Prototyping a Machine Learning Application with Streamlit, FastAPI, Hugging Face and Docker
Web app to predict knee osteoarthritis grade using Deep Learning and Streamlit
🔍 Flask-based web application for image classification. The application leverages the ResNet50 model from Keras to classify uploaded images.
AgroNomics is a machine learning web application that forecasts crop prices using historical agricultural data, seasonal trends, and region-specific variables. Built with Flask and scikit-learn, it provides nationwide coverage with state- and district-level insights, enabling farmers to make accurate, data-driven market decisions.
Interactive Streamlit app that predicts house prices using multiple machine learning models. Users can adjust real-world features via sliders, compare model performance, and explore feature impact through visual insights.
A ML application focused on EDA and basketball analytics, showcasing data visualization and insights using Python and relevant libraries.
A movie recommendation engine that recommends you movies based on their similarity to a movie that you've already watched and liked.
A simple mahcine learning application for stock prices, demonstrating data preprocessing, model training, and deployment using scikit-learn.
Comparative study of supervised machine learning architectures for multi-class medical classification using real-world clinical datasets.
ML Web-App using Tensorflow and Django.
Flusk Tutorial is featuring a to Flask, a Python web framework. It may include basic or tutorials covering Flask fundamentals for Machine Learning.
AgroNomics is a machine learning web application that forecasts crop prices using historical agricultural data, seasonal trends, and region-specific variables. Built with Flask and scikit-learn, it provides nationwide coverage with state- and district-level insights, enabling farmers to make accurate, data-driven market decisions.
Live ML app that predicts which GP practices will overspend on NHS prescribing. Upload a raw NHSBSA file, get at-risk practices ranked by predicted overspend. Random Forest (R²=0.95) built with scikit-learn and Streamlit.
This project predicts housing prices in Boston using machine learning techniques.
🏠 Built with machine learning, Dockerized for containerization, and automated with GitHub Actions for seamless CI/CD deployment with Render.
AI Powered Loan Approval Prediction System using Machine Learning & Streamlit
Web App to do Sentiment Analysis on the given text
🌸 A beginner-friendly Streamlit web app that predicts Iris flower species using a Random Forest classifier. Interactive, educational, and deployed on Streamlit Cloud.
Streamlit-based machine learning app for house price prediction with automated model selection, tuning, and deployment-ready setup.
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