Hi, I am José Rojas.
Frontend Developer.
Frontend Developer.
App to see the current maps rotation and legends upgrades of Apex Legends using an API from apexlegendsstatus.com.
This project was made using the next technologies:
HTML - CSS - JSX - REACT - VITE
Display the current map rotation and time remaining in Apex Legends on your Stream Deck.
Display the next map rotation in Apex Legends on your Stream Deck.
App to see the current and upcoming events that Ticketmaster has in Mexico. If you want to reserve some event you will be redirect to the Oficcial Ticketmaster page.
This project was made using the next technologies:
HTML - CSS - JSX - REACT - VITE
This is an interactive application developed with Python and Streamlit for managing cooking recipes. It allows users to view, add, modify, and delete ingredients, all stored in a JSON file that is created automatically.
CRUD (Create, Read, Update and Delete)
📜 View stored recipes along with their ingredients.
➕ Add new recipes to the database.
📝 Add ingredients to existing recipes.
❌ Remove ingredients from a recipe.
✏️ Update the name of a recipe.
Make sure you have Python 3.11 or newer version installed.
To run the app, use:
streamlit run recipes.py or python -m streamlit run recipes.py
Classic Tetris game made with Python and Pygames library
When one line completes that line dissapare and all the pieces on top descend one position, up to 4 line to score a tetris, the falling pieces speed up over time. The game ends when the pieces are piled up until they reach the top (in the visible area).
HUD: It's shows grid, on the left high score, on the right side current score and what's next figure.
Move the falling pieces using left and right arrow keys, Up to rotate and down to fall faster.
This project was made using the next technologies:
Python - Pygames
This web app allows you to identify animal images using a classification model trained with the Animals-10 dataset from Kaggle. It is built with Streamlit and a pre-trained Keras model.
This model is a Convolutional Neural Network (CNN) that uses transfer learning with the MobileNetV2 architecture. It has been trained with over 28.000 images from a dataset to classify them into 10 different animal categories.
This classification model is designed to predict the main category of an animal image and provide the top 3 predictions with their respective confidence percentages.
Este proyecto analiza los datos de ventas de cuatro tiendas utilizando Python y bibliotecas como pandas, matplotlib y seaborn. Se exploran métricas clave como ingresos, calificaciones de clientes, productos más vendidos, ventas de acuerdo a la ubicación y costos de envío. Para identificar cual tienda es la menos eficiente y se debe vender para iniciar un nuevo emprendimiento.
Cálculo y visualización de ingresos totales por tienda Análisis de ventas por categoría Comparación de ventas entre tiendas Cálculo de calificación promedio por tienda Identificación de productos más y menos vendidos Cálculo del costo promedio de envío por tienda
El análisis incluye gráficos de barras que muestran: Ingresos totales Ventas por categoría y tienda Top de productos más vendidos