You will get a real-time tracking web browser application
Top Rated
You will get a real-time tracking web browser application
Top Rated
Project details
The implemented shiny web browser application can be used for real-time tracking. The application could be utilized in the following example use cases,
• vehicles
• flights
• wildlife animals
The current status of the application makes use of an API (Application Programming Interface) to display registration numbers in real-time (every 15 seconds) and can be adjusted depending on the requirements.
The price of this Project is negotiable.
• vehicles
• flights
• wildlife animals
The current status of the application makes use of an API (Application Programming Interface) to display registration numbers in real-time (every 15 seconds) and can be adjusted depending on the requirements.
The price of this Project is negotiable.
What's included $450
These options are included with the project scope.
$450
- Delivery Time 2 days
- Number of Revisions 0
- Content Upload
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Lampros is very experienced in R. He has a lot of integrity, accuracy and quality in his code. He suggested improvements a long the way, and went above and beyond, understood the complexity of the requirement and managed it exceptionally well. The communication was great, he was available, finished his work on time and attentive to the budget. I definitely recommend him and hope to work in the future again.
About Lampros
R, Python, Remote Sensing, Deep Learning and Machine Learning Analyst
100%
Job Success
Paramythia, Greece - 9:40 am local time
I work with R and Rstudio on a daily basis. I can work with machine learning algorithms based on almost all CRAN, Github or Gitlab repositories. I can utilize visualization R packages such as ggplot2, plotly, tmap, leaflet, mapview. I can create shiny applications (shiny.rstudio.com/gallery/) and report my results in .pdf, word, .html or any other available format using Rmarkdown.
Moreover, I'm capable of using the hybrid 'Rcpp' and 'RcppArmadillo' R packages to improve the efficiency of R code and the Keras and Pytorch deep learning libraries for regression, classification, object detection or image segmentation (with or without pre-trained models).
Steps for completing your project
After purchasing the project, send requirements so Lampros can start the project.
Delivery time starts when Lampros receives requirements from you.
Lampros works on your project following the steps below.
Revisions may occur after the delivery date.
Implementation of the real-time application
Implementation of the real-time application depending on the client's requirements
deployment of the web browser application
The application will be deployed to shinyapps.io so that it can be accessed by the client