Analyzing Steam Games 2025: Genres, Players and User Ratings

As part of my Master in Data Science & AI at Evolve, I worked on a data analysis project using a real Steam games dataset from 2025.

The goal of the project was to practice the full workflow of a data analysis project: understanding the dataset, cleaning it, transforming variables, creating visualizations and explaining the results in a way that is easy to understand.

The main question I wanted to answer was:

Which Steam genres have the highest average estimated users per game, and which ones have the highest average playtime?

Later, I expanded the analysis with two additional questions:

• Are there clear differences between free and paid games?


• Is there any relationship between positive ratings, estimated users and average playtime?

Dataset

The dataset used in this project was Steam Games dataset 2025, downloaded from Kaggle.

It contains around 95,000 games and 47 columns, including information such as:

• game name;


• release date;


• genres;


• price;


• estimated owners;


• average playtime;


• positive and negative reviews.

One important detail is that the original CSV file is not included in the GitHub repository because it is too large. Instead, the repository explains where the file should be placed in order to reproduce the project.

Data cleaning and transformation

Before analyzing the data, I had to transform several columns.

For example, the estimated_owners column does not contain an exact number of users. It contains ranges such as 100000 - 200000. To work with this variable, I used the midpoint of each range as an approximation.

I also converted average_playtime_forever from minutes to hours, separated games with multiple genres, translated the main genre names into Spanish for the final report, and created summary tables for the analysis.

One of the most important decisions was not to use total estimated users by genre as the main metric. A single game can belong to several genres, so summing users by genre can produce inflated numbers. Instead, I focused on average estimated users per game, which gave a more realistic comparison between genres.

Main findings

When analyzing average playtime, the ranking changed. The genres with the highest average playtime were:

1. Simulation


2. Massively Multiplayer


3. Casual


4. Adventure


5. Action

This was one of the most interesting parts of the project. The genres with the most estimated users were not always the same genres with the highest playtime. In other words, popularity and retention are related, but they are not the same thing.

Free games vs paid games

This academic project was developed during the Master in Data Science & AI at Evolve.

https://evolve.es