Use the attached python notebook as a reference–> scania_failures.ipynb
1. Train a neural network to predict the class variable
2. Display the learning curve (Accuracy vs num of epochs) for 4 values of learning rate (0.0001, 0.001, 0.01 and 0.1)
3. Test your model on the test set and report the accuracy
4. Display the confusion matrix
5. Further optimize the model by changing the number of nodes and layers to obtain the best true positive rate
Goal is to improve the Area Under the Curve (AUC) of the ROC. The baseline is 0.77. Try to change the number of nodes, number of layers, learning rate and number of epochs to optimize the AUC. Let us see who can get the best AUC.
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.
Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
Read moreThanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.
Read moreYour email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.
Read moreBy sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.
Read more