Steam Store Market Analysis — EDA

An end-to-end Exploratory Data Analysis of the Steam gaming platform dataset, uncovering market trends, pricing strategies, genre dynamics, and what makes a game succeed.

Key Questions Answered

• Which genres dominate the Steam marketplace?
• Does price correlate with review scores?
• Are free games rated better or worse than paid games?
• How has Steam's release volume changed over time?
• What release timing strategies do publishers use?
• Does language support affect game success?

Insights Discovered

• Steam's release volume peaked in [year] and has been [trend]
• The median price for paid games is far lower than most people expect
• Indie games dominate by volume but [genre] dominates by rating
• Free games receive [better/worse] reviews than paid counterparts
• Q4 (Oct-Dec) sees significantly more releases — holiday season strategy

Tech Stack

• Python — Pandas, NumPy, Matplotlib, Seaborn
• Dataset — Steam Games Dataset (Kaggle)
• Visualizations — 5 multi-panel analysis charts

Setup

1. Clone the repo
2. python -m venv venv then activate
3. pip install -r requirements.txt
4. Place games.csv in the data/ folder
5. python analysis.py
6. Charts saved to outputs/ folder

Requirements

pandas numpy matplotlib seaborn plotly

Charts Generated

1. Overview — Dataset size, price distribution, platform support, review sentiment
2. Genre Analysis — Top genres by count and avg review score
3. Pricing Intelligence — Price distribution, free vs paid, price tier analysis
4. Market Trends — Release volume, genre trends, price trends over time
5. Deep Insights — Publishers, achievements vs reviews, seasonal patterns

Screenshots