You Are A Data Scientist For A Major Airline And You Have Bu ✓ Solved

You Are A Data Scientist For A Major Airline And You Have Built A Mode

You are a data scientist for a major airline and you have built a model to predict customer satisfaction. You now want to improve this model by maximizing model fit and minimizing overfitting. Use the dataset airline_satisfaction.csv to perform the tasks below. If you have previously used this dataset, it is unnecessary to download it again as it has not changed. Complete the series of questions, publish your experiment to the AI Gallery, and provide the required links and files as instructed.

Sample Paper For Above instruction

In the context of predicting customer satisfaction for a major airline, the development and optimization of a machine learning model involve several crucial steps aimed at enhancing its predictive accuracy while preventing overfitting. The process begins with data analysis, feature engineering, model selection, and rigorous evaluation followed by proper deployment and documentation.

Initially, the dataset airline_satisfaction.csv must be thoroughly explored to understand the distribution of variables, identify missing values, and detect potential data biases. Exploratory data analysis (EDA) facilitates insights into feature importance, correlations, and patterns within the data that could influence model performance (James et al., 2013). This step is vital to inform feature engineering strategies and model choice.

Next, feature engineering is employed to enhance the dataset's predictive power. Techniques such as selecting relevant features, encoding categorical variables, scaling numerical features, and creating new derived features help improve model accuracy (Hastie, Tibshirani, & Friedman, 2009). Ensuring the features are appropriately processed reduces the risk of overfitting and aids the model's generalization capacity.

Model building involves selecting algorithms suitable for classification, such as logistic regression, decision trees, or ensemble methods like random forests and gradient boosting machines. Cross-validation techniques, such as k-fold cross-validation, are crucial to assess the model's performance on unseen data, thereby detecting overfitting tendencies (Kohavi, 1995). Regularization methods, such as L1 and L2 penalties, can be implemented to constrain model complexity and enhance generalization.

To achieve optimal model fit and prevent overfitting, hyperparameter tuning via grid search or random search is performed. These methods systematically explore combinations of hyperparameters to find the most suitable model configuration that balances bias and variance (Bergstra & Bengio, 2012). Additionally, techniques like early stopping and pruning can be used during training to halt models before they overfit.

After training and tuning, the model is evaluated using unseen validation datasets. Metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve are considered to gauge the model's predictive performance comprehensively (Fawcett, 2006). If the model shows signs of overfitting (training accuracy significantly higher than validation accuracy), further regularization or simpler models are explored.

Finally, the trained and validated model is published to the AI Gallery, which is an online platform for sharing machine learning experiments. This involves exporting the experiment in a suitable format and uploading it to the platform, then copying the URL link. Additionally, a template Excel file recording all modeling results—like model types, hyperparameters, and performance metrics—is prepared and uploaded as required (Azure Machine Learning documentation, 2021).

This systematic process ensures that the predictive model for customer satisfaction is robust, reliable, and capable of generalizing well to new data, ultimately aiding the airline's decision-making process and customer experience strategies.

References

• Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(10), 281-305.
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1137-1143.
• Azure Machine Learning documentation. (2021). Model deployment and publishing. Microsoft. https://docs.microsoft.com/en-us/azure/machine-learning/service/model-deployment