Project to analize the Used Car Listings on eBay Kleinanzeigen

We will be working on a dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.

The dataset was originally scraped and uploaded to Kaggle. The version of the dataset we are working with is a sample of 50,000 data points that was prepared by Dataquest including simulating a less-cleaned version of the data.

The data dictionary provided with data is as follows:

• dateCrawled - When this ad was first crawled. All field-values are taken from this date.
• name - Name of the car.
• seller - Whether the seller is private or a dealer.
• offerType - The type of listing
• price - The price on the ad to sell the car.
• abtest - Whether the listing is included in an A/B test.
• vehicleType - The vehicle Type.
• yearOfRegistration - The year in which the car was first registered.
• gearbox - The transmission type.
• powerPS - The power of the car in PS.
• model - The car model name.
• kilometer - How many kilometers the car has driven.
• monthOfRegistration - The month in which the car was first registered.
• fuelType - What type of fuel the car uses.
• brand - The brand of the car.
• notRepairedDamage - If the car has a damage which is not yet repaired.
• dateCreated - The date on which the eBay listing was created.
• nrOfPictures - The number of pictures in the ad.
• postalCode - The postal code for the location of the vehicle.
• lastSeenOnline - When the crawler saw this ad last online.

Tools

• Python3
• Pandas
• Jupyter Notebook
• Pylint

Hands On

To run the project, just install pylint and run the script that calculates the score.

$ pip install pylint

$ pylint analyze_ebay_car_sales.py