📱 Google Play Store EDA

Exploratory Data Analysis (EDA) on apps listed in the Google Play Store to identify patterns in app ratings, reviews, pricing, installs, and categories.

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📌 Objective

To analyze a dataset of Google Play Store apps and extract meaningful insights that can help make data-driven decisions.

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📂 Dataset

• Source: Google Play Store Apps Dataset on Kaggle
• Size: ~10,000 rows and 13 columns
• Features: App name, Category, Rating, Reviews, Size, Installs, Type (Free/Paid), Price, Content Rating, Genres, Last Updated, Current Version, Android Version.

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❓ Key Questions Explored

• Which app categories are most common?
• What is the distribution of app ratings?
• How do installs vary across categories?
• Are paid apps better rated than free apps?
• Is there a correlation between price and rating?

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🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

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📊 Key Insights

• Most apps belong to the "Family" and "Game" categories.

• Free apps dominate the store (~92%), but paid apps sometimes receive slightly better ratings.

• Rating distribution is slightly left-skewed with most apps rated between 4.0–4.5.

• Number of installs varies heavily across categories; a few apps have over a billion downloads.

• Some apps have suspiciously high reviews or installs, indicating possible outliers or fake data.

• The Family and Game are the two most popular categories as more than 66% of the total apps belong to these categories.

• Facebook App has the maximum no of reviews

• Apps in the Books_and_reference and Business categories have the most no of installs.

• There is a strong positive correlation between the no of reviews and the no of installs

• 📎 Files

• google_Playstore1.ipynb – Jupyter notebook with full EDA workflow.

• README.md – This file.

• gooleplaystore.csv- Dataset

📊 Visualizations

Free vs Paid App Ratings

Correlation Matrix