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.
An end-to-end Machine Learning + Flask web application that predicts Heart Disease, Liver Disease, Kidney Disease, and Breast Cancer using trained ML models and a modern UI.
ML Web-App using Tensorflow and Django.
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.
Flusk Tutorial is featuring a to Flask, a Python web framework. It may include basic or tutorials covering Flask fundamentals for Machine Learning.
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.
Web App to do Sentiment Analysis on the given text
🔍 Flask-based web application for image classification. The application leverages the ResNet50 model from Keras to classify uploaded images.
This web app uses a Random Forrest Regressor model to suggest a suitable price for used cars.
Credit Card Transaction Fraud Detection App built using XGBoost, FastApi, Streamlit and Docker
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