Methodology and Tools - Metodología y Herramientas

EN:
Environment setup:
* A virtual environment (vehicles_env) was created, and the pandas, plotly-express, and streamlit libraries were installed.
* A GitHub repository was set up with essential files (README.md, .gitignore, requirements.txt).

Exploratory data analysis (EDA):
* The vehicles_us.csv dataset was downloaded.
* Exploratory graphs were generated in a Jupyter Notebook (EDA.ipynb), located in the notebooks folder.

Web application development:
* The app.py file was created to structure the app with Streamlit.

The following were implemented:
* An informative header.
* A button to build a histogram (axis: odometer).
* A button to build a scatter plot (odometer vs. price).
* Plotly Express was used to generate interactive visualisations.
* The repository files (requirements.txt, README.md) were updated.

Deployment on Render:
* The repository was connected to Render.com.
* The build (pip install -r requirements.txt) and run (streamlit run app.py) commands were defined.
* The application was made publicly accessible via a web link.


ES:
Configuración del entorno:
* Se creó un entorno virtual (vehicles_env) y se instalaron las librerías pandas, plotly-express y streamlit.
* Se configuró un repositorio GitHub con archivos esenciales (README.md, .gitignore, requirements.txt).

Análisis exploratorio (EDA):
* Se descargó el dataset vehicles_us.csv.
* Se generaron gráficos exploratorios en un Jupyter Notebook (EDA.ipynb), ubicado en la carpeta notebooks.

Desarrollo de la aplicación web:
* Se creó el archivo app.py para estructurar la app con Streamlit.

Se implementaron:
* Un encabezado informativo.
* Un botón para construir un histograma (eje: odometer).
* Un botón para construir un gráfico de dispersión (odometer vs. price).
* Se utilizó plotly-express para generar visualizaciones interactivas.
* Se actualizaron los archivos del repositorio (requirements.txt, README.md).

Despliegue en Render:
* Se conectó el repositorio a Render.com.
* Se definieron los comandos de construcción (pip install -r requirements.txt) y ejecución (streamlit run app.py).
* La aplicación quedó accesible públicamente a través de un enlace web.