Implementation of Microbe Prediction Using Machine Learning Algorithms
Dr. SUDHA KATKURI, Dr. D. HEMA LATHA, Dr. D. RAMA KRISHNA REDDY
Assistant Professor, Dept. of Business Management, RBVRR Women’s College, Narayanaguda, Hyderabad, TS, India
Assistant Professor, Dept. of Computer Science, Veeranari chakali Ilamma Women’s University, Hyderabad, TS, India
Assistant Professor in Computer Science, Dept. of Mathematics, Osmania University, Hyderabad, TS, India.
ABSTRACT
Microorganisms are fundamental to ecosystems, human health, and various industrial processes. The rapid and accurate identification of microorganisms is crucial for diagnosing diseases, monitoring environmental changes, and advancing biotechnological applications. Traditional methods of microbial identification, including culturing and microscopy, are often labor-intensive and time-consuming, necessitating the development of computational techniques to automate and improve accuracy.
This project presents a comprehensive approach to microbe prediction by leveraging advanced machine learning algorithms applied to a dataset containing genomic and morphological features of ten different microorganism species. The study investigates the performance of four classification algorithms — K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and Decision Trees — to classify microorganisms based on multiple quantitative features extracted from images and genetic data.
The methodology involves extensive data preprocessing steps such as cleaning, normalization, polynomial feature expansion, and dimensionality reduction using Principal Component Analysis (PCA) to enhance model performance. Rigorous experimentation and comparative analysis reveal that ensemble methods, particularly Random Forest, provide superior classification accuracy, robustness, and generalizability across unseen data.
In addition to model development, a user-friendly web interface built using Streamlit facilitates real-time microorganism prediction, making the system accessible to researchers and practitioners without deep technical expertise. The platform enables the input of feature values and instantly returns predicted microorganism classes along with relevant biological descriptions and preventive measures, thereby bridging the gap between computational predictions and practical microbiological insights.
This project contributes to the emerging intersection of microbiology and machine learning by demonstrating an effective pipeline from raw data to deployable predictive tool. Its implications span environmental monitoring, medical diagnostics, and industrial microbiology, emphasizing the potential for machine learning to revolutionize microbial identification and understanding.
Key Words – K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and Decision Trees, Stream lit user interface software.
