Implementing Machine Learning on AWS Standalone Cluster
DOI:
https://doi.org/10.4108/v8.9432Keywords:
AWS, EC2, Logistic Regression, Scikit-learn, Machine Learning, Cloud Computing, Iris Dataset, HadoopAbstract
INTRODUCTION: This paper presents the implementation of a machine learning classification pipeline on Amazon Web Services (AWS) EC2 in standalone cluster mode using Apache Hadoop and the scikit-learn library. OBJECTIVES: To configure a cloud-based standalone cluster, deploy a Logistic Regression classifier on the Iris dataset, and evaluate its performance using standard metrics. METHODS: An AWS EC2 t2.micro instance running Ubuntu 24.04 LTS was provisioned and configured as a Hadoop standalone cluster. Python libraries including scikit-learn, pandas, Matplotlib, and Seaborn were installed. The 150-sample Iris dataset was split 80/20 for training and testing. Logistic Regression was applied with a maximum of 200 iterations. RESULTS: The model achieved 100% classification accuracy on the test set with a precision, recall, and F1-score of 1.00 across all three Iris species. The confusion matrix confirmed zero misclassifications. Training completed in under two seconds on the free-tier instance. CONCLUSION: Cloud-based standalone clusters on AWS EC2 provide a cost-effective environment for deploying machine learning workloads. Logistic Regression with scikit-learn delivers 100% accuracy on the Iris benchmark, demonstrating the viability of cloud-hosted ML pipelines for educational and small-scale production use cases.
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