "bad" (i.e., their credit rating). Evaluation Method: If your predictions are correct (good or bad)
for each example, that counts as 0. If your prediction for a good customer is "bad" add 1 to your total. If your prediction for a bad customer is "good", add 5 to your total. The lower the total the better your machine learning model. What you need to do. 1) Partition the data into training (980 data points) and testing (last 20 data points) datasets. 2) Train any *two* machine learning models discussed in class (e.g. decision trees, logistic regressions, SVM) with the first 980 data points, 3) Test the models on the last 20 data points using the evaluation method described above. See if you can improve the performance by changing various parameters in the model. ( Include the notebook of your python code.)
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