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Rodrigo Guadalupe Medina (@RodrigoAGM)

RodrigoAGM

Stars
36
Global Rank 430,311 (Top 15 %)
Followers 11
Following 6
Registered almost 8 years ago
Most used languages

Python
23.5 %

C++
11.8 %

Dart
11.8 %

TypeScript
11.8 %

Kotlin
11.8 %

Go
5.9 %

Java
5.9 %

HTML
5.9 %

JavaScript
5.9 %

Jupyter Notebook
5.9 %
Location 🇵🇪 Peru
Country Total Rank 357
Country Ranking

Dart
50

Java
51

Kotlin
62

Go
110

C++
143

Jupyter Notebook
149

Python
171

TypeScript
247

HTML
603

JavaScript
946

Nearby-Places-Finder

App developed using the Google Maps API and Google Places API. The app consists on finding the nearest places around your location just by clicking on a button;moreover, this app also allows making searches with addresses and key words. This was made using Kotlin and Java programming languages on Android Studio.

flutter-drive-filer

A google drive filer made with flutter

take-care-backend

Backend developed with Nodejs using typescript and MySql database.

subastapp-mobile

travelling-salesman-project

Project for the course of algorithmic complexity at UPC.

Jupyter Notebook

Shortest-route-algorithm-project

Rubbish pick up process optimization with shortest route algorithm, Google Maps API and Gmaps.js. Try it at https://rodrigoagm.github.io/Shortest-route-algorithm-project/Project/index.html

tsp-genetic-algorithm

Solution for the Travelling Salesman Problem using genetic algorithm in Python. ⭐ 💥

heart_attack_prediciton

Heart Attack prediction using a naive implementation of neural networks

teloh-api

Solar_Fox_UPC

Game based on Atari game Solar Fox and developed as final project of the programming 1 course at UPC.

take-care-app

DistributedExamples

Examples of distributed programs using golang

River-Raid

Game developed as a final project of the programming 1 subject at UPC

SI404-artificial-intelligence

Algorithms from the artificial intelligence course in UPC 2020-1.

CC76-Complejidad-Algoritmica

Algoritmos desarrollados en clase de complejidad algorítmica UPC.

CatchUp

Simple news app

data-optimization-functions

Data optimization functions implemented using a dataset readed as a list of lists. Functions implemented are: data normalization, maximin/minimax, leximin/leximax and weighted average.