itaimorag/Video-Games-Sales-EDA

🎮 Video Game Sales — Exploratory Data Analysis (EDA)

Executive Summary

The video game industry is a multi‑billion dollar market characterized by extreme unpredictability—a single "mega‑hit" can generate more revenue than thousands of average games combined. This project analyzes historical video game sales (1980–2016) to uncover the main patterns behind commercial success.

Key findings: Action and Sports genres have the highest historical sales volumes, but the market is highly volatile, era‑dependent, and dominated by a small number of outlier hits. Higher critic scores raise the ceiling of potential sales and are more predictive than user scores, but they do not guarantee commercial success, and regional tastes differ substantially.

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Navigation

• Executive Summary
• 1. Dataset Description
• 2. Intended Uses & Audience
• 3. Dataset Structure
• 4. Data Integrity & Cleaning
• 5. Exploratory Data Analysis Highlights
  • 5.1 Market Size & Global Trends
  • 5.2 Demographics & Audience
  • 5.3 Quality vs Commercial Success
• 6. Machine Learning Readiness
• 7. Strategic Takeaways
• 8. Limitations
• 9. Author

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1. Dataset Description

This dataset contains historical sales data and review scores for video games released between 1980 and 2016.
Each row represents a single game release.

1.1 Source

The data is based on public video game sales and review aggregators, cleaned and repackaged here for EDA and ML tasks.

1.2 Features

• Identity
  • Name, Platform, Year_of_Release, Publisher
• Categorization
  • Genre, Rating (ESRB)
• Financials (in millions of units)
  • NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales
• Reception
  • Critic_Score, Critic_Count, User_Score, User_Count
• Additional
  • Developer, Rating

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2. Intended Uses & Audience

• Publishers & investors – estimate market size and historical performance by genre, platform, and publisher.
• Indie developers & analysts – understand genre saturation, realistic sales expectations, and promising niches.
• Data scientists & ML practitioners – build models for:
  • sales prediction,
  • hit vs non‑hit classification,
  • portfolio and trend analysis. The full EDA notebook is provided as Assignment_1_EDA_&_Dataset_new.ipynb.

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3. Dataset Structure

• Rows: ~16.7k games
• Columns: 16 tabular features
• Split: single train split (users create their own train/val/test).

3.1 Sample Rows

After cleaning

4. Data Integrity & Cleaning

Raw game sales data is noisy and inconsistent. These steps were taken to make it analysis‑ready.

4.1 Initial State

• Missing identifiers and partial metadata for older titles
• A few implausible Year_of_Release values
• User_Score as strings with "tbd", forcing an object dtype
• Strong numerical outliers in Global_Sales (e.g., Wii Sports) Pre‑cleaning snapshot:

4.2 Cleaning Decisions

• Removed games with Year_of_Release after 2016 to avoid partially observed recent years.
• Converted "tbd" in User_Score to NaN and cast back to float.
• Left missing Critic_Score / User_Score as NaN to avoid fabricating scores for eras without aggregators.
• Retained extreme best‑sellers; handled their influence via log scaling in plots rather than dropping them. Post‑cleaning snapshot:

4.3 Summary Statistics After Cleaning

5. Exploratory Data Analysis Highlights

5.1 Market Size & Global Trends

A. Total Global Sales by Genre

Question: What are the most profitable genres of all time (by total units sold)? - Insight: Action and Sports dominate absolute sales volumes, with Shooters also crossing the billion‑unit mark.

B. Genre Popularity Over Time

Static trend of top genres - Insight: Platformers and Puzzle titles were strong early; Action and Shooters rise sharply in the 2000s. Genre viability is era‑dependent.

C. The Titans of the Industry

Question: Which publishers dominate lifetime global sales? - Insight: A small number of publishers (Nintendo, EA, Activision, etc.) control a large share of total units sold.

5.2 Demographics & Audience

A. Regional Taste Differences

Question: Do North America, Europe, and Japan prefer different genres? - Insight: NA and EU lean toward Action/Sports/Shooters; Japan strongly prefers Role‑Playing games and contributes little to Shooters.

B. ESRB Age Ratings and Sales

Question: Does restricting a game to a mature audience limit its sales potential? - Insight: Medians are similar across E, T, M, E10+, but E and M have the highest outliers. Both family‑friendly and mature games can reach very high sales.

C. Sales Distribution by Genre (Log Scale)

Question: What does typical performance look like within each genre once we control for outliers?** - Insight: The median game in almost any genre sells well under one million units; huge totals are driven by a few extreme hits.

5.3 Quality vs Commercial Success

A. Feature Correlations

Question: How do review scores relate to sales numerically?

• Insight:
  • Regional sales correlate strongly with Global_Sales (by construction).
  • Critic_Score has a moderate positive correlation (~0.24–0.25) with Global_Sales.
  • User_Score shows a weaker correlation with Global_Sales.
  • Critic_Score and User_Score correlate moderately with each other.
• Interpretation: Professional reviews are a better linear predictor of sales than user scores, but still far from deterministic.

B. Distribution of Professional Critic Scores

Question: How are critic scores distributed overall?

• Insight: Scores are slightly left‑skewed and heavily clustered between 65 and 85. In practice, ~70+ behaves like the “average” functioning game.

C. Critic Scores vs Global Sales

Question: Do higher critic scores actually translate into more sales? - Insight: Very high‑selling games almost all have critic scores above ~80, but many highly rated games still sell modestly. A high score raises the ceiling more than it guarantees a result.

D. Critic vs User Alignment

Question: Do critics and players agree on quality? - Insight: Points follow an overall upward trend but with wide dispersion above and below the 45° line, indicating both agreement and strong disagreements (e.g., cult classics or controversial titles).

6. Machine Learning Readiness

• Scaling / normalization
  • Global_Sales (millions), Critic_Score (0–100), and User_Score (0–10) live on different scales.
  • Use StandardScaler or MinMaxScaler for distance‑based or gradient‑based models.
• Targets
  • Regression: Global_Sales or log(Global_Sales + ε) to reduce skew.
  • Classification: “hit” vs “non‑hit” (e.g., >1M units), or multi‑tier sales brackets.
• Useful features
  • Categorical: Genre, Platform, Publisher, Rating, binned Year_of_Release
  • Numerical: Critic_Score, User_Score, Critic_Count, User_Count
  • Engineered: critic–user gap, regional shares, log‑sales, time‑decayed features.

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7. Strategic Takeaways

1. Genre shapes opportunity size.
   Action, Sports, and Shooters reach the largest audiences but are also the most saturated genres.
2. Most games are modest sellers.
   The market is strongly hit‑driven: medians are low, and a handful of blockbusters carry totals.
3. Reviews help, but aren’t everything.
   Higher critic scores are associated with higher potential sales, but IP strength, marketing, and platform reach remain crucial.
4. Market power is concentrated.
   Major publishers start with a much higher expected baseline than small studios.
5. Audience targeting matters.
   Both E and M‑rated games can perform extremely well if genre, platform, and marketing match the intended demographic and region.

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8. Limitations

• Dataset coverage effectively ends in 2016 and does not fully capture the shift to digital‑only, live‑service, or mobile ecosystems.
• Many older titles lack Critic_Score / User_Score due to the absence of historical aggregators.
• All relationships are correlational, not causal.

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9. Author

Itay Morag