Quick start
- Open the Dashboard. Use the sidebar to access the prediction form.
- Fill in game features. Enter pre-launch attributes across the 10 sections (Pricing, Release, Genre, …).
- Submit. Click Predict Success to run the ensemble model.
- Review. Inspect the predicted owner tier, confidence, class probabilities, and recommendations.
Feature reference
All 48 pre-launch features, grouped by section:
💰 Pricing
| Field |
Label |
Type |
Range |
price |
Price (USD) |
Number
|
0–200
|
initialprice |
Initial Price (USD) |
Number
|
0–200
|
is_free |
Free to Play? |
Yes / No
|
0 / 1
|
🗓️ Release
| Field |
Label |
Type |
Range |
release_month |
Release Month |
Selection
|
12 options
|
🎮 Genre
| Field |
Label |
Type |
Range |
Action |
Action |
Yes / No
|
0 / 1
|
Adventure |
Adventure |
Yes / No
|
0 / 1
|
RPG |
RPG |
Yes / No
|
0 / 1
|
Strategy |
Strategy |
Yes / No
|
0 / 1
|
Simulation |
Simulation |
Yes / No
|
0 / 1
|
Indie |
Indie |
Yes / No
|
0 / 1
|
Sports |
Sports |
Yes / No
|
0 / 1
|
Racing |
Racing |
Yes / No
|
0 / 1
|
🖥️ Platform
| Field |
Label |
Type |
Range |
platform_windows |
Windows |
Yes / No
|
0 / 1
|
platform_mac |
Mac |
Yes / No
|
0 / 1
|
platform_linux |
Linux |
Yes / No
|
0 / 1
|
🌍 Languages
| Field |
Label |
Type |
Range |
supported_languages_count |
Text Languages Supported |
Number
|
0–50
|
full_audio_languages_count |
Full Audio Languages Supported |
Number
|
0–20
|
🏪 Store Page
| Field |
Label |
Type |
Range |
screenshot_count |
Number of Screenshots |
Number
|
0–20
|
has_trailer |
Has Trailer? |
Yes / No
|
0 / 1
|
trailer_count |
Number of Trailers |
Number
|
0–10
|
about_length |
Description Length (chars) |
Number
|
0–5000
|
has_detailed_desc |
Detailed Description (>500 chars)? |
Yes / No
|
0 / 1
|
has_website |
Has Official Website? |
Yes / No
|
0 / 1
|
has_support_email |
Has Support Email? |
Yes / No
|
0 / 1
|
🏢 Developer / Publisher
| Field |
Label |
Type |
Range |
developer_count |
Number of Developers |
Number
|
1–20
|
publisher_count |
Number of Publishers |
Number
|
0–10
|
has_publisher |
Has Publisher? |
Yes / No
|
0 / 1
|
is_solo_dev |
Solo Developer? |
Yes / No
|
0 / 1
|
required_age |
Required Age (0=none) |
Number
|
0–18
|
is_mature_content |
Mature Content (18+)? |
Yes / No
|
0 / 1
|
🏆 Steam Features
| Field |
Label |
Type |
Range |
has_achievements |
Steam Achievements? |
Yes / No
|
0 / 1
|
achievement_count |
Number of Achievements |
Number
|
0–500
|
has_trading_cards |
Steam Trading Cards? |
Yes / No
|
0 / 1
|
has_workshop |
Steam Workshop? |
Yes / No
|
0 / 1
|
has_cloud_save |
Steam Cloud Save? |
Yes / No
|
0 / 1
|
has_controller_support |
Controller Support? |
Yes / No
|
0 / 1
|
has_vr_support |
VR Support? |
Yes / No
|
0 / 1
|
has_in_app_purchases |
In-App Purchases? |
Yes / No
|
0 / 1
|
has_family_sharing |
Family Sharing? |
Yes / No
|
0 / 1
|
category_count |
Total Steam Categories |
Number
|
0–15
|
🏷️ Tags & Community
| Field |
Label |
Type |
Range |
tag_count |
Number of Tags |
Number
|
0–20
|
has_multiplayer_tag |
Has Multiplayer Tag? |
Yes / No
|
0 / 1
|
top_tag_votes_total |
Top Tag Votes Total |
Number
|
0–5000
|
top_tag_votes_mean |
Top Tag Votes Mean |
Number
|
0–1000
|
is_multiplayer |
Multiplayer Game? |
Yes / No
|
0 / 1
|
📦 Packaging & DLC
| Field |
Label |
Type |
Range |
dlc_count |
DLC Count |
Number
|
0–50
|
package_count |
Package Count |
Number
|
1–10
|
sku_count |
SKU Count |
Number
|
1–20
|
Understanding results
Predicted owner tier
- Class 0 (≤10K) — Common Indie
- Class 1 (35K) — Niche
- Class 2 (75K) — Growing
- Class 3 (150K) — Established
- Class 4 (350K) — Popular
- Class 5 (≥750K) — Breakout Hit
Confidence score
- 80–100%: very confident
- 60–79%: moderately confident
- 40–59%: low confidence (borderline)
- Below 40%: uncertain (mixed/weak features)
Class probabilities
Predicted probability for each of the 6 classes. The class with the highest probability is the final prediction.
Recommendations
- Strengths — features currently working in your favor (high positive impact)
- Improvements — actionable suggestions to lift the predicted tier (driven by SHAP analysis)
Best practices
- Be accurate. The model is trained on real Steam data; inflated numbers mislead predictions.
- Pre-launch only. No reviews or playtime — fill only what you can decide before release.
- Use recommendations. Prioritise high-impact suggestions surfaced by SHAP.
- Test scenarios. Try with/without publisher, varying pricing, etc., to see what moves the needle.
- Don't over-optimise. Correlations ≠ causation. Game quality is the ultimate factor.
FAQ
What if I don't have a publisher?
Set has_publisher to 0 and publisher_count to 0. Publisher backing helps but isn't required.
Can I predict for unreleased games?
Yes — the model uses only pre-launch features (pricing, store page, platform support, etc.).
What if some features are unknown?
Use the defaults shown in the form. Leave toggles unchecked (0) for features you don't plan to ship.
Why is my confidence low?
Your features fall in regions where multiple classes overlap. Adjust key drivers (store page quality, pricing, platform reach) and try again.
How accurate is the model?
The ensemble achieves 70.85% accuracy and 0.6211 weighted F1 on the test set. See
Model Metrics for details.
Does it work for non-Steam platforms?
No. Trained exclusively on Steam data; predictions for Epic, GOG, or consoles aren't reliable.