This project explores the XGBoost model to optimize AWS RDS migrations, focusing on minimizing downtime and enhancing efficiency. It includes data collection, preprocessing, and evaluation of migration issues. Key findings emphasize feature engineering and hyperparameter tuning to improve predictive modeling and reduce operational disruptions. Dataset Link:https://www.kaggle.com/datasets/jurisoo/database-migrations
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This project explores the XGBoost model to optimize AWS RDS migrations, focusing on minimizing downtime and enhancing efficiency. It includes data collection, preprocessing, and evaluation of migration issues. DataseKey findings emphasize feature engineering and hyperparameter tuning to improve predictive modeling and reduce operational disruptions
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bathulaveera2022/XGBoost-for-AWS-RDS-Migration-Optimization_Predicting-and-Reducing-Downtime
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This project explores the XGBoost model to optimize AWS RDS migrations, focusing on minimizing downtime and enhancing efficiency. It includes data collection, preprocessing, and evaluation of migration issues. DataseKey findings emphasize feature engineering and hyperparameter tuning to improve predictive modeling and reduce operational disruptions
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