Steam-Games-Visualisation

This project visualises data of various games from Steam and analyses different trends in them.

More information about the project can be found in Proposal.pdf and Project_Presentation.pdf files

Team:

• Denis Troegubov (DS-01)
  Checkpoint 1:

  • Scraped and cleaned data from the websites.

  Checkpoint 2:

  • Wrote part of backend and frontend.

• Diana Tsoi (DS-02)
  Checkpoint 1:

  • Made Exploratory Data Analysis (EDA) and analyzed trends.

  Checkpoint 2:

  • Wrote part of backend and frontend.

• Victor Mazanov (DS-02)
  Checkpoint 1:

  • Made a list of possible trends, scraped data from the websites.

  Checkpoint 2:

  • Wrote part of backend and frontend.

Setup and Run Instructions

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/Data-Wrangling-and-Visualisation/Steam-Games-Visualisation.git
   cd Steam-Games-Visualisation

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

Running the Project

1. Scrape the data:

   • Navigate to the scrape directory:

     cd scrape

   • Run the Jupyter Notebook to scrape the data:

     jupyter notebook Steam_games_scrape.ipynb

   • Follow the instructions in the notebook to scrape and save the data to games.json.

2. Navigate to the eda directory:

   cd ../eda

3. Run the Jupyter Notebook for data analysis:

   jupyter notebook games.ipynb

4. Open the games.ipynb notebook in your browser and run the cells to visualize the data.

Troubleshooting

• If you encounter a ValueError related to nbformat, ensure you have the correct version installed:

  pip install --upgrade nbformat

• If you face any issues with missing files, ensure that the games.json file is located in the scrape directory.

5. Command for project running

docker-compose up -d