This project focuses on scraping and analyzing data from Mobile.de, the largest European second-hand car marketplace. The goal is to identify the best deals available in the market, using a systematic data-driven approach.
The project is divided into two main parts:
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Ad Scraper (First Notebook): This part involves scraping data from Mobile.de. It includes scripts to systematically collect information about second-hand cars, such as make, model, price, year of manufacture, mileage, etc.
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Market Analysis (Second Notebook): After collecting the data, this section focuses on analyzing the scraped information. The analysis aims to uncover the best deals in the marketplace by considering factors like price, vehicle condition, depreciation rates, and other relevant metrics.
- Ensure you have installed all the necessary dependencies from
requirements.txt.
pip install -r requirements.txt
- Execute the first notebook to start the scraping process. This will collect data from Mobile.de and save it for analysis.
- Run the second notebook for data analysis. This notebook contains various data visualization and statistical tools to identify the best car deals.