This repository contains the full workflow of a data science project that scrapes, analyses, and models campsite information from camping.info. It was created as a final project for a data‑science course and demonstrates how to collect data from the web, clean and explore it, and build predictive models using Python.
The project aims to understand factors that influence campsite characteristics (such as price and rating) and to build simple models that can estimate these values. To do this, we scraped thousands of campground listings across Europe from camping.info and assembled a dataset with the following fields:
country – the country where the campground is located
price – average nightly price in euros (may be missing)
rating – average user rating (stars)
size_of_site – total area of the campground in hectares
total_pitches – number of pitches (camping spots)
caravans – indicator of caravan accommodation
sea , lake, river – indicators of nearby water bodies
mountain – indicator of mountainous terrain
Height – altitude above sea level (metres)
next_city – distance to the nearest city (km or descriptive text)
transport – public transport availability or travel time
Missing values are common because information on camping.info is inconsistent. The notebook includes cleaning steps such as dropping columns with too many missing values and imputing numeric data.
Camping.Info - Project/
├── NOTEBOOKS/
│ └── Final project - Camping.Info.ipynb # main Jupyter notebook with
scraping, EDA and modelling
├── data1.csv, data2.csv, ... # CSV files containing the scraped
data
├── lst_links1.csv, lst_links2.csv, lst_links3.csv # search page URL lists used
for scraping
├── <hebrew_name>.py # Python script with scraping helper
functions
├── chromedriver.exe # ChromeDriver binary used by
Selenium
├── LICENSE.chromedriver # licences for ChromeDriver and
third‑party components
└── README.md # project description (this file)
Clone the repository
gitclone https://github.com/yourusername/Camping-Project.git
cd Camping-Project/Camping.Info\ -\ Project
Install Python dependencies
The project uses Python 3.8+. Install required packages with pip:
pipinstall -r requirements.txt
If there is no requirements.txt, install the main packages manually:
pipinstall pandasnumpy beautifulsoup4seleniumrequests scikit-learn
matplotlibseaborn
Set up ChromeDriver
The scraper uses Selenium with ChromeDriver. If you are on Windows, chromedriver.exe is included. On Linux/Mac, download the appropriate version from https://chromedriver.chromium.org/downloads and place it in the project directory or ensure it is in your system PATH.
Run the notebook
Open the notebook with Jupyter:
jupyternotebook "NOTEBOOKS/Final project - Camping.Info.ipynb"
The notebook is divided into sections:
Scraping links – uses lists of search‑result URLs to collect individual campsite links.
Scraping campground data – visits each campsite and extracts the features listed above.
Data cleaning – handles missing values, converts strings to numeric types and encodes categorical
variables.
Exploratory analysis – visualises distributions, correlations and geographic trends using matplotlib
and seaborn.
Modelling – builds regression models (decision tree and linear regression) to predict price and a
Gaussian Naive Bayes classifier for rating categories.
Explore the dataset
The scraped data are stored in data1.csv, data2.csv, ... You can load them in Python using pandas.read_csv() for further analysis or to extend the model.
The exploratory analysis reveals that campsite ratings cluster between ~3.5 and 4.5 stars and that prices vary widely across countries. There is a weak positive correlation between price and site size or amenities. The regression models capture some patterns but are limited by missing data and heterogeneity; the decision‑tree model outperforms linear regression but still has moderate accuracy. The classification model categorises campsites into rating tiers with modest success. Improving the models would require more complete data or additional features, such as textual reviews.
Contributions are welcome! Feel free to open issues or pull requests if you spot bugs, have suggestions or want to add new analyses.
The repository contains chromedriver.exe and a combined licence file (LICENSE.chromedriver) which covers ChromeDriver and various third‑party components. Please review that file for details on usage rights. The rest of the project’s code is provided for educational purposes; you may modify and reuse it at your discretion.