ICT371-Tutorial 8 T124 (1)
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King's Own Institute**We aren't endorsed by this school
Course
ICT 371
Subject
Information Systems
Date
Jan 10, 2025
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5
Uploaded by sujit3mhjn
ICT371 Tutorial 7 Compiled by: Dr. Praneel Chand/ Dr. Muhammad Zeeshan Baig25/03/2024ICT371 Artificial IntelligenceTutorial 8Note: Complete the task and submit the tutorial on Moodle by the end of the tutorial classTopic: Week 8: Building an Artificial Neural Network for PredictionThis is the overview of the process that you need to createDrag and drop the dataset and make sure there are no missing values in the attribute columns that you want to make the prediction accordingly. For example, if you have any missing value in the column “age” you can replace it with the average age of all the customers
ICT371 Tutorial 7 Compiled by: Dr. Praneel Chand/ Dr. Muhammad Zeeshan Baig25/03/2024In the “filter example” operator, you can filter the rows that have some conditions. For example, I want to have the rows that their “Churn” column is not missing. See the figure below:“Set role” is used here to determine the value of what attribute you want to predict. In this case, we want to predict whether a customer is churn or loyal. Hence, we consider the “churn” attribute as a “label”
ICT371 Tutorial 7 Compiled by: Dr. Praneel Chand/ Dr. Muhammad Zeeshan Baig25/03/2024Keep in mind that Artificial Neural Network (ANN) only operate on numerical data. But we have other data of data in our dataset. So we need to convert them to numerical. Use the operator “nominal to numerical”. Choose the codding type “dummy”.You need to split the data because we want to have data for training and testing. The training set is for learning purposes and testing is for validation of what the model has learned. In this case, we assume 70% for training and 30% for testing.
ICT371 Tutorial 7 Compiled by: Dr. Praneel Chand/ Dr. Muhammad Zeeshan Baig25/03/2024ANN operator has many parameters that you can change and see their effect on the performance of the model. The “Apply model” operator is to build the learned model and receive the testing data for validation. Lastly “performance” operator shows the accuracy of the modelYou should get the results as figures below:Activity:Download the hotel app dataset from Moodle and apply the neural network to classify if the customer is loyal or churn. Use the cross-validation operator to evaluate the performance of your neural network. Change the parameters of your neural network including the number of hidden layers and decay rate to see the effect on performance. You need to place the screenshots of the process and results and submit the tutorial on Moodle.
ICT371 Tutorial 7 Compiled by: Dr. Praneel Chand/ Dr. Muhammad Zeeshan Baig25/03/2024