This project consists of an interactive web application developed with Streamlit for exploratory analysis of vehicle advertisement data.
The application allows users to visualize key characteristics of the dataset through interactive charts and controls, making it easier to identify patterns and relationships between vehicle attributes.
The project combines data analysis, visualization, and web deployment techniques to provide an accessible analytical tool for users.
Online vehicle marketplaces contain large volumes of listings, making it difficult to quickly identify trends and relationships within the data.
This application helps users explore vehicle advertisement information by providing interactive visualizations that reveal:
- Mileage distribution
- Relationships between vehicle price and mileage
- General characteristics of listed vehicles
- Data patterns through interactive exploration
Such insights can support market analysis and data-driven decision-making.
The project uses the dataset:
vehicles_us.csv
The dataset contains information about vehicle advertisements, including price, model year, mileage, condition, fuel type, transmission type, and other vehicle characteristics.
- Python
- Pandas
- Plotly Express
- Streamlit
- Jupyter Notebook
- Git
- GitHub
- Render
Before building the web application, an exploratory analysis was conducted to understand the dataset structure and identify important variables.
The analysis included:
- Data cleaning and preprocessing
- Missing value handling
- Exploratory visualizations
- Variable relationship analysis
- Dataset quality assessment
The complete exploratory analysis can be found in:
notebooks/EDA.ipynb
The application provides:
- Interactive histogram visualization
- Interactive scatter plot visualization
- Dynamic user controls through checkboxes
- Real-time chart generation
- Responsive web interface
Interactive histogram showing the distribution of vehicle mileage.
Interactive scatter plot illustrating the relationship between vehicle mileage and price.
The application is available online:
https://vehicles-app-epdt.onrender.com
- Clone the repository:
git clone https://github.com/etienne-94/vehicles-app.git- Navigate to the project directory:
cd vehicles-app- Install dependencies:
pip install -r requirements.txt- Launch the Streamlit application:
streamlit run app.pyvehicles-app/
│
├── app.py
├── vehicles_us.csv
├── requirements.txt
├── README.md
├── .gitignore
│
├── notebooks/
│ └── EDA.ipynb
│
├── .streamlit/
│ └── config.toml
│
└── images/
├── histogram.png
└── scatter_plot.png
The dataset used in this project was provided by TripleTen for educational purposes and is included exclusively for learning and portfolio development.
Etienne Viegas dos Santos
Data Analytics Student | TripleTen
GitHub: https://github.com/etienne-94