Self hosted web analytics for endurance athletes
A step-by-step guide for deploying your Dash application on Render
Summary of 'genomic analysis' conducted at the Right Information company. Code for our portfolio page.
This app integrates the Segment Anything Model (SAM) with Sentinel-2 data. The app is built using Dash Plotly and dash leaflet. It allows segmenting satellite images using the two ways provided by SAM: automatic mask generator and prompt segmentation (using points and bounding boxes).
Connect gpus with your eyes.
Using batman-adv to connect several raspberrypi to a mesh network and creating a vernemq cluster
Sentiment analysis for tweets written in Portuguese-Brazil
A dashboard for helping beginners identify trading opportunities through technical analysis, fundamental analysis, and possible future projections.
A complete stack for running a Docker container with Python 3.8 and all the necesary dependencies for Dash Plotly with GUnicorn as HTTP Server.
This is a web application to visualize various famous technical indicators and stocks tickers from user
Simulation model to run scenarios on staffing/operations readiness for a call center
Application to increase traffic to an account and gain followers that actively engage with the account.
Dash web app for binary classification model selection
Tango trees are an advanced binary search tree that aims to optimize the search operation by minimizing rotations. By maintaining a set of preferred paths and adjusting them dynamically, Tango trees ensure efficient access to frequently searche
Dash App - Simulation of double pendulum equations of motion
This project provides an integrated environment with tools to study traffic flow in Teatinos, Málaga. It automatically collects, refines, and stores traffic information for effective historical analysis. Additionally, it uses an Agent-Based Model (ABM) to simulate traffic behavior.
A Reddit Post Sentiment Analysis Dashboard created with Python using PRAW and Dash Plotly
This is a Web Application meant for interactively visualizing the K-means clustering algorithm. The purpose is to showcase the strengths and limitations of the method under different settings (data shape, size, number of clusters, number of centroids, initialization method etc.).
Add a description, image, and links to the dash-plotly topic page so that developers can more easily learn about it.
To associate your repository with the dash-plotly topic, visit your repo's landing page and select "manage topics."