Datasets:

yonilev
/

GooglePlayStoreApps

	---
	license: apache-2.0
	task_categories:
	- tabular-classification
	language:
	- en
	pretty_name: Google Play Store Apps 2020 - Cleaned
	size_categories:
	- 1K<n<10K
	---

	# 📱 Google Play Store 2020 — What Made an App Go Viral?

	> A data-driven EDA of 9,436 apps released in 2020, exploring the patterns behind viral success on the Play Store.

	---

	## 🎬 Video Walkthrough

	<!-- Replace the src with your actual video link after uploading -->
	<video src="https://www.loom.com/share/3b2dc51bcf384a339d3d3328766e4705" controls="controls" style="max-width: 720px;"></video>

	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

	---
	license: apache-2.0
	task_categories:
	- tabular-classification
	language:
	- en
	pretty_name: Google Play Store Apps 2020 - Cleaned
	size_categories:
	- 1K<n<10K
	---

	# 📱 Google Play Store 2020 — What Made an App Go Viral?

	> A data-driven EDA of 9,436 apps released in 2020, exploring the patterns behind viral success on the Play Store.

	---

	## 🎬 Video Walkthrough

	<!-- Replace the src with your actual video link after uploading -->
	<video src="https://www.loom.com/share/3b2dc51bcf384a339d3d3328766e4705" controls="controls" style="max-width: 720px;"></video>

	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