Category: Machine Learning & Bioinformatics Consulting
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Predicting Malaria Incidence from Climate Data Using Machine Learning
The project aimed to predict malaria incidence using climate and geographical data through machine learning, deploying a Streamlit web app for visualization across 98+ countries. With data from WHO and others, the CatBoost model achieved a 96.7% correlation. It provides easily accessible insights for researchers and policymakers, addressing malaria’s global health challenge.
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AutomatedMLPack: A Python Package for End-to-End Automated Machine Learning
AutomatedMLPack is a Python package designed for streamlined automated machine learning workflows, enabling efficient data ingestion, model training, and evaluation through a command-line interface. It supports classification and regression tasks, offers flexible feature selection, and provides visualizations and evaluation reports, significantly enhancing productivity in machine learning projects.
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Building Scalable ML Applications: A Practical Approach
The project involved developing a comprehensive machine learning pipeline for classification and regression tasks, culminating in a Flask-based web application deployed on Azure. It features automated deployment via GitHub Actions, ensuring a user-friendly interface for real-time predictions. Key achievements include a modular pipeline and seamless integration, enhancing accessibility in ML applications.
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BIOPRED: A Machine Learning-Based Web Application for Accurate Bioactivity Prediction, Drug Repurposing, and Molecular Docking
BIOPRED is a machine learning-driven web application developed for predicting drug-target interactions and supporting molecular docking. Utilizing ChEMBL data, it employs various algorithms for both regression and classification tasks with high accuracy. The user-friendly platform enables researchers to input SMILES strings and get bioactivity predictions, facilitating drug repurposing efforts.
Your Bioinformatics Developer 