DataAanalysis

Apple App Store Apps EDA

Welcome to the Apple App Store Apps EDA repository! This project focuses on Exploratory Data Analysis (EDA) of the Apple App Store dataset, where we dive into various aspects of mobile applications available on the Apple App Store.

Overview

The Apple App Store is a thriving marketplace for mobile applications, and this EDA aims to provide valuable insights into this ecosystem. Our analysis touches upon key aspects of these apps, including categories, user ratings, pricing strategies, content ratings, and more.

Dataset

Dataset Columns

App_Id: Unique identifier for each app.
App_Name: Name of the mobile application.
AppStore_Url: URL of the app on the Apple App Store.
Primary_Genre: The primary genre of the app.
Content_Rating: Content rating assigned to the app.
Size_Bytes: Size of the app in bytes.
Required_IOS_Version: The minimum iOS version required to run the app.
Released: Date of app release.
Updated: Date of the last app update.
Version: App version.
Price: App price.
Currency: Currency of the app price.
Free: Indicates whether the app is free or paid (binary).
DeveloperId: Unique identifier for the developer.
Developer: Name of the developer.
Developer_Url: URL of the developer on the Apple App Store.
Developer_Website: Developer's website.
Average_User_Rating: Average user rating for the app.
Reviews: Number of user reviews.
Current_Version_Score: Score for the current app version.
Current_Version_Reviews: Number of user reviews for the current app version.

Key Insights

Here are some of the key insights from our analysis:

Top Categories: Discover the most popular app categories.
User Ratings: Understand user ratings and their impact.
Pricing Trends: Analyze pricing strategies and their effectiveness.
Content Ratings: Explore content ratings and their influence.
Release Trends: Identify trends in app releases over time.
Size vs. Price: Investigate the relationship between app size and price.

Files in This Repository

eda.ipynb: Jupyter Notebook containing the complete code and analysis.

How to Use This Repository

Clone the repository to your local machine using git clone.
Open the Jupyter Notebook eda.ipynb to explore the code and findings.
Feel free to use the insights and code for your own projects or research.

Connect with Us

If you have any questions, want to collaborate, or discuss the analysis further, please feel free to connect with us.

Acknowledgments

We would like to express our gratitude to the data science community and the contributors of the dataset used in this analysis. Your work has been instrumental in generating these insights.

FaizanOmer/DataAanalysis