This project is a Python-based Data Mining & Analysis tool designed to scout vehicle listings from public marketplaces (OLX). It acts as an intelligent consultant, extracting raw data, enriching it with official market values (FIPE), and detecting opportunities or risks for the user.
- Extraction (Scraping):
- Real-time extraction of vehicle listings using
curl_cffito handle TLS fingerprints and avoid anti-bot blocks. - Dynamic filtering by State (UF), City, Engine type, and Year range.
- Real-time extraction of vehicle listings using
- Transformation (Analysis):
- FIPE Integration: Automatically identifies the vehicle version and fetches the official market price via API.
- Smart Scoring: Classifies deals as "Excellent" (Green), "Fair", or "Expensive" based on FIPE comparison.
- Risk Detection: Scans descriptions for keywords like "Leilão" (Auction), "Sinistro" (Accident), or "RS".
- Usage Metrics: Calculates average KM/Year to identify high-usage vehicles (e.g., ex-taxis).
- Loading (Visualization):
- Modern GUI: A clean, responsive desktop interface built with
CustomTkinter(Light/Dark mode). - Excel Reports: Generates professional
.csvfiles with conditional formatting and active hyperlinks.
- Modern GUI: A clean, responsive desktop interface built with
- Language: Python 3.10+
- Libraries: CustomTkinter (GUI), Pandas & XlsxWriter (Data), Curl_cffi & BeautifulSoup4 (Scraping).
- Concepts: Object-Oriented Programming (OOP), Multi-threading, API Integration, ETL Pipelines.
src/: Contains the core modules:scraper.py: Logic for extracting data from the web.analyser.py: Intelligence layer (FIPE comparison & tagging).fipe.py: API client for official car pricing.models.py: Data validation using Pydantic.
data/: Directory where the Excel reports are saved.main.py: Application entry point (GUI).
- Clone the repository.
- Install the dependencies listed in requirements.txt.
- Ensure you have Python 3.x installed on your system.
- Run the project by executing the main.py file.
Nathan Chaia | LinkedIn