Iris Species Recognition: A Fusion of Machine Learning and NoSQL Databases
Year: 2025
Type: article
Abstract: This paper presents a hybrid system for classifying iris flower species using classical machine learning (ML) algorithms integrated with a NoSQL database. Utilizing the Iris dataset, the system applies algorithms such as Support Vector Machines (SVM), Logistic Regression, and Random Forest to accurately identify the species based on floral measurements. To ensure scalability, the prediction outputs are stored in MongoDB, enabling persistent, queryable storage. The study demonstrates the practical value of integrating ML with flexible data architectures for real-time applications in botany and education.
Author: Dr. Ahsan Saud Qadri
Institution: Deccan College Post Graduate and Research Institute
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912369908
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