Portfolio analytics tools to help compare portfolios deployed on streamlit cloud
-
Updated
Jun 25, 2024 - Jupyter Notebook
Streamlit is an open source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
Turn your data scripts into shareable web apps in minutes, without requiring any front-end web experience.
Portfolio analytics tools to help compare portfolios deployed on streamlit cloud
ISS Data with Web-App and Newsletter option
Streamlit — A faster way to build and share data apps.
Image object detection using YOLO 4, published on the web using Streamlit
Performe data analysis, data visualization with Python and any BI Tool, prediction with machine learning or deep learning and service all of them on web application.
This project involves fetching and analyzing recent NBA scores, player statistics, and news. Technologies used include AWS S3, EC2, Airflow, Snowflake, DBT, Streamlit, Python, and SQL.
A machine learning model built with scikit-learn. It predicts the score at the end of 6th over by using features such as batting_team, bowling_team, and venue
10+ scripts about the COVID-statistics in the Netherlands, accessibile at https://share.streamlit.io/rcsmit/covidcases/main/covid_menu_streamlit.py
Streamlit AI App for Triaging and Segmenting Breast MRI
The Trends in Data Jobs project is a web scraping and data visualization tool designed to track and analyze trends in data-related job postings.
This is our capstone project for the Data Science workshop at neuefische. We worked together with the Institute of Biochemistry from the Veterinary Medical School Hanover to design an application for automated recognition and quantification of Neutrophil Extracellular Traps (NETs)
This project leverages Natural Language Processing (NLP) to classify disaster-related tweets using a Naive Bayes classifier. Implemented with Streamlit, it provides an interactive web application to visualize and analyze tweet classifications in real-time, aiding in timely and effective disaster response.
This Streamlit application predicts the presence or absence of cardiovascular disease using machine learning techniques. It visualizes input data, provides predictions, and performs exploratory data analysis.
A python console and streamlit web app which uses RAG, Chroma DB, Open AI APIs to find answers!
Created by Adrien Treuille, Amanda Kelly, Thiago Teixeira
Released March 27, 2018
Latest release 5 days ago