Lab 3 Part 1 Neural Networks_GladysVillafuerte_301264680

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Big Data and Predictive Analysis Lab 3 Part 1 Predictive Modeling Using Neural Networks Submitted by Gladys Anne Villafuerte

Predictive Modeling Using Neural Networks a. In preparation for a neural network model, is imputation of missing values needed? Why or why not? Type your answer here: In preparation for a neural network model in SAS Miner, imputation of missing values may be necessary depending on the amount and nature of the missing data. If the dataset contains a small amount of missing values, it may be possible to simply remove the corresponding rows or columns from the dataset without significantly impacting the overall performance of the neural network model. However, if a significant proportion of the data is missing, removing the affected rows or columns can result in a substantial loss of valuable information and may lead to a biased model. In such cases, imputation techniques can be used to estimate the missing values based on the available data. SAS Miner provides several imputation methods such as mean imputation, hot deck imputation, and regression imputation. These methods can help fill in the gaps in the data and produce a complete dataset that can be used to train the neural network model. It's important to note that the choice of imputation method should depend on the specific characteristics of the dataset and the type of missing data. Also, imputation should be done carefully to avoid introducing biases or inaccuracies into the dataset, which can affect the performance of the neural network model.

b. In preparation for a neural network model, is data transformation generally needed? Why or why not? c. Add a Neural Network tool to the Organics diagram. Connect the Impute node to the Neural Network node. Screenshot the results window and put your name in a title or footnote. Type your answer here: Neural network model to improve the accuracy and performance of the model by ensuring that the data is in the appropriate format and range, and by selecting and transforming the input variables to improve the predictive power of the model. However, the specific data transformation techniques used should be based on the specific characteristics of the dataset and the nature of the problem being addressed

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d. Set the model selection criterion to average squared error. e. Run the Neural Network node and examine the validation average squared error. How does it compare to other models? NEURAL NETWORK DIAGRAM

REGRESSION Type your answer here: The average squared error of Neural Network is 0.132354 while the Resgression is 139545.

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