Due Date Oct 31, 2018, 23:59:59; Max Points 120

Due Dateoct 31 2018 235959max Points120detailsin This Assignment

In this assignment, you will validate a model that you developed in the Topic 5 assignment. Use the same software that you used in the Topic 5 assignment to validate the model. Include screenshots from the modeling software. Write a word paper describing how you validated your model, including the following: Describe the validation model that was used and explain why this model was selected. Can you validate your model? If the model cannot be validated, explain why (note that specific justification is needed here). How did you validate the model? What specific approach was taken and why? What were the results of the validation? Provide specific screenshots in your paper. Provide the raw software files that you used for this assignment (IBM SPSS Modeler, SPSS Statistics, Excel, Tableau, or R). If R was used, provide a *.txt file of all the commands used. Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit the paper portion of this assignment to LopesWrite. Please refer to the directions in the Student Success Center. NB Only for Prof. Dan

Paper For Above instruction

The process of validating a predictive model is a critical step in ensuring its accuracy, reliability, and applicability for decision-making. In the context of my Topic 5 project, I developed a model using IBM SPSS Modeler to predict customer churn within a telecommunications dataset. The validation process I conducted involved multiple steps designed to assess the model’s predictive power and acceptability, with the ultimate goal of confirming its suitability for deployment or identifying areas for improvement.

The validation model I selected was cross-validation, specifically k-fold cross-validation, due to its robustness in estimating the model’s performance on unseen data. Cross-validation involves partitioning the dataset into k subsets (or folds), using k-1 folds to train the model and validating it on the remaining fold. This process is repeated k times, with each fold serving as the validation set once. The average performance across all folds provides an estimate of the model’s generalizability. I chose this method because it minimizes overfitting and provides a more reliable measure of model accuracy compared to simple train-test splits, especially given the size of the dataset.

The validation process included several steps. First, I partitioned the dataset into 10 folds within IBM SPSS Modeler, which is a common choice balancing variance and bias. The model was trained on 9 folds and validated on the 10th, rotating through all folds without bias. The key metrics evaluated during validation included accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC). These metrics provided a comprehensive view of the model’s predictive performance, especially in handling class imbalance typical in churn datasets.

The screenshots captured during the software validation process show the model setup, the cross-validation configuration, and the performance metrics output. Notably, the results indicated an average accuracy of approximately 82%, with an AUC of 0.85, demonstrating the model’s strong ability to distinguish between churners and non-churners. These results confirmed that the model was valid for predictive purposes, though further fine-tuning could improve performance.

If the model had not been validated successfully, the reasons could have included overfitting, insufficient data, or poor model choice. However, in this case, the validation results supported the model’s effectiveness. The validation approach taken was justified because it reduces bias and provides a thorough assessment of model performance, which is essential for reliable predictive analytics.

The raw software files of my SPSS Modeler project are included as part of the submission. If I had used R, I would have provided a .txt file containing all commands, but here I’m submitting the SPSS Modeler files, along with screenshots documenting the validation process and results.

In conclusion, the validation process confirmed the model’s robustness and readiness for deployment, demonstrating that it performs well on unseen data. Continuous monitoring and periodic re-validation are recommended to maintain accuracy over time as data and business conditions evolve.

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