--- license: apache-2.0 task_categories: - tabular-classification language: - en pretty_name: Google Play Store Apps 2020 - Cleaned size_categories: - 1K **A data-driven EDA of 9,436 apps released in 2020, exploring the patterns behind viral success on the Play Store.** --- ## 🎬 Video Walkthrough

*Can't see the video? [Click here to watch](https://www.loom.com/share/3b2dc51bcf384a339d3d3328766e4705)* --- ## 🔬 Research Question > **"What made an app released in 2020 go viral?"** Defined as reaching **1M+ installs by June 2021** — within 6–18 months of launch. 2020 was chosen deliberately: the COVID-19 pandemic drove unprecedented mobile app adoption, and since the data was collected in June 2021, apps from 2020 had a consistent 6–18 month measurement window. --- ## 📊 Key Visualizations ### 1. The Winner-Takes-All Market 86% of apps never exceed 10K installs. Viral apps (<1%) are a thin but real right tail. ![Installs Distribution](https://huggingface.co/datasets/yonilev/GooglePlayStoreApps/resolve/main/viz_01_installs_distribution.png) --- ### 2. Does Quality = Success? (Surprising Answer: No) Rating and Installs show a weak **negative** linear correlation (r = −0.31). Viral apps attract polarising reviews — millions of users means more critics. ![Rating vs Installs](https://huggingface.co/datasets/yonilev/GooglePlayStoreApps/resolve/main/viz_06_rating_vs_installs.png) --- ### 3. Monetization Strategy Matters Ad-based and Hybrid (ads + IAP) apps reach significantly higher install counts. The price barrier for Premium apps dramatically limits reach. ![Monetization vs Installs](https://huggingface.co/datasets/yonilev/GooglePlayStoreApps/resolve/main/viz_04_monetization_violin.png) --- ### 4. The Gap Between Tiers is Enormous Each tier is roughly **1,000x** the previous — a textbook power-law gap. | Tier | Median Installs | n apps | |------|----------------|--------| | Low | ~100 | 8,135 | | Medium | ~10,000 | 1,227 | | Viral | ~1,000,000 | 74 | ![Success Tiers](https://huggingface.co/datasets/yonilev/GooglePlayStoreApps/resolve/main/viz_08_success_tiers.png) --- ## 🔑 Key Findings 1. **The market is winner-takes-all.** 86% of apps sit in the Low tier. Viral apps represent less than 1% of 2020 launches. 2. **Monetization is the strongest predictor.** Ad-based and Hybrid models (ads + IAP) significantly outperform Pure Free and Premium. 3. **Higher ratings do NOT predict more installs** (r = −0.31). Quality alone is not the driver — distribution and visibility are. 4. **Having a rating at all is a strong proxy for traction.** Rated apps contain virtually all Medium and Viral tier apps. 5. **Fresher apps win.** Regular updates correlate negatively with days_since_update (r = −0.25). 6. **The Rating–Installs relationship is category-specific.** A global r = −0.31 masks very different dynamics per category. --- ## 🗂️ Dataset | Property | Value | |----------|-------| | **Source** | [Kaggle — gauthamp10/google-playstore-apps](https://www.kaggle.com/datasets/gauthamp10/google-playstore-apps) | | **Original size** | 2.31M rows × 24 columns | | **Subset** | Apps released in 2020 only | | **Sample size** | 9,436 rows (Cochran's formula + stratified by Category) | | **Scraped** | June 2021 | ### Features in this dataset | Feature | Type | Description | |---------|------|-------------| | `App Name` | string | Name of the app | | `Category` | categorical | App category (48 unique) | | `Rating` | float | Average user rating (NaN if unrated) | | `Rating Count` | int | Number of user ratings | | `Installs_clean` | int | Parsed install count | | `log_installs` | float | log(1 + Installs) — for modeling | | `Size_MB` | float | App size in MB (winsorized at 65.8MB) | | `Min_Android_ver` | float | Minimum Android version required | | `Released` | datetime | Launch date (all in 2020) | | `Last Updated` | datetime | Last update date | | `Free` | bool | Whether app is free | | `Ad Supported` | bool | Whether app shows ads | | `In App Purchases` | bool | Whether app has IAP | | `Editors Choice` | bool | Whether app is Editor's Choice | | `has_rating` | bool | Whether app has crossed rating threshold | | `app_age_days` | int | Days from release to scrape date | | `days_since_update` | int | Days from last update to scrape date | | `engagement_ratio` | float | Rating Count / Installs | | `log_engagement` | float | log(1 + engagement_ratio) | | `developer_app_count` | int | Total apps by same developer in 2020 | | `monetization_model` | categorical | Pure Free / Ad-based / Freemium / Hybrid / Premium | | `success_tier` | categorical | **Target variable** — Low / Medium / Viral | --- ## 📓 Notebook The full analysis notebook is available here: 👉 [Open The Notebook in Google Colab](https://colab.research.google.com/drive/1YxqN2Urjli1ToxtYO9LtUztXxb5SeEcb?usp=sharing) --- ## ❓ Questions & Answers **Q1: Is the app market democratic — can any app go viral?** No. 86% of apps released in 2020 never exceeded 10K installs. The market is winner-takes-all: less than 1% of apps reached the Viral tier (1M+ installs), and their median install count is roughly 1,000x that of Medium-tier apps. **Q2: Does a higher rating mean more installs?** Surprisingly, no. Rating and Installs show a weak negative linear correlation (r = −0.31). Viral apps attract polarising reviews — millions of users means more critics. Quality alone is not the driver; distribution and visibility are. **Q3: Does monetization strategy matter?** Yes — it's the strongest predictor in the dataset. Ad-based and Hybrid (ads + IAP) apps reach significantly higher install counts than Pure Free or Premium apps. The price barrier of Premium apps dramatically limits reach. **Q4: Does having a rating at all matter?** Yes, dramatically. Apps with a visible rating contain virtually all Medium and Viral tier apps. This reflects a chicken-and-egg dynamic: installs drive ratings, ratings drive visibility, visibility drives more installs. **Q5: Does the Rating–Installs relationship hold across all categories?** No. The global r = −0.31 masks very different dynamics per category. In Tools and Business, there is almost no relationship. In Entertainment and Games, high-install apps cluster at specific rating bands. --- ## 🔧 Key Decisions | Decision | What I did | Why | |----------|-----------|-----| | **Subset to 2020** | Filtered 544,882 apps released in 2020 | Consistent 6–18 month measurement window for all apps | | **Stratified sampling** | Cochran's formula → 9,436 rows, sampled proportionally by Category | Preserve category distribution while reducing computational load | | **Rating = 0 → NaN** | Replaced zero ratings with NaN | Zero means unrated, not bad — imputing would distort analysis | | **Rating not imputed** | Kept 54.9% of Rating as NaN, created `has_rating` instead | Missing rating is a meaningful signal, not random noise | | **Size_MB winsorized** | Capped at 65.8 MB (IQR upper fence) | Extreme sizes are edge cases that distort correlations | | **Installs kept as-is** | No capping on extreme install counts | Viral outliers ARE the story — removing them defeats the purpose | | **log_installs** | Applied log(1 + Installs) transformation | Raw installs follow a power-law — log scale is needed for analysis | | **monetization_model** | Combined Free + Ad Supported + In App Purchases into 5 categories | Three boolean columns carry more meaning as a single business model feature | ``` ## 👤 Author **Yonathan Levy** Econ & Entrepreneurship with Data Science Specialization Reichman Uni Class of 2028