Project overview

By analyzing a dataset of games released on Steam in 2024, we aimed to provide insights into market trends, popular genres, and growth areas to guide strategic investment decisions in the gaming industry.

Installation

1. Clone the repository:

git clone https://github.com/YourUsername/repository_name.git

2. Install UV

If you're a MacOS/Linux user type:

curl -LsSf https://astral.sh/uv/install.sh | sh

If you're a Windows user open an Anaconda Powershell Prompt and type :

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Create an environment

uv venv 

3. Activate the environment

If you're a MacOS/Linux user type (if you're using a bash shell):

source ./venv/bin/activate

If you're a MacOS/Linux user type (if you're using a csh/tcsh shell):

source ./venv/bin/activate.csh

If you're a Windows user type:

.\venv\Scripts\activate

4. Install dependencies:

uv pip install -r requirements.txt

Business Case

An investment company wants to invest in the game industry. By gathering and examining this dataset our aim was to help this company to reach the best decision.

Dataset

For this project, we used "Top 1500 games on steam by revenue 09-09-2024" dataset which can be find on this link: https://www.kaggle.com/datasets/alicemtopcu/top-1500-games-on-steam-by-revenue-09-09-2024?resource=download

As this dataset lack some information that we were looking for, we also used Steam API and web scrapping to added several columns, by looping through each steamID (which is unique for every game).

Main dataset issues

• Missing information in the main dataset
• Finding other sources to complete the dataset

Solutions for the dataset issues

• Using Steam API and web scraping to add the required information to the dataset.

Hypotheses:

• Games released during peak seasons (eg. holidays/ sales) generate higher revenue.
• Certain genres drive higher average revenue.
• Higher-priced games have better ratings and more revenue potential.
• High review scores and positive sentiment correlate with higher revenue.
• Multiplayer and in-app purchases increase revenue potential.

Conclusions and findings:

• The price of the game and its review score don't have any meaningful correlation with each other.
• The top 5 highest grossing games released during the Steam Sale have a mean revenue of $319,158.21, while the top 5 highest grossing games released outside of the Steam Sale have a mean revenue of $2,659,939.32. This is over 8 times higher. The large gap suggests that release timing, specifically in relation to major sales events like the Steam Sale, has a substantial impact on a game's revenue performance. Games released during the Steam Sale seem to generate notably lower revenue on average compared to those released at other times
• Action and Adventure are the genres with the greatest total revenue for the top new games, but average revenue per game is similar across genres. The greatest median revenue was for Simulation and Racing games.
• The average revenue for high score games is higher, proving the hypothesis.
• There is no meaningful correlation between the price and review score.

Next steps

• By using a dataset which contains the costs of game production we can have a better look at profitability of the companies.
• The dataset can be merged with other gaming platforms to have a better overall view of game industry.
• To produce further analysis, we could supplements our data with splits by geography and changes over time.